DEV Community: Emmanuel Mumba

Best Tools for Managing Parallel AI Coding Agents in 2026

Emmanuel Mumba — Fri, 16 Oct 2026 12:12:29 +0000

I’ve been looking at how AI coding agents are changing the way developers work, and one thing is becoming clear: the real opportunity isn’t just getting one agent to write code faster. It’s being able to run several agents at once without losing control of the development process.

Imagine having one agent work on a new feature, another fix a bug, and a third write tests, all at the same time. That sounds great until they start making conflicting changes, duplicating work, or leaving you to figure out what each one has actually completed.

This is where the right tools make a difference. Managing parallel AI coding agents requires more than launching multiple sessions. You need a way to organize tasks, keep work isolated, monitor progress, and review the results before anything gets merged into your codebase.

I wanted to look at the tools that make this workflow easier, from platforms built specifically for coordinating AI agents to coding environments that support background execution and parallel sessions. Here are the best tools for managing parallel AI coding agents in 2026, along with what each one offers and where it fits best.

What to Look for in a Parallel AI Coding Agent Tool

Before choosing a tool, I think it’s worth looking at how it handles the practical challenges of running multiple agents.

Task coordination: Can you assign separate tasks to different agents and track their progress?
Workspace isolation: Can agents work independently without accidentally overwriting one another’s changes?
Agent compatibility: Does the tool work with the coding agents and models you already use?
Review and integration: Can you inspect changes, compare diffs, run tests, and decide what gets merged?
Visibility: Can you quickly identify which agents are running, blocked, or finished?
Scalability: Can you increase the number of concurrent agents without overwhelming your machine or development workflow?

Not every tool handles all these responsibilities. Some focus on running coding agents, while others help coordinate the broader development process.

1. Sharkly: Best for Coordinating AI Agents Across Development Tasks

When I think about managing parallel AI coding agents, I think beyond simply opening several coding sessions. I want a place where tasks are organized, agents can work independently, and I can see what is happening without constantly switching between terminals.

That is the problem Sharkly is designed to address.

Sharkly brings product and engineering work into a shared workspace. Teams can organize backlogs, manage tasks, assign work to AI agents, and track progress as the agents execute their assignments. Instead of treating each agent session as a separate activity, the workflow connects agent execution to the tasks the team is trying to complete.

One useful part of this approach is workspace isolation. When tasks run in separate working directories or worktrees, agents can work on different assignments without automatically modifying the same working files. That makes it easier to run work in parallel while keeping the results organized for review.

Sharkly also supports multiple coding agents, including Claude Code, Codex, Gemini CLI, and other supported agent environments. Teams can use their existing coding-agent setups rather than being forced into a single model or provider.

Another practical consideration is that Sharkly supports Jira imports and two-way synchronization during migration, which can help teams that already manage their work in Jira.

What I like about the approach: It treats parallel agent execution as part of the development workflow, rather than simply a way to open more coding sessions.

Best for: Engineering teams that want task management, agent execution, and progress visibility in one place.

Considerations: The value depends on how much coordination your workflow needs. Developers who only want to run a couple of isolated agents locally may prefer a lighter setup.

Website: Sharkly.ai

2. OpenAI Codex: Best for Running Multiple Coding Tasks in Parallel

OpenAI Codex is another option worth considering if you want AI agents to handle several development tasks concurrently.

The Codex app supports working with multiple agents and threads, allowing developers to delegate different coding tasks instead of waiting for one task to finish before starting another. Its support for worktrees can also help keep changes isolated when agents are working on different parts of a codebase.

For example, I could assign one task to investigate a failing test, another to implement a small feature, and a third to review an existing implementation. I would then inspect each result and decide which changes are ready to integrate.

This workflow is particularly useful when tasks are reasonably independent. If two agents need to rewrite the same core component, parallel execution can create more review work than it saves.

Codex is appealing because it brings agent execution and code review into a developer-focused workflow. However, teams that need a shared task-management layer across many projects may still want a separate coordination platform.

Best for: Developers who want to delegate multiple coding tasks and review their results within the Codex ecosystem.

Considerations: Parallel execution does not remove the need to define task boundaries, run tests, and review changes before merging.

Website: OpenAI Codex

3. Cursor Background Agents: Best for Asynchronous Coding Tasks

Cursor is well known as an AI-powered code editor, but its background-agent capabilities also make it relevant for parallel development.

Background agents can work asynchronously in remote environments, allowing developers to delegate tasks without keeping every operation in the foreground. Depending on the workflow, agents can work on code, make changes on branches, and return results for review.

I find this approach particularly useful for tasks that take time but do not require constant interaction. For example, I could ask an agent to investigate a bug or implement a self-contained improvement while continuing to work on another feature locally.

The remote execution model also means that background work does not have to consume all the resources on your own development machine. That can be helpful when running several tasks, although concurrency, usage limits, and pricing depend on the applicable plan and product configuration.

Cursor is a strong option for developers who already use its editor and want to extend their workflow with asynchronous agents.

Best for: Developers who want background coding tasks integrated into their existing Cursor workflow.

Considerations: Remote execution and branch-based changes are helpful, but developers still need to coordinate overlapping tasks and review the output.

Website: Cursor

4. Conductor: Best for Managing Multiple Coding Agents in Separate Workspaces

Conductor focuses on running multiple coding agents concurrently while keeping their work organized in separate environments.

Rather than making every agent operate in the same working directory, Conductor gives parallel tasks their own workspaces. This helps developers run multiple sessions, inspect their changes independently, and manage the results without constantly switching between manually configured environments.

It supports several coding-agent workflows, including Claude Code, Codex, Cursor, and OpenCode. This makes it worth considering if you prefer to choose an agent based on the task rather than standardizing on one tool for everything.

For instance, I could use one agent to implement a feature and another to investigate a separate issue. Each can work in its own environment, reducing the chance that one task will interfere directly with another's uncommitted changes.

The main advantage is organization. When several agents are active, having a clear view of their workspaces and changes can make the development process easier to manage.

Best for: Developers who want a dedicated environment for running multiple coding agents side by side.

Considerations: Separate workspaces help prevent direct file collisions, but changes can still conflict when they are eventually combined.

Website: Conductor

5. Claude Code: Best for Developers Who Want Flexible Agent Workflows

Claude Code offers several ways to work with multiple agents, making it useful for developers who want more control over how coding tasks are delegated.

Its subagent capabilities allow a primary agent to delegate certain tasks to specialized agents. For example, one agent might investigate a problem while another explores the relevant code or performs a separate analysis.

Developers can also use multiple Claude Code sessions and Git worktrees to keep independent coding tasks separated. This is useful when you want to work on different features or investigations without making every agent share the same files.

The important distinction is that running multiple Claude Code sessions is not the same as having a complete team-wide task coordination system. Developers still need to decide how tasks are divided, how branches are managed, and how completed changes are reviewed.

For people who enjoy customizing their development environment, that flexibility can be an advantage.

Best for: Developers who want agent-assisted coding, delegated sub-tasks, and flexible terminal-based workflows.

Considerations: More control can mean more setup and coordination, particularly when managing many independent tasks.

Website: Claude Code

6. Git Worktrees: Best for Lightweight, DIY Parallel Development

Git worktrees are not an AI coding agent manager, but I would still include them in this discussion because they solve one of the most important problems with parallel coding: workspace isolation.

A worktree allows you to check out another branch of the same Git repository into a separate directory. Instead of repeatedly switching branches in one working directory, you can have multiple working directories available at the same time.

That makes it possible to run separate coding agents against separate worktrees. One agent might implement a feature while another fixes a bug in a different worktree.

The benefit is that the agents do not automatically edit the same working files. You can inspect each branch, run tests, and integrate the changes when they are ready.

However, worktrees do not provide task assignment, agent monitoring, or automatic conflict resolution by themselves. You still need to manage the sessions, track progress, and decide when to merge changes.

Best for: Developers who want a lightweight way to run parallel agents using their existing Git workflow.

Considerations: Worktrees require some manual coordination, and separate branches can still produce merge conflicts.

Documentation: Git Worktree

7. OpenAI Agents SDK: Best for Building Custom Multi-Agent Systems

The OpenAI Agents SDK takes a different approach from the developer tools above. Rather than providing a ready-made interface for coordinating coding sessions, it gives developers building blocks for creating agent-powered applications and workflows.

You can define agents with different instructions and tools, then configure how work is handed off or delegated. In suitable workflows, a coordinating agent can assign independent tasks to other agents and combine their results.

For example, a custom development assistant could ask one agent to analyze a bug report, another to identify relevant code, and a third to propose tests. A coordinating workflow could then collect the findings and present them for review.

This approach offers considerable flexibility, particularly if you want to build your own internal developer platform or automate a specialized engineering process.

However, it also means that you are responsible for implementing the surrounding workflow. Workspace isolation, task tracking, Git operations, access controls, and code review may require additional components.

Best for: Developers and engineering teams building custom multi-agent applications or orchestration systems.

Considerations: It is a framework for building agent workflows, not an out-of-the-box project-management interface for parallel coding tasks.

Documentation: OpenAI Agents SDK

Quick Comparison

These tools are not direct substitutes for one another. Some focus on executing coding tasks, some provide a way to isolate work, and others help coordinate the wider development process.

How I Would Choose the Right Tool

After comparing these options, I would start with the main problem I am trying to solve rather than the number of agents a tool claims to support.

If I want task management and agent execution together, I would look at Sharkly. It connects agent assignments to a broader engineering workflow, which is useful when several people and agents are contributing to the same projects.

If I mainly want to delegate coding tasks, I would consider Codex or Cursor Background Agents, depending on the environment I already use.

If workspace separation is my biggest concern, Conductor or Git worktrees would be worth exploring. Both approaches help keep concurrent work organized, although the amount of manual setup differs.

If I want more control over how agents collaborate, Claude Code or the OpenAI Agents SDK may be a better fit. The former supports flexible coding-agent workflows, while the latter is useful when building custom orchestration.

I would also consider the cost of running agents concurrently. More agents can mean more compute usage, model consumption, and review overhead. Starting with two or three independent tasks is often more useful than immediately trying to run ten agents at once.

Best Practices for Managing Parallel AI Coding Agents

Choosing a tool is only part of the equation. How you structure the work matters just as much.

1. Give Each Agent a Clear, Independent Task

Avoid assigning several agents vague instructions such as "improve the application." Instead, define the expected outcome, relevant files or components, constraints, and acceptance criteria.

For example, an agent can update input validation while another adds tests for an unrelated service. This makes it easier to identify overlapping work before execution begins.

2. Isolate Workspaces Where Possible

When agents share the same working directory, their changes can interfere with each other. Separate worktrees or isolated execution environments help reduce this risk.

Isolation does not eliminate every problem, though. Two agents working on separate branches can still make incompatible changes to the same API or shared component.

3. Track Progress and Blockers

When multiple agents are running, it is easy to lose track of which tasks are complete and which are waiting for input.

Use a task board, agent dashboard, or another tracking system to record assignments, status, blockers, and results. This becomes increasingly important as the number of concurrent tasks grows.

4. Limit Concurrency to What You Can Review

Running more agents does not automatically make development faster. If ten agents finish at the same time and you cannot review their work, you may simply move the bottleneck from coding to quality assurance.

Start small, measure how much review each task requires, and increase concurrency only when the workflow can support it.

5. Review and Test Every Change

Treat agent output as a proposed change, not a guarantee of correctness. Inspect the diff, run relevant tests, and check for regressions before merging.

For important changes, consider security, performance, dependency updates, and compatibility with the rest of the application.

6. Keep Dependencies Between Tasks Explicit

Some tasks cannot begin until others are finished. For example, a frontend agent may depend on an API contract that another agent is still implementing.

Identify these dependencies in advance. Run independent work in parallel and sequence tasks that depend on one another.

Frequently Asked Questions

What does it mean to run AI coding agents in parallel?

It means running multiple AI agents on separate coding tasks at the same time rather than completing every task sequentially. Depending on the tools, agents may run in separate sessions, worktrees, remote environments, or coordinated workflows.

Can multiple AI coding agents work on the same repository?

Yes. Multiple agents can work on the same repository, but they should generally use separate branches, worktrees, or isolated environments when changing code concurrently. Their changes still need to be integrated and tested.

Is a parallel AI coding agent manager necessary?

Not always. If you run only two agents occasionally, terminal sessions and Git worktrees may be sufficient. A dedicated coordination platform becomes more valuable when you need to assign tasks, monitor progress, manage several workspaces, and review results consistently.

Which tool is best for managing parallel AI coding agents?

There is no single best option for every developer. Sharkly is worth considering for task coordination and agent execution, Codex and Cursor for their respective coding workflows, Conductor for managing separate agent workspaces, and Git worktrees for a lightweight manual approach.

Does running more agents make coding faster?

Not necessarily. Parallel execution can reduce waiting time when tasks are independent, but it can also increase model usage, compute costs, review effort, and integration conflicts. The best results usually come from dividing work carefully and keeping tasks independent.

Final Thoughts

I think parallel AI coding agents are most useful when they help developers move several independent tasks forward without creating extra coordination work.

The tools in this list approach the problem from different angles. Some focus on coding, others on workspace isolation, and others on coordinating agents across a broader development workflow.

For a lightweight setup, Git worktrees can be enough. If you already work in Cursor, its background agents are a natural option to explore. Codex and Claude Code offer their own approaches to delegating and running coding tasks, while the OpenAI Agents SDK is useful for building custom multi-agent workflows.

For teams that want to connect agent execution with task management and progress tracking, I would also take a look at Sharkly.

Ultimately, the goal is not to run as many agents as possible. It is to get more useful development work done while keeping the codebase organized, the results reviewable, and the final decisions in the hands of the people responsible for the software.

10 Best Jira Integrations for Engineering Teams in 2026

Emmanuel Mumba — Wed, 14 Oct 2026 10:41:44 +0000

Jira is one of the most widely used tools for managing software development projects. Engineering teams use it to organize backlogs, plan sprints, track bugs, and coordinate releases.

But managing tickets is only one part of building software.

Developers also work with code repositories, CI/CD pipelines, communication platforms, monitoring tools, and increasingly, AI coding agents. When these tools operate independently, teams can waste time switching between applications, searching for updates, and manually connecting development work to its results.

That's where Jira integrations become useful.

The right integrations can connect issues to code changes, surface build failures, improve team communication, and even help teams experiment with AI agents that execute coding tasks.

In this guide, we'll explore 10 of the best Jira integrations for engineering teams in 2026, what each tool offers, and when it makes sense to use it.

Quick Comparison

Integration capabilities, availability, and pricing depend on your Jira edition and the configuration of each connected product.

1. GitHub — Best for Source Control and Code Reviews

Best for: Engineering teams that use GitHub to host repositories and review code.

GitHub is one of the most useful integrations for teams that use Jira to plan work and GitHub to develop software.

Without an integration, a Jira ticket might say that a bug has been fixed, but developers may still need to open GitHub to locate the relevant branch, commits, and pull request.

Connecting GitHub to Jira brings supported development activity into the context of the Jira work item.

Depending on the configuration, teams can connect:

Branches associated with Jira issues
Commits referencing issue keys
Pull requests and review activity
Related build and deployment information

For example, imagine a ticket called PAY-142: Fix payment validation. A developer creates a branch, implements the fix, and opens a pull request referencing PAY-142. The team can then inspect the linked development activity from the corresponding Jira issue.

This improves traceability between requirements and code changes.

Why choose it? GitHub is an excellent starting point if your team wants to reduce manual status updates and make development progress more visible.

Explore: GitHub and Jira integration.

2. GitLab — Best for DevOps Workflows

Best for: Teams using GitLab for source control, merge requests, and CI/CD.

GitLab combines repository management with development automation, making it useful for teams that want to connect Jira planning with their delivery workflow.

Its Jira integration can connect work items to development activity, including commits and merge requests. Teams can also configure supported workflows to coordinate issue tracking with development progress.

For example, a developer working on a new feature can reference a Jira issue in a commit or merge request. This helps the team trace the implementation back to the original requirement.

GitLab is particularly useful when engineering teams already rely on its repositories and CI/CD pipelines.

Why choose it? Choose GitLab if your team wants to retain its existing DevOps environment while making development activity more visible in Jira.

Explore: GitLab's Jira integration documentation.

3. Sharkly — Best for Connecting Jira Work to AI Coding Agents

Best for: Engineering teams that want AI agents to execute coding tasks while keeping their existing Jira workflow connected.

Project management tools help teams decide what needs to be done. Developers then take those tasks into their coding environments, implement changes, run tests, and submit the results for review.

AI coding agents are changing this workflow. They can now assist with bug fixes, test generation, code changes, and other development tasks. The challenge is coordinating that work without losing visibility into the original requirements.

Sharkly approaches this problem by combining project management with AI-agent execution.

Teams can import Jira issues into Sharkly and assign suitable tasks to Agents that execute work through connected coding tools, including Claude Code, Codex, and Gemini CLI.

Instead of treating an AI agent as a separate chat session, Sharkly connects its work to a task. Agents can run in isolated worktrees, report progress and blockers, and return results for human review.

How does it work?

Imagine your Jira backlog contains several maintenance tasks:

Fix a validation bug.
Add unit tests to an existing service.
Update API documentation.

A team could import the relevant Jira work into Sharkly, assign a suitable coding task to an Agent, and review the resulting changes before merging them.

Sharkly also supports two-way synchronization with Jira during a transition, allowing teams to evaluate its workflow without immediately moving every project away from their existing tracker.

This makes it possible to experiment with AI-agent execution while preserving connections to existing project work.

Why choose it?

Sharkly is worth exploring if your team wants to go beyond tracking development tasks and start assigning coding work to AI agents within a connected workflow.

One thing to consider: Sharkly's Jira import and synchronization capabilities are different from a conventional Jira Marketplace app. Teams should evaluate the setup, connected coding tools, security requirements, and human-review process before adopting it.

Explore: Sharkly's Jira comparison.

4. Bitbucket — Best for Teams Already Using Atlassian Tools

Best for: Teams using Jira and Bitbucket together.

Bitbucket is Atlassian's Git-based code hosting platform. Its connection with Jira makes it a natural option for engineering teams that want their planning and source-control tools to work together.

Developers can connect branches, commits, and pull requests to Jira work items. Teams can also use the broader Atlassian development ecosystem to link supported build and deployment activity.

For example, a Jira issue defines a task, a developer creates a Bitbucket branch, and a pull request is opened for review. The associated development activity can help the team understand how the work is progressing.

Why choose it? Bitbucket is a practical option for teams that already use Atlassian products and want a connected development workflow.

Explore: Atlassian software development integrations.

5. Slack — Best for Team Communication

Best for: Teams that coordinate work through Slack channels and messages.

Important engineering decisions often happen outside Jira. A developer might report a bug in a channel, a product manager might clarify a requirement in a thread, and an engineer might announce that a deployment is complete.

Without a connection to Jira, these discussions can become difficult to track.

The Jira Cloud integration for Slack helps connect conversations with work items. Depending on the configuration, users can receive notifications, create issues from conversations, preview Jira issues, and perform supported actions without constantly switching applications.

For example, a team member can turn a bug report shared in Slack into a Jira issue, preserving a link to the original discussion.

Why choose it? Slack is useful for distributed engineering teams that need to turn conversations into actionable work.

Explore: Jira integration guides.

6. Microsoft Teams — Best for Enterprise Collaboration

Best for: Organizations that use Microsoft Teams for daily communication.

Microsoft Teams provides similar benefits to Slack integration, particularly for organizations that already rely on Microsoft's collaboration ecosystem.

With the Jira Cloud app for Teams, users can interact with Jira work items in supported chats and channels, receive notifications, and access issue information.

Imagine an engineering team discussing a delayed release in Teams. Members can look up the relevant Jira issue and review its status without leaving the conversation.

Why choose it? Microsoft Teams is a strong option when it is already the organization's primary collaboration platform.

Explore: Jira Cloud and Microsoft Teams.

7. Confluence — Best for Technical Documentation

Best for: Teams that need to connect engineering work with documentation.

A Jira ticket rarely contains every detail needed to implement a feature. Developers may also need architecture diagrams, API specifications, technical decisions, and acceptance criteria.

Confluence provides a documentation and knowledge-sharing platform that works closely with Jira.

Teams can link Jira issues to Confluence pages, embed work items in documentation, and connect project plans to implementation tasks.

For example, an engineering team might maintain an API migration specification in Confluence and track the implementation, testing, and deployment work through linked Jira issues.

Why choose it? Confluence is useful for teams that need a shared source of technical knowledge rather than relying on scattered messages and individual documents.

Explore: Atlassian integrations.

8. Jenkins — Best for CI/CD Visibility

Best for: Teams that automate builds and deployments with Jenkins.

Continuous integration and continuous delivery pipelines generate information developers need when deciding whether work is ready to move forward.

A Jira issue might be marked as complete while the associated build is still failing. Connecting Jenkins with Jira can help surface supported build and deployment information in the context of the work item.

This makes it easier to investigate failures and understand the status of development work.

Why choose it? Jenkins is a good choice for teams with established CI/CD pipelines that want better visibility between project tracking and software delivery.

Explore: Jenkins and Jira integration.

9. Sentry — Best for Error Monitoring

Best for: Teams that need to connect production errors with engineering work.

Sentry helps developers identify and investigate application errors and performance problems.

Connecting Sentry with Jira can help teams turn important findings into trackable engineering tasks. Depending on the configuration, developers can create or link Jira issues from Sentry events and access relevant context across both systems.

For example, if a release introduces an exception affecting users, Sentry can help identify the error while Jira tracks its investigation and resolution.

Why choose it? Sentry is useful for teams that want production issues to flow into their development backlog instead of remaining isolated in monitoring dashboards.

Explore: Sentry documentation.

10. Xray — Best for Test Management

Best for: QA teams and engineering organizations with structured testing requirements.

Jira issues describe what needs to be built or fixed, but they do not necessarily provide a complete system for managing test cases, execution, and coverage.

Xray adds dedicated test management capabilities to Jira. Teams can organize tests, associate them with requirements, record execution results, and track validation.

For example, a team developing a payment service might create test cases for successful refunds, invalid requests, and failure scenarios. Xray can help connect these tests to the relevant requirements and track their execution.

Why choose it? Xray is a strong option for teams that need structured testing and traceability integrated with Jira.

Explore: Xray for Jira.

How to Choose the Right Jira Integrations

You don't need to install all ten integrations to improve your engineering workflow.

Start by identifying where your team loses the most time or visibility.

Code and reviews: Start with GitHub, GitLab, or Bitbucket.
AI coding execution: Explore Sharkly if you want to connect Jira work to AI-agent execution.
Team communication: Choose Slack or Microsoft Teams based on your existing environment.
Technical knowledge: Consider Confluence.
Build and deployment visibility: Evaluate Jenkins.
Production errors: Consider Sentry.
Test traceability: Explore Xray.

Before installing any integration, check the permissions it requires, the data it accesses, its compatibility with your Jira deployment, and any additional subscription costs.

Also, avoid configuring unnecessary notifications. Integrations should reduce context switching, not create more distractions.

Final Thoughts

Jira becomes more useful when it connects the work teams plan with the tools they use to build, test, deploy, and maintain software.

GitHub and GitLab can improve traceability between issues and code. Slack and Microsoft Teams connect discussions with actionable work. Jenkins, Sentry, and Xray support delivery, monitoring, and testing workflows.

Sharkly addresses another emerging need: connecting project management with AI-agent execution. For teams exploring AI-assisted development, its Jira import and synchronization capabilities offer a way to evaluate an Agent-based workflow without immediately abandoning existing project tracking.

The best integration stack isn't necessarily the largest. It is the one that removes real bottlenecks while preserving the team's security, quality, and review processes.

Start with the tools that solve your biggest problems, measure their impact, and expand from there.

Frequently Asked Questions

What are the most useful Jira integrations for engineering teams?

GitHub, GitLab, and Bitbucket are useful for source control. Slack and Microsoft Teams help connect discussions with work items, while Jenkins, Sentry, and Xray support delivery, monitoring, and testing workflows. Sharkly is worth exploring for teams interested in connecting Jira work with AI coding agents.

Can Sharkly work with Jira?

Sharkly supports importing Jira work and two-way synchronization for linked projects during a transition. It also connects project management with AI-agent execution through supported coding tools. See Sharkly's Jira comparison for its current capabilities.

Are Jira integrations free?

Some integrations are included with existing products or offer free options. Others require paid Marketplace apps, subscriptions to connected services, or particular Jira plans. Check current pricing before adopting an integration.

Can Jira integrate with AI coding agents?

Yes. Jira can participate in AI-assisted development workflows through Atlassian's own products and supported third-party integrations. The capabilities differ by product. Teams should assess whether they need AI assistance, task execution, or a complete workflow for coordinating multiple agents.

How many Jira integrations should a team install?

There is no universal number. Start with the integrations that solve specific workflow problems, such as linking issues to code or improving build visibility. Add more only when the benefits justify the maintenance and complexity.

Sharkly vs Jira in 2026: Which Project Management Tool Is Better for AI Development Teams?

Emmanuel Mumba — Sun, 11 Oct 2026 17:21:42 +0000

Jira has been a staple of software development teams for years. Developers use it to manage backlogs, organize sprints, track bugs, and coordinate releases across entire organizations.

But software development is changing.

AI coding agents can now write code, fix bugs, generate tests, and work across repositories. As these agents become part of everyday development, teams are starting to ask a different question: what happens when a task is ready to be executed by AI?

This is where the comparison between Sharkly and Jira becomes interesting.

Jira provides an established system for planning and tracking work, with customizable workflows, reporting, and a broad ecosystem of integrations. Sharkly provides a similar planning layer but adds AI agents that can be assigned tasks, execute coding work, and return their progress and results for human review.

The difference isn't simply about managing tasks more efficiently. It's about connecting task management to the actual execution of software development.

So, which platform makes more sense for your team in 2026?

Let's compare their features, AI capabilities, pricing, and workflows to find out.

Sharkly vs Jira: Quick Comparison

Both tools can help development teams plan and track work. The biggest difference is how they approach execution, agent environments, and the relationship between coding work and project management.

What Is Jira?

Jira is Atlassian's project and issue-tracking platform. It helps software teams organize work into issues, epics, projects, and sprints while providing configurable workflows and reporting.

Its capabilities include:

Backlogs and sprint planning
Scrum and Kanban boards
Issue types, priorities, and statuses
Workflow and permission customization
Reports and dashboards
Automation rules
Integrations through the Atlassian Marketplace
AI-powered features through Atlassian's Rovo ecosystem

For larger organizations, Jira is particularly useful because it can support complex processes across teams, departments, and projects.

A development team can create an issue for a bug, assign it to a developer, prioritize it for the next sprint, connect it to a pull request, and track its progress through review and release.

Jira also provides AI capabilities. Atlassian's Rovo platform includes AI search, chat, and agents, with availability depending on the plan and product configuration. Atlassian has also introduced agent-oriented development workflows through Rovo Dev.

This means Jira is not simply a traditional tracker without AI. It is an established work management system that continues to expand its AI capabilities.

For teams that already rely on Jira for reporting, permissions, integrations, and coordination, replacing it can be a significant undertaking.

What Is Sharkly?

Sharkly is a work management platform designed for teams where humans and AI agents can both participate in getting work done.

It includes familiar project management capabilities such as Issues, Backlogs, Sprints, Projects, Views, comments, and triage. The difference is that an AI Agent or Crew can be assigned to a task just like a human teammate.

Consider a typical development ticket:

Task: Add automated tests for the payment API.

In a conventional workflow, a developer receives the ticket, opens the repository, writes the tests, runs them, and submits the changes.

In Sharkly, the team can assign the ticket to an Agent connected to its coding environment. The Agent can execute the task, report progress and blockers, and return the results to the task for human review.

Sharkly supports coding tools including Claude Code, Codex, and Gemini CLI, along with other compatible tools. Agents can run on connected local or cloud Computers, and isolated worktrees help prevent parallel coding tasks from interfering with one another.

The goal is not to eliminate developers. It is to let them delegate suitable tasks while retaining responsibility for reviewing the work.

That makes Sharkly particularly relevant to engineering teams experimenting with AI-assisted development at scale.

Sharkly vs Jira: 5 Key Differences

1. Task Management and Agile Planning

At the planning level, Sharkly and Jira have considerable overlap.

Both support organizing work, tracking task status, managing backlogs, and coordinating development through boards and sprints.

Jira has a significant advantage in organizational maturity. Its extensive workflow customization, permission controls, reporting, and marketplace integrations make it suitable for teams with complicated processes and established governance requirements.

Sharkly offers the familiar planning structure while connecting it more directly to execution by AI agents. Teams can organize work into projects and sprints, filter tasks through saved views, and use triage to manage incoming work.

For teams that primarily need advanced issue tracking and enterprise workflow customization, Jira is a strong choice.

For teams that want planning and AI execution in the same workflow, Sharkly has a different advantage.

Winner for complex enterprise planning: Jira.

2. AI Agents and Coding Execution

This is the most important distinction between the platforms.

Jira now supports AI through Atlassian Rovo and Rovo Dev, along with supported integrations that allow agents to participate in development workflows. It would therefore be inaccurate to say that Jira cannot work with AI agents.

However, the available execution environments and agent capabilities depend on the specific Atlassian products and integrations being used.

Sharkly is built around assigning work directly to Agents and connecting those Agents to coding tools and execution environments selected by the team.

For example, imagine a sprint contains these three tickets:

Fix a validation bug.
Add unit tests for an existing service.
Update API documentation.

A team could assign suitable tasks to separate Agents rather than asking one developer to execute every task manually.

Those Agents can work in isolated worktrees, allowing multiple tasks to proceed in parallel. Progress and blockers are recorded against the work, and results return for review.

This creates a more direct connection between task assignment and coding execution.

The distinction is not that Jira has no AI capabilities. It is that Sharkly makes Agent assignment and execution a central part of its work management model.

Winner for a workflow centered on connected AI coding agents: Sharkly.

3. Human Review and Execution Visibility

AI-generated code still requires review.

An Agent might complete a task successfully, encounter a dependency issue, or produce a change that needs further work. Developers need to understand what happened before accepting the result.

Sharkly connects execution to the task itself.

Progress and blockers return to the task, and the resulting work can be reviewed before it is merged. Comments can also be used to direct follow-up work.

This keeps the task connected to the execution process rather than treating the coding agent as a separate chat session.

Jira also provides work-item history, workflow states, and integrations with source-control and code-review systems. Teams can use those capabilities to track AI-assisted development.

The difference lies in the execution workflow a team chooses. Sharkly emphasizes an Agent's task assignment, execution trace, and review process as part of the same system.

For teams managing several AI coding agents, this can make it easier to understand which agent is working on which task and what happened during execution.

Winner for an integrated Agent execution-and-review workflow: Sharkly.

4. Integrations and Existing Development Workflows

Jira has a mature integration ecosystem, including tools across source control, development, collaboration, testing, and project reporting.

That ecosystem is one reason many organizations continue using Jira even when they begin experimenting with AI coding agents.

Sharkly supports connections with Jira, GitHub, and Slack.

Its Jira integration is particularly relevant because teams do not necessarily have to abandon their existing tracker to experiment with AI execution.

Sharkly supports importing Jira work and synchronizing linked projects. Teams can bring existing issues into a Sharkly Space and use the imported work as the basis for an Agent-driven workflow.

For example, a team could import a project containing a backlog of bug fixes, assign a small number of suitable tasks to Agents, and evaluate the results before moving more work.

This approach reduces the need to redesign the entire development process before testing the platform.

Winner for breadth of established enterprise integrations: Jira.

Winner for connecting existing Jira work to Sharkly's Agent execution workflow: Sharkly.

5. Migration and Switching Costs

Changing project management platforms is rarely as simple as creating a new account.

Teams may have years of issues, comments, epics, users, sprint data, and established processes. Rebuilding that information manually can create unnecessary work and disrupt ongoing projects.

Sharkly addresses this with its Jira import and synchronization capabilities.

Depending on the import method and selected options, teams can bring issues, epics, statuses, users, and sprint data into a Sharkly Space, including historical information.

Sharkly also supports bidirectional synchronization for linked Jira projects, allowing teams to keep both systems connected while transitioning.

A practical migration could look like this:

Create a Sharkly Space. Start with one project instead of migrating the entire organization.
Import the Jira data. Select the project and map statuses and users as required.
Enable synchronization. Keep supported changes connected while both systems remain in use.
Create an Agent. Connect a local or cloud Computer and configure an appropriate coding tool.
Assign a low-risk ticket. Start with something repeatable, such as adding tests or fixing a small bug.
Review the results. Evaluate the quality of the work, the execution trace, and the amount of developer oversight required.

One important detail: synchronization and historical import are different operations. A sync link keeps subsequent changes connected; bringing over older history requires the relevant import process.

For teams that want to test AI-agent workflows without committing to a complete migration, this gradual approach is useful.

Winner for teams transitioning from Jira while preserving existing work: Sharkly.

Sharkly vs Jira Pricing

Pricing deserves a closer look because the two platforms approach AI usage differently.

Sharkly's published pricing is straightforward: it is free for teams of up to 10 people, with a Team plan at $7 per user per month for larger teams. Model usage stays with the coding subscriptions or API accounts the team connects.

Jira also has a free plan for up to 10 users. Atlassian's published Cloud pricing lists Standard at approximately $7.91 per user per month and Premium at approximately $14.54 per user per month, based on the pricing displayed when checked. Actual pricing can vary by billing cycle, team size, region, and subsequent pricing changes.

Sources: Sharkly's Jira comparison and Atlassian's official Jira pricing.

These prices are not a complete like-for-like comparison. Jira's plans include capabilities that some organizations may need, while the total cost of an AI workflow also depends on model usage, coding subscriptions, integrations, and infrastructure.

For small teams already paying for AI coding tools, Sharkly's pricing model may be attractive because it does not charge a separate Sharkly seat for each Agent.

For large organizations that need advanced governance, reporting, and Atlassian ecosystem capabilities, Jira's broader plans may justify the additional expense.

Who Should Choose Jira?

Jira remains a strong option for teams that need a mature issue-tracking and project management system.

It is particularly suitable for:

Large engineering organizations
Teams managing complex Agile workflows
Companies with extensive Atlassian integrations
Organizations that need granular permissions and workflow customization
Teams relying on established reports and dashboards
Businesses coordinating work across multiple departments

Jira is also worth keeping if your organization already has effective AI workflows through Rovo Dev or other supported integrations.

There is no reason to replace a platform simply because another product has AI features. The important question is whether the alternative solves a problem your current setup does not.

If Jira already handles your planning, reporting, execution integrations, and governance requirements, continuing with it may be the simplest choice.

Who Should Choose Sharkly?

Sharkly is particularly interesting for teams that want AI coding agents to participate directly in software development.

Consider it if your team:

Uses Claude Code, Codex, Gemini CLI, or compatible coding tools
Wants to assign development tickets to AI Agents
Needs several coding tasks to run in parallel
Wants execution progress and blockers connected to tasks
Wants human review before accepting AI-generated changes
Prefers to use coding subscriptions or API accounts it already pays for
Wants to experiment with Agent workflows without rebuilding its entire project management process

It can also make sense for a team that wants to keep Jira while testing a different execution layer.

You do not have to migrate every project or ask every developer to change tools on the same day. Starting with one project and a small number of tickets gives the team a way to measure whether the workflow is genuinely useful.

Final Verdict: Sharkly vs Jira

Jira and Sharkly both help teams plan, organize, and track work. But they approach AI-assisted development from different starting points.

Jira is a mature project and issue-tracking platform with extensive workflow customization, reporting, enterprise administration, and a broad integration ecosystem. Its AI capabilities have also expanded, so it remains a credible option for teams adopting AI within their existing Atlassian environment.

Sharkly focuses more directly on connecting project management to AI-agent execution. Agents can be assigned tickets, run through connected coding tools, work in isolated worktrees, and return progress and results for human review.

That distinction matters most when developers are moving beyond using AI as a coding assistant and starting to delegate entire tasks to agents.

Choose Jira if your priority is established enterprise work management and your current workflows already meet your needs. Choose Sharkly if you want AI coding agents to become assignable participants in your development workflow.

And if you already use Jira, you may not need to choose immediately.

Importing a single project, testing an Agent on a low-risk ticket, and evaluating the results can be a practical way to determine whether Sharkly adds value to your existing setup.

The next phase of software development is not simply about tracking more tasks. It is about coordinating people and AI agents so that work moves from a backlog to a reviewed result.

For teams looking to make that transition, Sharkly is worth exploring.

Frequently Asked Questions

Is Sharkly a replacement for Jira?

Sharkly can replace Jira for teams whose requirements fit its planning and execution features. It also supports importing and synchronizing Jira work, so teams can use the two platforms together while transitioning.

Does Jira support AI agents?

Yes. Jira's AI ecosystem includes Atlassian Rovo and Rovo Dev, as well as supported integrations with external agents. Available capabilities depend on the product, plan, and execution environment.

Can Sharkly import Jira issues and history?

Yes. Sharkly supports Jira imports that can include issues, epics, statuses, users, and sprint data, along with historical information. The exact data available depends on the import method and options selected.

Can Sharkly and Jira run together?

Yes. Sharkly supports synchronization for linked Jira projects. Teams can keep supported changes connected while evaluating the workflow and moving projects gradually.

Can Sharkly Agents write code?

Sharkly Agents can execute coding tasks through connected coding tools and execution environments. The resulting work can be returned to the task for human review before it is accepted or merged.

Is Sharkly cheaper than Jira?

It can be, depending on the team and the features required. Sharkly is free for teams of up to 10 people and lists its Team plan at $7 per user per month above that limit. Jira also offers a free tier, with paid plans providing additional capabilities. Compare current plan details and any AI usage costs before making a decision.

Do I need to stop using Jira to try Sharkly?

No. A team can import one project, test an Agent on a suitable ticket, and evaluate the results before deciding whether to move additional work.

5 Best AI-Native API Platforms for Developers in 2026

Emmanuel Mumba — Thu, 08 Oct 2026 14:13:32 +0000

APIs have always been an important part of software development. But in 2026, they are becoming even more important as AI agents increasingly interact with software through APIs.

That shift is changing what developers expect from API tools.

An API platform can no longer be just a place to send HTTP requests or view documentation. Developers increasingly need tools that can work with AI assistants, generate and validate API artifacts, automate testing, expose API context to coding agents, and fit naturally into CI/CD workflows.

This is where AI-native API platforms come in.

Instead of adding AI as a small feature on top of an existing API client, these platforms are increasingly designed around workflows where developers and AI agents work together.

In this article, we'll look at five of the best AI-native API platforms developers should consider in 2026:

Apidog
Postman
Insomnia
Hoppscotch
Speakeasy

What Is an AI-Native API Platform?

An AI-native API platform is more than an API client with a chatbot attached.

The best platforms connect AI to the actual API development lifecycle.

Depending on the platform, that can include:

Designing APIs
Generating API specifications
Creating documentation
Generating code
Writing API tests
Debugging requests
Creating mock servers
Managing environments
Running automated tests
Connecting APIs to AI agents
Supporting MCP workflows
Integrating with CI/CD
Providing CLI tools for automation

The important distinction is context and execution.

A developer shouldn't have to copy an API specification into an AI assistant, explain the project manually, generate some code, and then return to another application to test it.

An AI-native platform should make that context available to the AI and allow the AI to perform meaningful API-development tasks.

With that in mind, here are five platforms worth considering.

1. Apidog

Apidog is an all-in-one API development platform covering API design, documentation, testing, mocking, debugging, and collaboration.

What makes it particularly interesting in 2026 is how deeply it is connecting those capabilities to AI-assisted development.

Apidog provides AI features for API development, AI-ready documentation, MCP support, and Apidog CLI, which brings Apidog workflows directly into terminals, CI/CD pipelines, and AI-agent environments.

Why Apidog stands out

The Apidog CLI is especially relevant for developers working with coding agents.

Instead of forcing an AI agent to interact with a graphical interface, the CLI gives it command-line access to Apidog capabilities.

An agent can use it to:

Query API resources
Manage endpoints and schemas
Create and update API test cases
Run automated test scenarios
Generate test reports
Manage environments and variables
Import and export API data
Publish documentation
Work with branches
Validate API resources before making changes

Apidog's documentation specifically describes the CLI as being designed for AI agents and lists compatibility with tools such as Cursor, Claude Code, Trae, and Codex.

This is important because an AI coding agent needs more than information.

It needs reliable actions.

For example, instead of asking an agent to manually figure out how to modify an API specification, an AI agent can use Apidog CLI commands to retrieve the relevant project information, make changes, validate them, and run tests.

Apidog also supports MCP, allowing AI assistants to access API specifications and use them for tasks such as code generation and specification lookup.

Best for

Developers and teams looking for an all-in-one API platform with strong AI-agent and CLI workflows.

Standout feature

Apidog CLI + AI Agent Skills

2. Postman

Postman is one of the most established API development platforms, and it has made a major push toward AI-native development.

In 2026, Postman introduced a broader AI-native platform built around the idea that APIs are increasingly the infrastructure behind AI agents. Its platform now connects API development, testing, documentation, Git workflows, API catalogs, and agentic workflows.

Postman's AI capabilities include Agent Mode, which allows developers to use natural language to perform actions such as sending requests, fixing errors, updating tests, and working with API assets.

Postman also provides an MCP server that allows AI agents to work with Postman resources such as:

Workspaces
Collections
Specifications
Mocks
Monitors

The platform can translate natural-language instructions into API workflows through MCP.

Postman CLI and AI agents

Postman is also moving beyond the graphical interface.

Its current CLI workflow allows developers to initialize Postman skills inside coding agents so they can run collections, tests, and API workflows directly from development environments.

That makes Postman a strong option for teams that already have significant investment in Postman and want to introduce AI without abandoning their existing API workflows.

Best for

Teams that want a mature API platform while gradually incorporating AI agents into existing workflows.

Standout feature

Agent Mode + MCP + Postman CLI

3. Insomnia

Insomnia has long been popular with developers who prefer a more focused API development experience.

In 2026, the platform has moved strongly toward an AI-native approach.

Insomnia describes itself as an AI-native API collaboration platform with capabilities for API design, testing, debugging, mocking, and MCP development.

One of its strongest features is flexibility around AI models.

Developers can use hosted models such as Claude, OpenAI, or Gemini, or connect local models and control where their data is processed.

That can be particularly important for organizations with strict privacy or data-residency requirements.

AI-powered API workflows

Insomnia can use AI to generate mock servers from natural-language descriptions.

For example, instead of manually creating every route and response, a developer can describe the API they need and use AI to generate a mock server that can be used for testing.

Insomnia also provides an MCP client, allowing developers to connect to MCP servers, test their tools and resources, and identify issues before those tools are used by AI agents.

For developers working on AI applications, this is a useful capability because MCP servers themselves are becoming part of the API ecosystem.

Design-first development

Insomnia also supports OpenAPI-based, design-first development.

Developers can create OpenAPI specifications and generate collections, tests, and mocks from them. Its Inso CLI can then be used to automate validation and linting through existing Git workflows.

Best for

Developers who want an AI-ready API client with strong local-model, MCP, and design-first capabilities.

Standout feature

AI-native testing + MCP client + flexible AI model support

4. Hoppscotch

Hoppscotch has built a reputation around being a fast, open-source API development platform.

It supports REST, GraphQL, WebSocket, Socket.IO, MQTT, and other protocols, while also offering web, desktop, and CLI workflows.

But Hoppscotch has also been moving toward agentic API development.

Its 2026 releases introduced an MCP server that lets AI hosts such as Claude Code, Claude Desktop, and Cursor interact directly with Hoppscotch workspaces.

An AI agent can use the MCP server to:

Read collections
Write collections
Manage requests
Work with environments
Execute API requests
Validate responses
Generate code
Generate documentation

That turns Hoppscotch from something an AI assistant can simply reference into something an AI assistant can actually operate.

AI Assistant

Hoppscotch also introduced an AI Assistant for Enterprise Cloud and Enterprise Self-Host customers.

The assistant understands the active request, response, environment, and collection and can perform actions such as editing requests, running requests, creating environments, organizing collections, publishing documentation, and managing mock servers.

For organizations that want more control over infrastructure, Hoppscotch's self-hosting option is another advantage.

Best for

Developers who prefer open-source tooling, self-hosting, and flexible AI-agent integrations.

Standout feature

Open-source API tooling + MCP server + self-hosting

5. Speakeasy

Speakeasy takes a somewhat different approach from the other platforms on this list.

Rather than focusing primarily on API request testing, it focuses heavily on turning APIs into high-quality developer and AI interfaces.

The platform starts with OpenAPI specifications and can generate:

Type-safe SDKs
API documentation
Terraform providers
CLI tools
MCP servers

Its documentation describes the platform as OpenAPI-native, with generated SDKs and agent tools kept in sync through CI/CD workflows.

That makes Speakeasy particularly relevant in an AI-native environment.

An API isn't only consumed by humans anymore.

AI agents also need structured, discoverable, reliable interfaces.

MCP server generation

Speakeasy can generate MCP servers from OpenAPI specifications.

The workflow is straightforward:

OpenAPI → MCP tools → AI agent

The platform analyzes the API specification and generates MCP-compatible tools that agents can discover and use.

Speakeasy also provides agent-ready CLI generation.

Generated CLIs can provide machine-readable output, agent modes, schemas, and discovery information designed specifically for AI agents.

This makes it particularly interesting for teams building APIs that need to be consumed by both developers and AI systems.

Best for

API teams focused on SDK generation, developer experience, MCP, and making APIs usable by AI agents.

Standout feature

OpenAPI-to-SDK, CLI, and MCP generation

AI-Native API Platforms Compared

Choosing between these platforms depends heavily on what you're trying to accomplish.

The important thing is that these platforms don't all solve exactly the same problem.

Apidog and Postman are broader API development platforms.

Insomnia focuses strongly on developer-centric API design and testing.

Hoppscotch combines open-source API tooling with increasingly powerful agent integrations.

Speakeasy is particularly focused on turning APIs into high-quality interfaces for developers and AI agents.

Why CLI Support Matters for AI-Native API Development

One feature deserves more attention than it usually gets: the command line.

When a developer works with an AI coding agent, the terminal is often where the agent already operates.

The agent can inspect files, run tests, execute build commands, modify code, and interact with Git.

An API platform that only works through a graphical interface creates another boundary.

The agent has to stop, ask for information, or rely on a separate integration.

A CLI removes that boundary.

For example, an AI agent might be asked:

Update the API endpoint, validate the schema, run the API tests, and generate the test report.

A capable API CLI can give the agent a structured way to perform those actions.

This is one reason Apidog CLI is particularly relevant to AI-native development.

Apidog describes its CLI as providing higher-level actions designed around agent workflows, including read → validate → write → verify operations, structured output, and pre-write validation.

Instead of giving an agent dozens of small API operations, a CLI can provide commands that map more closely to the workflows developers actually perform.

Apidog CLI: Bringing API Workflows Into AI Coding Agents

For developers already using AI coding agents, Apidog CLI is one of the more interesting parts of the Apidog ecosystem.

The CLI isn't simply a way to run API tests from a terminal.

It can expose much of the Apidog API lifecycle to command-line and AI-agent workflows, including API documentation, schemas, mocks, environments, variables, test cases, test scenarios, test suites, reports, imports, exports, and branch collaboration.

This makes it possible to create workflows where an AI agent can interact with the API project directly.

For example:

Developer → AI coding agent → Apidog CLI → API project → Tests → Results

That is different from asking an AI model to generate an API request and manually copying the result into an API client.

The AI has access to the actual project context and can perform verifiable actions against it.

Apidog also provides AI Agent Skills specifically to help agents understand the CLI's commands and workflows.

For teams already using tools such as Claude Code, Cursor, or Codex, this can make API development feel much more like a continuous part of the coding workflow rather than a separate activity.

How to Choose an AI-Native API Platform

The best platform isn't necessarily the one with the most AI features.

Instead, consider how AI fits into your development process.

Choose Apidog if...

You want an all-in-one API lifecycle platform and want AI agents to work with API projects through CLI and MCP workflows.

Choose Postman if...

Your team already relies heavily on Postman and wants to add AI capabilities without replacing its existing API development workflow.

Choose Insomnia if...

You prioritize local development, OpenAPI design, flexible AI model providers, and testing AI/MCP systems.

Choose Hoppscotch if...

You want an open-source, developer-focused API platform with self-hosting and growing AI-agent capabilities.

Choose Speakeasy if...

Your main goal is turning OpenAPI specifications into SDKs, CLIs, documentation, and MCP tools that developers and AI agents can consume.

Final Verdict

AI-native API development is still evolving.

But one thing is already clear: APIs are becoming interfaces not only for applications and developers, but also for AI agents.

That means API tooling needs to evolve as well.

Apidog stands out for combining API design, documentation, testing, mocking, and collaboration with AI-agent workflows through its CLI and MCP capabilities.

Postman brings its enormous API ecosystem into the agentic era with Agent Mode, MCP, CLI workflows, and an increasingly AI-native platform.

Insomnia offers a developer-focused experience with strong AI, MCP, local-model, and design-first capabilities.

Hoppscotch is an attractive option for developers who value open source, self-hosting, and direct AI-agent integration.

Speakeasy takes a different route by making APIs easier for both developers and AI agents to consume through generated SDKs, CLIs, and MCP servers.

Ultimately, the best AI-native API platform is the one that reduces the distance between your API, your development environment, and your AI agent.

And as AI coding agents become a normal part of software development, that distance is likely to matter more than ever.

Frequently Asked Questions

What is an AI-native API platform?

An AI-native API platform is an API development tool designed to work directly with AI-assisted workflows. It can include AI-powered API design, testing, documentation, debugging, MCP integration, agent access, CLI automation, and other capabilities that allow AI systems to interact with API development workflows.

Which is the best AI-native API platform in 2026?

There isn't one universal winner. Apidog is particularly strong for teams that want a full API lifecycle platform with AI-agent, CLI, and MCP capabilities. Postman is a strong choice for teams already invested in its ecosystem, while Insomnia, Hoppscotch, and Speakeasy each have distinct strengths.

Can AI agents work with Apidog?

Yes. Apidog provides AI-agent support through its CLI and AI Agent Skills, and it also provides an MCP server for connecting AI-powered development environments to Apidog projects.

What is Apidog CLI used for?

Apidog CLI allows developers and AI agents to interact with Apidog from the command line. It can be used for API resource management, automated testing, documentation, environments, variables, imports and exports, reports, and CI/CD workflows.

Why is MCP important for API development?

MCP gives AI applications a standardized way to interact with external tools and resources. For API development, MCP can allow an AI agent to access API specifications, execute requests, manage API resources, and perform other development tasks without relying entirely on manual copy-and-paste workflows.

Are AI-native API platforms replacing traditional API clients?

Not necessarily. Most AI-native platforms still provide the traditional API development capabilities developers need. The difference is that AI is becoming another way to interact with those capabilities rather than replacing the underlying API workflow.

Sharkly vs Asana: Which Project Management Tool Is Better for AI-Powered Teams?

Emmanuel Mumba — Tue, 06 Oct 2026 15:27:41 +0000

Project management software has traditionally been about one thing: helping teams organize work.

Create a task. Assign it to someone. Add a deadline. Track the status. Move it across a board. Repeat.

That workflow still works, but AI is changing what happens after a task is created.

AI coding agents can now write code, fix bugs, add tests, investigate issues, and work across repositories. That creates a new question for engineering teams:

Should an AI agent just help manage the work, or should it actually become part of the team doing the work?

This is where the comparison between Sharkly and Asana gets interesting.

Asana is a mature work management platform built around projects, tasks, goals, workflows, reporting, and collaboration. Sharkly takes a similar project-management foundation but builds AI agents directly into the execution workflow.

So, Sharkly.ai vs Asana: which one should you choose?

It depends on how your team works.

Sharkly.AI vs Asana: Quick Comparison

The table makes the products look similar.

The important difference appears when a task leaves the planning stage and needs to be executed.

What Is Asana?

Asana is one of the better-known work management platforms for teams.

It gives organizations a central place to manage projects, tasks, deadlines, dependencies, goals, forms, reporting, automation, and team workloads.

Asana also supports multiple ways to visualize work, including list, board, calendar, timeline, and Gantt views. More advanced plans add features such as portfolios, resource management, goals, reporting, and additional workflow capabilities.

For teams managing marketing campaigns, product launches, operations, HR processes, or cross-functional projects, that broad approach makes sense.

Asana has also moved heavily into AI.

Its current AI offering includes AI Teammates, Asana Dash, AI Studio, AI-assisted project work, and integrations that allow external AI tools to interact with Asana's Work Graph.

So this isn't a comparison between an AI product and a non-AI product.

Both platforms are moving toward AI-powered work management.

The difference is how deeply AI is connected to execution.

What Is Sharkly.AI?

Sharkly is a work management platform designed around teams that include both humans and AI agents.

It provides the familiar building blocks you'd expect from a project management system:

Tasks
Projects
Backlogs
Sprints
Boards
Lists
Dashboards
Gantt views
Comments
Custom fields
Team collaboration

But Sharkly adds another type of assignee.

An AI Agent can become the execution owner of a task.

Instead of simply writing:

Fix the authentication bug.

and assigning it to a developer, a team can assign that ticket to an Agent.

The Agent can then execute the work through connected coding tools, while progress, blockers, and results return to the same task for review.

That creates a different workflow:

Task → Agent → Execution → Review

rather than simply:

Task → Human → Execution

And that distinction is the main reason an engineering team might consider Sharkly instead of a traditional work management platform.

Sharkly vs Asana: Where They Differ

1. Traditional project management

If you're looking strictly at project management, there isn't an enormous gap between the two.

Both platforms can help teams organize projects and tasks, assign responsibility, monitor progress, and visualize work.

Asana has a particularly mature set of project management features.

Its Starter plan, for example, includes Timeline and Gantt views, reporting dashboards, custom fields, forms, automations, templates, and other tools for managing structured projects.

Sharkly also provides boards, lists, tables, dashboards, Gantt views, Projects, Issues, and Sprints.

If your team simply wants a powerful system for organizing human work, Asana is already very capable.

Winner: Asana

For traditional project and work management, Asana's maturity and breadth give it an advantage.

2. AI agents as teammates

This is where the comparison becomes more interesting.

Asana has AI Teammates that can be assigned work and help teams automate tasks and workflows. Asana describes its AI as a way to automate work, provide insights, summarize information, and work alongside teams inside the platform.

Sharkly takes a different approach.

Agents are built directly into the task execution model.

An Agent can be configured with instructions, Skills, and a Runtime, then assigned to a real task. Once assigned, comments, blockers, progress, and results can stay connected to that task.

For a software engineering team, this is important.

The Agent isn't just helping you decide what should happen next.

It can actually be responsible for executing the task.

Winner: Sharkly

For teams specifically looking to put AI agents into the execution loop, Sharkly has a more focused approach.

3. AI coding workflows

Imagine your team has this ticket:

Add automated tests for the payment API.

With a conventional project management workflow, the ticket gets assigned to a developer.

The developer opens their development environment, reads the ticket, checks the repository, writes the tests, runs them, and submits the changes.

With an AI-native workflow, an Agent can take the execution role.

Sharkly supports Agents running through coding tools such as Claude Code, Codex, and Gemini CLI, with Computers and Runtimes providing the execution environment.

The work can happen in an isolated worktree, while the task remains the place where the team can see progress and review the result.

That creates a much tighter connection between project management and software execution.

Asana can certainly integrate with developer tools and its AI capabilities can participate in workflows, but software development execution isn't the central model of the platform.

For engineering teams that already use AI coding agents, this distinction matters.

Winner: Sharkly

4. Human review still matters

One concern with AI agents is giving an AI too much freedom.

Nobody wants an agent making changes to production without oversight.

Sharkly's workflow is designed around keeping the human in the loop.

The Agent works on the assigned task, while its progress and results return to the task for review before the work is merged.

This is a useful middle ground.

The goal isn't:

Let AI do everything.

It's:

Let AI do more of the execution while humans remain responsible for reviewing the outcome.

That model makes more sense for engineering teams that want to increase development capacity without completely removing human oversight.

What About Asana's AI?

It would be misleading to describe Asana as a traditional project management tool that simply added a chatbot.

Asana has made AI a significant part of its platform.

Its current AI features include AI Teammates, Asana Dash, AI Studio, AI-assisted workflows, and integrations with external AI systems.

Asana AI can help with things such as:

Automating repetitive work
Summarizing projects
Drafting updates
Analyzing information
Assigning work to AI Teammates
Building AI-powered workflows

Starting in September 2026, Asana's advanced AI capabilities include AI Teammates and Asana Dash for eligible organizations, with AI usage managed through an AI Request system.

So the real comparison isn't:

Asana has AI vs Sharkly has AI.

It's:

Where does the AI sit in the workflow?

Asana is expanding AI inside an established work management platform.

Sharkly is building the work management platform around a world where AI agents can be assigned real work.

That is a subtle but important difference.

5. Switching From Asana to Sharkly

One of the biggest reasons teams hesitate to change project management platforms is migration.

Nobody wants to rebuild years of projects and task history.

This is where Sharkly's Asana compatibility becomes particularly useful.

Sharkly supports importing Asana projects, tasks, and history. It also supports two-way synchronization so teams can run the systems alongside each other while they transition.

That means a team doesn't necessarily have to make a massive switch overnight.

A practical approach could look like this:

Step 1: Import an existing project

Bring one existing Asana project into Sharkly.

Step 2: Keep the existing workflow

The team can continue using familiar project and task structures rather than starting from scratch.

Step 3: Introduce an Agent

Choose one repetitive engineering task, such as bug fixes, test coverage, or documentation.

Step 4: Assign the task to an Agent

Let the Agent execute the task while the developer reviews the result.

Step 5: Expand gradually

If the workflow works, gradually move more engineering work into Sharkly.

This is arguably more realistic than asking an entire organization to abandon its existing project management system in one afternoon.

6. Sharkly vs Asana Pricing

Pricing is another important consideration.

Sharkly currently offers its full product free for organizations with up to 10 people.

Its Team plan is $7 per user per month when billed annually for organizations above that limit. Agents, Crews, Computers, and Runtimes do not occupy seats. Sharkly also uses a bring-your-own-model-usage approach, meaning model usage runs through the coding plans or API accounts connected to the Runtime.

Asana's current paid plans start at $10.99 per user per month when billed annually for Starter. Advanced is $24.99 per user per month billed annually. Asana's paid plans include AI capabilities, while AI usage can also be governed through its AI Request system.

That makes the pricing models quite different.

For a small team experimenting with AI-powered development, Sharkly's free allowance can make it easier to test the platform without immediately committing to another per-user subscription.

For larger organizations, the choice becomes more dependent on the team's existing workflows, required enterprise features, and how much value they expect from AI-powered execution.

7. Who Should Use Asana?

Asana is still an excellent choice for many teams.

You should probably consider Asana if your main goal is managing projects across different departments.

It is particularly well suited to teams such as:

Marketing
Operations
HR
Product
Sales
Customer success
Cross-functional business teams

Asana's broad collection of project management, reporting, automation, goals, and resource-management features makes it useful when the main challenge is coordinating people and processes.

If your team isn't looking for AI agents to execute technical work, there may be little reason to move away from a platform you already understand.

8. Who Should Use Sharkly?

Sharkly becomes more interesting when software development is a major part of the workflow.

It is worth considering if your team:

Uses AI coding tools regularly
Wants AI agents assigned directly to tickets
Manages multiple AI coding agents
Wants AI to execute repetitive engineering tasks
Wants development progress connected to the original task
Wants humans to review AI-generated work
Is experimenting with AI-native development workflows
Wants to introduce agents without completely abandoning project management

The biggest question isn't whether your team uses AI.

Most modern teams are beginning to.

The better question is:

What do you want the AI to actually do?

If the answer is "help me manage my work," Asana may be enough.

If the answer is "take a ticket, execute the engineering work, and bring the result back for review," Sharkly is built much closer to that use case.

Sharkly vs Asana: Final Verdict

There isn't a universal winner.

Asana is a mature, flexible work management platform with strong project planning, automation, reporting, collaboration, and increasingly sophisticated AI capabilities.

Sharkly takes a narrower but potentially more significant direction.

It treats AI agents as participants in the work itself.

That's the key distinction.

For a marketing team planning campaigns, an operations team coordinating processes, or a company managing complex cross-functional projects, Asana remains a very strong option.

For an engineering team asking:

"What if our AI coding agents could be assigned tickets just like developers?"

Sharkly is a much more interesting proposition.

The shift is subtle but important.

Traditional project management asks:

Who is responsible for this task?

AI-native project management asks:

Who — human or agent — should execute this task, and how do we review the result?

That may be where project management software is heading next.

And for teams already building with AI agents, it may make sense to start making that transition now.

Frequently Asked Questions

Is Sharkly an alternative to Asana?

Yes. Sharkly can serve as a project and work management platform, with tasks, Projects, Sprints, Views, comments, and other planning features. Its main difference is that AI Agents can also be assigned to execute work.

Does Sharkly work with Asana?

Yes. Sharkly supports importing Asana projects, tasks, and history, as well as two-way synchronization during migration.

Does Asana have AI agents?

Yes. Asana currently offers AI Teammates and AI Studio as part of its broader AI-powered work management platform.

Can Sharkly AI agents write code?

Yes. Sharkly Agents can run through connected coding tools and execute development work from assigned tasks. Sharkly currently lists support for tools including Claude Code, Codex, and Gemini CLI.

Is Sharkly free?

Sharkly currently offers its full product free for organizations of up to 10 people. Its Team plan is $7 per user per month when billed annually for larger organizations.

Is Asana better than Sharkly?

It depends on the use case. Asana is particularly strong for general-purpose work management and cross-functional teams. Sharkly is more focused on teams that want AI agents to participate directly in task execution.

How to Work With Agentic Teammates: A Practical Guide to Assigning, Governing, and Reviewing AI Work

Emmanuel Mumba — Tue, 29 Sep 2026 05:55:01 +0000

AI agents are becoming much more useful when they can do more than answer a prompt.

Instead of asking an AI to explain a problem or generate a few lines of code, we can now give an agent a piece of work and let it investigate, make changes, run tests, and report back.

That changes the relationship.

The agent isn't necessarily replacing a person. It is becoming another way a team can execute work.

That is where the idea of agentic teammates comes in.

But once an agent starts behaving like a teammate, the team has to think about more than the quality of the underlying model.

How do you assign work to an agent?

How much context should you give it?

Who remains responsible when an agent performs the work?

How do you review what it did?

What happens when it gets stuck?

And how do you make sure the work doesn't disappear into someone's private AI session?

These questions are becoming just as important as the agents themselves.

What Are Agentic Teammates?

An agentic teammate is an AI agent that participates in a team's workflow by taking on defined pieces of work rather than simply responding to individual prompts.

The distinction matters.

A chatbot might help you think through a problem. An agentic teammate can potentially take the next step.

For example, a developer might create a task to investigate a failing API endpoint. An agent could inspect the relevant code, identify the problem, make a change, run tests, and return its findings.

The developer still owns the outcome.

The agent handles part of the execution.

This creates a useful division of responsibility:

People provide direction, judgment, and accountability.

Agents handle execution where they are capable of doing so.

The exact balance will depend on the task, but the important thing is that the agent becomes part of an existing workflow rather than existing as an isolated AI conversation.

Agentic Teammates Need More Than Prompts

One of the biggest differences between traditional AI assistance and agentic work is that a prompt isn't necessarily enough.

If you're asking an AI a simple question, a single prompt may contain everything it needs.

A real task is different.

An agent might need:

the expected outcome
relevant files or repositories
project conventions
technical constraints
acceptance criteria
available tools
instructions about how the work should be performed
information about what has already been tried

Without that context, the agent has to make assumptions.

And assumptions become more expensive when the agent is allowed to actually change things.

This is why agentic workflows benefit from separating persistent instructions from task-specific requirements.

The persistent instructions can explain how an agent generally works.

The task explains what needs to happen this time.

That makes the workflow easier to repeat without turning every task into a giant prompt.

Give Each Agent a Clear Role

I don't think every agent needs to be a general-purpose AI that can do everything.

In fact, specialized agents can make agentic workflows easier to manage.

Consider a software team with several recurring types of work.

One agent could focus on backend development. Another could handle testing. Another could help with documentation. Another might be configured around research or analysis.

The point isn't that each agent has to be limited to one narrow task.

The point is that the team should know what an agent is configured to do.

That makes assignment more intentional.

Instead of thinking:

Which AI should I open for this?

The question becomes:

Which capability should handle this work?

This is an important shift because it makes the agent reusable.

A team doesn't need to recreate the same instructions every time someone needs a particular type of work done.

Keep Human Ownership Clear

Giving an agent execution responsibility doesn't mean giving it ownership of the outcome.

This distinction is important.

Imagine a developer is responsible for fixing a bug. They assign an agent to investigate the problem and implement the change.

The agent might do most of the actual work.

But the developer is still the person responsible for deciding whether the result is correct and ready to move forward.

This creates two separate concepts:

Responsibility: Who owns the work?

Execution: Who or what is performing the work?

They don't have to be the same.

That separation becomes particularly important as agents become capable of working independently for longer periods.

If nobody is responsible for reviewing the result, autonomy can quickly become a liability.

How Much Autonomy Should an Agent Have?

There isn't one correct level of autonomy for every task.

A useful way to think about it is to consider the consequences of being wrong.

An agent researching possible solutions can usually operate with more freedom than an agent making a change that affects production infrastructure.

Similarly, an agent preparing documentation may need less supervision than one modifying authentication logic.

So instead of asking:

Should agents be autonomous?

I'd ask:

Which parts of this task can the agent safely handle without intervention?

That can lead to a practical division.

An agent might be allowed to:

investigate an issue
search a codebase
propose an implementation
modify files
run tests
prepare documentation
summarize findings

While a person remains responsible for:

approving important architectural decisions
reviewing sensitive changes
resolving ambiguous requirements
accepting the final result
deciding whether something is ready for production

The goal isn't maximum autonomy.

The goal is appropriate autonomy.

Put the Work Somewhere the Team Can See It

This is where agentic workflows can become difficult.

Suppose an agent spends an hour investigating a problem inside a private AI session.

It finds something important.

It makes several changes.

It encounters a blocker.

Then it eventually produces a result.

If all of that information exists only inside that person's session, what happens when someone else needs to review the work?

They have to reconstruct the conversation.

That doesn't scale particularly well.

For agentic teammates to work as part of a team, the important context should stay attached to the work itself.

The task should contain the goal and requirements.

The conversation should capture decisions and follow-up instructions.

The execution history should show what the agent actually did.

The final result should remain accessible to the people responsible for reviewing it.

This is one of the biggest differences between simply using an AI agent and actually building an agentic workflow.

How to Review Work From Agentic Teammates

An agent saying "completed" isn't the same thing as a task being complete.

The review process should focus on the actual result.

Depending on the task, that could mean checking:

whether the original requirements were satisfied
what files or systems were changed
whether tests passed
whether the implementation follows project conventions
whether any assumptions were made
whether the agent encountered unresolved issues
whether anything still requires human judgment

For a coding task, this could involve reviewing the diff and test results.

For research, it could mean checking sources and conclusions.

For documentation, it might mean checking technical accuracy and completeness.

The review process should therefore be connected to the original task rather than treated as a completely separate activity.

That gives the reviewer the context they need to answer a simple question:

Did the agent actually accomplish what we asked it to do?

What Happens When an Agent Gets Stuck?

Agentic teammates will still get stuck.

The problem might be missing context.

The requirements might be ambiguous.

A test might fail.

A dependency might not be available.

The agent might reach a decision that requires someone with domain knowledge.

This doesn't necessarily mean the agent failed.

It means the workflow needs a way for humans to intervene.

A useful pattern is:

Agent works → Agent encounters blocker → Human provides context → Agent continues

The important part is that the intervention should happen within the same piece of work.

You don't want the developer to open another chat, explain the entire situation again, and manually reconstruct everything the agent already knows.

The agent should be able to receive the additional context and continue from there.

That is much closer to how collaboration with a human teammate works.

Use Comments as Part of the Workflow

This also changes how we should think about comments.

In a traditional project-management system, comments are often just discussion around a task.

With agentic teammates, they can become part of the execution loop.

A person might tell the agent:

Use the existing authentication middleware instead of introducing a new dependency.

The agent can then continue the work with that additional instruction.

Or a reviewer might say:

The tests pass, but the error handling doesn't match the existing service pattern. Please revise it.

The important thing is that the instruction stays attached to the work.

This creates a useful feedback loop without requiring the team to constantly switch between a task tracker and a separate AI conversation.

When One Agent Isn't Enough

Some tasks are simple enough for one agent.

Others naturally involve several roles.

Imagine building a new feature.

One agent might investigate the existing implementation.

Another might work on the backend.

Another might create tests.

Another might review the documentation.

You could coordinate all of these manually, but eventually the coordination itself becomes work.

This is where agent teams or groups can become useful.

A coordinating agent can interpret the overall goal and involve other agents when different capabilities are required.

The human team still remains involved, but the coordination can happen within the workflow rather than through a collection of disconnected AI sessions.

The important principle is that multiple agents should not simply mean multiple independent conversations.

There needs to be a shared context around the overall work.

Where Sharkly.AI Fits Into This Model

One practical example of this approach is Sharkly.

Sharkly treats Agents as saved working configurations rather than one-off prompts. An Agent can have instructions, Skills, a Runtime, repositories, environment settings, and run settings, and then be assigned to a Task much like a teammate. The person remains responsible while the Agent handles execution.

The interesting part is what happens around the Agent.

A Sharkly Task can hold the goal, scope, acceptance criteria, people responsible, execution assignee, comments, status, and history. A person can remain the human assignee while an Agent or Crew handles execution.

That directly reflects the separation between responsibility and execution.

It also means the work doesn't have to disappear into a private AI session.

The Agent's conversation, execution, comments, and results can remain connected to the Task. An eligible comment can continue an Agent run when the Agent is the execution assignee.

Reusable instructions with Skills

Sharkly also separates reusable practices from individual task requirements.

Skills can contain reusable instructions and supporting files and be bound to Agents that should follow the same practice, such as review rules, delivery checklists, or writing standards. One-off requirements can remain on the Task instead of being baked into the Skill.

That's a useful pattern for teams because it avoids turning every task into a completely new prompt.

The Agent knows how it generally works.

The Task explains what needs to happen now.

Using Crews for more complex work

When one Agent isn't enough, Sharkly has another layer called Crews.

A Crew consists of a leader Agent plus other Agents and People. The leader can interpret the goal and involve other Agent members when the work requires different roles or sequencing.

That fits naturally with the idea of agentic teammates.

Instead of thinking about each AI session separately, the team can think about which capability should handle each part of the work.

Chat versus tracked work

There is also an important distinction between exploration and actual team work.

Sharkly's standalone Agent Chat is intended for exploring, planning, or acting before work needs ownership. When the work needs shared status, history, or review, it can become a Task.

That distinction makes sense beyond Sharkly.

Not every conversation with an AI needs to become a tracked task.

But once an agent is doing work that other people depend on, the work needs a durable place to live.

A Practical Agentic Teammate Workflow

Putting everything together, a team could structure agentic work like this.

1. Define the outcome

Start with a clear task.

Explain what needs to be accomplished, what is in scope, what isn't, and how the result will be evaluated.

2. Assign human ownership

Make sure someone is responsible for the outcome.

The person doesn't have to perform every step themselves.

3. Choose the appropriate agent

Select an agent whose configuration matches the type of work.

A specialized agent may be more useful than a general-purpose one.

4. Give the agent the right context

Provide the repositories, files, requirements, constraints, and other information necessary for the work.

Don't assume the agent knows project-specific conventions unless they have been provided.

5. Let the agent execute

Allow it to perform the work within the boundaries you've established.

6. Keep communication attached to the task

If something changes, add the new information to the work rather than starting a disconnected conversation.

7. Review the result

Check the actual output against the original requirements.

Don't treat the agent's own completion message as approval.

8. Redirect when necessary

If the work isn't right, explain what needs to change and let the agent continue where appropriate.

9. Accept the result

The responsible human decides whether the work is ready.

This workflow creates a simple feedback loop:

Assign → Execute → Review → Redirect → Accept

The agent can handle more of the execution while the human remains connected to the important decisions.

What Good Agentic Teamwork Looks Like

The best agentic workflows probably won't look like humans disappearing and a collection of autonomous agents taking over a project.

They'll look more like teams gaining another category of teammate.

Some work will still be handled entirely by people.

Some work will be delegated to an agent.

Some work will move back and forth between people and agents.

And some larger tasks will involve multiple agents coordinated around a shared objective.

The common requirement is visibility.

Everyone should be able to understand:

What is being done?

Who owns it?

Which agent is executing it?

What context was provided?

What has happened so far?

What needs review?

What happens next?

Without those answers, agentic workflows can become difficult to manage regardless of how capable the underlying models are.

The Future of Working With Agentic Teammates

Agentic AI changes more than the tools we use.

It changes the unit of work.

For years, a task was something assigned to a person. The person opened their tools, performed the work, and reported back.

Now the execution slot can increasingly be shared between people and agents.

That means project-management systems, development workflows, and collaboration tools need to account for a new reality.

An agent needs more than a prompt.

It needs a role, context, an environment, boundaries, and a way to report its work.

A human needs more than a notification saying that an agent finished.

They need enough context to review what happened and decide what should happen next.

And teams need a shared record that keeps all of this connected.

That is why the concept of agentic teammates is more interesting than simply saying that AI can automate tasks.

The real question is not just:

What can an AI agent do?

It's:

How do we build a team workflow around what the agent can do?

Final Thoughts

Working with agentic teammates requires a slightly different mindset from using AI as an assistant.

The agent shouldn't simply receive a prompt and disappear into a private session.

It should have a defined role, enough context to perform the work, and appropriate boundaries around what it can do.

The team should remain responsible for the outcome, while the agent handles the execution it is capable of handling.

And most importantly, the work should remain reviewable.

That means keeping requirements, conversations, execution history, blockers, and results connected to the work itself.

We're still figuring out what the long-term model for human-agent collaboration will look like. But one thing is already becoming clear: the teams that benefit from agents won't just be the teams with the most capable models. They'll be the teams that learn how to organize work around them.

7 Best AI Project Management Tools for Software Development Teams in 2026

Emmanuel Mumba — Mon, 28 Sep 2026 06:29:25 +0000

How AI is changing project management, from traditional task tracking to workflows where AI agents become part of the team.

AI has changed how software gets written.

But I think we're only starting to understand how much it is going to change how software teams manage work.

A few years ago, a project management tool mainly needed to answer simple questions:

What are we building?
Who is working on it?
What's the deadline?
What's blocked?
What's finished?

That's still important.

But now there's another question:

What happens when some of the work is being done by AI agents?

A developer might use Claude Code to implement a feature. Another might use Codex to fix a bug. An AI agent might generate tests, update documentation, refactor code, or investigate an issue.

Suddenly, the project manager isn't only tracking developers.

They're tracking developers and agents.

And that's where traditional project management tools start to feel different.

In this guide, I'll look at 7 AI project management tools that can help software teams organize development work in 2026, from established platforms adding AI capabilities to tools designed around the emerging human-and-agent workflow.

What Makes an AI Project Management Tool Different?

Before looking at individual tools, it's worth defining what we're actually looking for.

Simply adding an AI chatbot to a project management application doesn't necessarily make it an AI project management tool.

For development teams, I think there are several more useful capabilities to consider.

1. AI-Assisted Task Management

AI can help turn rough requirements into actionable tasks, summarize discussions, identify missing information, or suggest priorities.

2. Developer Workflow Integration

The project management tool shouldn't exist completely separately from the development environment.

GitHub, GitLab, CI/CD systems, IDEs, and AI coding tools all need to work together.

3. AI Agent Support

This is the newer part.

Can an AI agent actually participate in the workflow?

Can it receive a task, work on it, report progress, and return a result for someone to review?

4. Human Oversight

The goal isn't necessarily to remove humans from the process.

For software development, people still need to define requirements, review changes, resolve conflicts, and decide what gets shipped.

5. Context

AI becomes much more useful when it has access to the right project context.

A task shouldn't just say:

Fix login.

It should contain enough information about the problem, requirements, repository, previous discussions, and expected outcome for the person or agent working on it.

With that in mind, let's look at the tools.

1. Sharkly — Project Management Built Around People and AI Agents

Best for: Development teams that want AI agents to participate directly in their work management workflow.

Sharkly approaches AI project management from a slightly different direction.

Instead of treating AI as another assistant sitting beside the project-management system, Sharkly is designed as a shared work system for people and AI agents.

The idea is simple:

AI coding tools are already capable of executing development tasks.

The harder problem is coordinating all that execution.

A team still needs to decide what should be done, provide context, assign responsibility, monitor progress, handle blockers, and review the result.

Sharkly puts that coordination around the AI execution.

Key features

Task-based work management
Multiple AI agents working on tasks in parallel
Connected Computers and AI coding Runtimes
Shared context and project information
Reusable Agents and Skills
Projects and Sprints
Agent activity and execution history
Human review and acceptance
Integrations with existing project-management and collaboration workflows

Real-world scenario

Imagine a team building a SaaS application.

Instead of creating one large ticket called "Build the new dashboard," the work could be divided into smaller tasks:

Research the dashboard requirements
Build the frontend components
Add the required API endpoints
Write automated tests
Update the documentation

Different Agents can work on bounded tasks while developers and product managers maintain visibility into the overall project.

The important distinction is that Sharkly doesn't replace tools such as Claude Code or Codex.

It provides the management and coordination layer around those tools.

That makes it particularly interesting as AI coding moves from individual experimentation toward team-based development.

2. Jira — Established Project Management With AI

Best for: Larger development organizations already using Jira.

Jira remains one of the most recognizable project management platforms in software development.

Its strength comes from the amount of structure it provides around issues, projects, workflows, planning, and development processes.

AI capabilities can assist teams with tasks such as summarizing work, generating content, and working with project information.

Key features

Issue and task management
Agile boards
Backlogs and sprints
Workflows and automation
Reporting
Development integrations
AI-assisted productivity features

Real-world scenario

A large engineering organization might have hundreds of issues across several teams.

A developer can work from an assigned Jira issue while the project manager uses boards and reports to understand overall progress.

AI can then help summarize discussions or assist with repetitive project-management activities.

Jira makes the most sense when a team already has established processes around it and wants to introduce AI without completely replacing its existing project-management system.

3. Linear — Fast Project Management for Product Teams

Best for: Modern software teams that want lightweight issue tracking and strong developer workflows.

Linear has become popular with startups and product-focused engineering teams that want project management without a lot of unnecessary complexity.

Its interface is built around issues, projects, cycles, roadmaps, and team workflows.

Linear has also incorporated AI into its product experience, allowing teams to use AI to help with various aspects of issue and project management.

Key features

Issue tracking
Projects and roadmaps
Cycles
Team workflows
GitHub integration
Automation
AI-assisted workflows

Real-world scenario

Consider a small SaaS team with five developers.

Instead of maintaining a complicated project-management hierarchy, the team can organize work into projects and cycles while developers manage individual issues.

AI can help with repetitive tasks such as creating or organizing issues, while developers remain responsible for implementation and review.

Linear works particularly well when the team values speed and a clean interface.

4. GitHub Projects — Development Work and Planning in One Place

Best for: Teams that already manage their source code and development workflow through GitHub.

GitHub Projects takes a different approach because project management is closely connected to the code itself.

For teams already using GitHub Issues and pull requests, keeping planning close to the repository can reduce context switching.

Key features

Issues
Project boards
Custom fields
Roadmaps
Pull request integration
GitHub Actions
AI development ecosystem

Real-world scenario

A team might create an issue for a new feature, assign it to a developer, connect the issue to a pull request, run automated checks through GitHub Actions, and close the issue when the change is merged.

This workflow becomes even more interesting with AI coding agents.

An agent can work against repository tasks while GitHub remains the place where developers review code and manage the source.

For teams already deeply invested in GitHub, adding another project-management platform may not be necessary.

5. ClickUp — Broad Project Management With AI

Best for: Teams that want project management, documentation, tasks, and AI features in one platform.

ClickUp takes a broader approach than developer-specific tools.

It's designed to handle tasks, documents, goals, project planning, collaboration, and other types of organizational work.

Its AI capabilities can assist with writing, summarization, task-related work, and other productivity workflows.

Key features

Task management
Docs
Goals
Dashboards
Automations
Team collaboration
AI-assisted productivity

Real-world scenario

A software company could use ClickUp to manage more than engineering.

Product requirements, marketing tasks, documentation, design work, and engineering projects can all live in the same environment.

That's useful when development is only one part of a larger operational workflow.

The trade-off is that developers looking for a highly specialized engineering workflow may prefer something more focused.

6. Asana — AI-Assisted Work Management

Best for: Cross-functional organizations managing software projects alongside broader business work.

Asana is another established project-management platform that has incorporated AI into its work-management experience.

Asana is particularly useful when software development is closely connected to product, marketing, operations, design, and other departments.

Key features

Tasks and projects
Timeline planning
Workflows
Goals
Team collaboration
Automation
AI-assisted work management

Real-world scenario

Imagine a company launching a new web application.

Engineering might have development tasks, while marketing prepares the launch campaign, design works on assets, and customer success prepares onboarding materials.

Instead of putting all of that into separate systems, Asana can provide a shared project view.

AI can then assist with repetitive work such as summarizing information or helping teams organize their tasks.

7. Monday.com — Customizable Workflows With AI

Best for: Teams that want highly customizable project and workflow management.

is designed to be flexible enough for different departments and types of work.

Teams can create customized boards and workflows rather than following one rigid project-management methodology.

AI capabilities can be used to automate and assist with different work-management tasks.

Key features

Customizable boards
Task management
Automations
Dashboards
Team collaboration
Workflow customization
AI features

Real-world scenario

A growing software company could use different boards for product development, engineering, customer feedback, and release planning.

For example, customer feedback could automatically become development tasks, while project managers use dashboards to track progress.

This flexibility is useful for organizations that don't want their project-management system to dictate exactly how every team works.

AI Project Management vs. Traditional Project Management

One of the biggest changes I see happening is that project-management systems are moving from tracking people doing work to tracking people and AI doing work.

Traditional workflow:

Requirement → Task → Developer → Code → Review → Release

AI-assisted workflow:

Requirement → Task → Human/Agent → Execution → Tests → Evidence → Human Review → Release

The difference is subtle but important.

An AI agent can potentially work continuously and handle several types of tasks.

But that doesn't mean the project manager's job disappears.

If anything, coordination becomes more important.

Someone still needs to decide:

What should the agent work on?
Does it have enough context?
What permissions should it have?
How do we know the result is correct?
Who reviews the changes?
What happens when two tasks conflict?
Is the work actually ready to ship?

That's why I don't think the future of AI project management is simply "let AI manage everything."

It's more about creating systems where AI can participate in the workflow without making the workflow invisible.

How to Choose an AI Project Management Tool

There's no single best platform for every team.

I'd start with the way your team actually works.

If your team is already heavily invested in Jira

Moving everything to a new platform may create more problems than it solves.

Look at how the AI features integrate with your existing workflows first.

If you're a small developer-focused team

Something lightweight like Linear may be more appropriate than a large enterprise platform.

If everything already lives in GitHub

GitHub Projects can keep planning close to the code and pull requests.

If you manage multiple departments

ClickUp, Asana, or Monday.com may make more sense because they're designed for broader organizational workflows.

If AI agents are becoming actual participants in development

This is where a platform like Sharkly becomes particularly interesting.

The question changes from:

"How can AI help me manage my project?"

to:

"How can my team manage work when AI agents are doing some of the work?"

That's a very different problem.

What I Think Comes Next

I don't think AI project management is going to be about adding a chatbot to every Jira board.

The bigger shift is going to happen when AI agents become normal participants in software development.

Imagine opening a project dashboard and seeing:

3 developers working

5 AI agents executing tasks

2 tasks waiting for human review

1 agent blocked by a failing test

4 tasks ready for verification

That starts to look less like traditional project management and more like an operating system for a mixed human-and-AI development team.

And that creates a new set of requirements around context, permissions, task ownership, execution history, isolation, testing, and human approval.

The project-management tools that adapt to that reality will become increasingly useful.

Final Thoughts

AI is making software development faster, but speed creates a coordination problem of its own.

When one developer uses an AI assistant to write a few lines of code, traditional project management works perfectly well.

When an entire team starts using multiple AI agents to research, code, test, document, and maintain software, the workflow becomes much more complicated.

That's where AI project management tools become interesting.

Some platforms are adding AI to existing project-management workflows.

Others are building AI-native collaboration experiences.

And tools like Sharkly are exploring a different model where people and AI agents can participate in the same work system, while humans remain responsible for direction, review, and acceptance.

I don't think there will be one universal winner.

The right choice depends on how your team develops software today and how much AI you expect to introduce tomorrow.

But one thing seems increasingly clear:

The future of software development isn't just about AI that can write code. It's about building workflows where humans and AI can work together without losing visibility, context, or control.

Top 5 MCP Gateways in 2026

Emmanuel Mumba — Tue, 22 Sep 2026 15:55:33 +0000

Model Context Protocol has changed the way AI applications interact with external tools.

Instead of building a separate integration for every AI application and every tool, MCP gives developers a standardized way to connect models and agents to external systems.

That sounds simple when you're working with one agent and a couple of MCP servers.

It becomes a very different problem when an organization has dozens of AI applications, hundreds of tools, multiple teams, and production workloads running across different environments.

At that point, connecting MCP servers isn't the difficult part.

Managing them is.

Who can access a particular tool? How do you authenticate users and applications? How do you monitor tool calls? How do you handle multiple MCP servers behind a single endpoint? And how do you keep AI traffic observable as the number of agents grows?

That's where MCP gateways come in.

I've been looking at how the AI infrastructure stack is evolving, and MCP gateways are increasingly becoming the layer between AI applications and the tools they use. Projects such as Bifrost, for example, are approaching this problem by combining MCP management with broader AI gateway capabilities. You can explore the Bifrost project to see how that approach works in practice.

In this article, I'll look at five MCP gateway solutions worth considering in 2026: Bifrost, OpenRouter, Cloudflare AI Gateway, Kong AI Gateway, and LiteLLM.

The goal here isn't to suggest that every gateway is interchangeable. Each one approaches AI traffic, model routing, MCP, and governance somewhat differently, so the right choice depends on what you're actually trying to manage.

TL;DR

Bifrost — Best for enterprise AI and MCP governance, with centralized access control, tool management, and observability. Bifrost is built for teams managing AI infrastructure at scale.
OpenRouter — Best for unified access to multiple models and providers, with routing and fallback capabilities.
Cloudflare AI Gateway — Best for AI traffic management, including analytics, caching, rate limiting, retries, and provider routing.
Kong AI Gateway — Best for enterprises extending API management and governance to LLMs, MCP servers, and AI agents.
LiteLLM — Best for teams wanting an open-source gateway for multiple LLM providers, with built-in authentication, logging, and cost tracking.

The right choice depends heavily on whether your priority is MCP governance, model routing, API infrastructure, edge deployment, or self-hosted flexibility.

What Should You Look for in an MCP Gateway?

Before comparing the different options, it helps to understand what actually matters when evaluating an MCP gateway. The right gateway should not only connect AI applications to MCP servers but also provide the controls needed to operate those connections reliably at scale.

1. MCP Connectivity

A good MCP gateway should make it easier to connect, manage, and organize multiple MCP servers from a centralized layer. Instead of requiring every AI client or agent to configure individual servers separately, the gateway can provide a consistent way to discover and access the tools those servers expose.

This becomes increasingly useful as organizations add more MCP servers and AI applications to their infrastructure.

2. Authentication and Access Control

Enterprise environments need clear controls over who can access specific MCP servers and tools. Authentication establishes who is making a request, while access controls determine which resources that user, application, or agent is actually allowed to access.

This is particularly important when MCP servers expose internal systems, sensitive data, or actions that should only be available to certain teams or applications.

3. Observability

Once AI applications begin using multiple tools, teams need visibility into what is happening across those interactions. An MCP gateway should make it possible to understand which tools are being called, how frequently they are being used, how requests are performing, and whether calls are succeeding or failing.

Good observability also makes it easier to troubleshoot problems and identify unusual usage patterns before they become larger operational issues.

4. Routing

Organizations using multiple models or providers may need more than a simple connection layer. Intelligent routing can direct requests to different providers or models based on factors such as availability, latency, capabilities, or cost.

This can help teams improve reliability while giving them more flexibility as their AI infrastructure evolves.

5. Governance

As AI adoption grows, governance becomes increasingly important. Organizations may need to manage budgets, permissions, rate limits, audit logs, and policies across different applications, users, agents, and tools.

An MCP gateway can provide a centralized enforcement point for these controls, helping teams manage AI usage without having to implement the same policies independently across every application.

6. Deployment Flexibility

Infrastructure requirements vary from one organization to another. Some teams may prefer a managed service that can be deployed quickly, while others may need to run the gateway within their own cloud, network, or on-premises environment.

Deployment flexibility can therefore be an important consideration, particularly for enterprises with specific security, compliance, or infrastructure requirements.

With these criteria in mind, let's look at the five MCP gateway options and where each one fits.

1. Bifrost

Bifrost is a high-performance AI gateway that combines LLM infrastructure with MCP gateway capabilities.

Key capabilities

MCP gateway for centralized tool access
Virtual keys
Tool-level access control
MCP Tool Groups
Audit logging for MCP tool calls
Multiple MCP connection types
Authentication and OAuth support
Model routing and load balancing
Semantic caching
Guardrails
Usage and cost visibility
Claude Code and other MCP client integrations

The access-control model is particularly relevant for enterprise deployments.

Instead of simply giving an agent access to an entire MCP server, Bifrost can scope access at the tool level. For example, an application could be allowed to call customer_lookup without being given access to a destructive operation on the same server.

MCP Tool Groups also make it possible to organize tools into reusable collections and assign them to different users, teams, or virtual keys.

That becomes useful when an organization has hundreds of tools rather than five or ten.

Another important capability is audit logging. MCP tool calls can be tracked alongside information about the server, tool, virtual key, latency, and related AI request, giving teams more visibility into what agents are actually doing.

Bifrost is also open source, with its code available on GitHub, while enterprise deployments provide additional capabilities around governance, private infrastructure, clustering, and scale.

Best for

Bifrost is built for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. It serves as a centralized AI gateway to route, govern, and secure all AI traffic across models and environments with ultra low latency. Bifrost unifies LLM gateway, MCP gateway, and Agents gateway capabilities into a single platform. Designed for regulated industries and strict enterprise requirements, it supports air-gapped deployments, VPC isolation, and on-prem infrastructure. It provides full control over data, access, and execution, along with robust security, policy enforcement, and governance capabilities.

2. OpenRouter

OpenRouter takes a different approach.

Rather than primarily positioning itself as an enterprise MCP governance layer, it focuses heavily on providing unified access to a large ecosystem of models and providers through a common API.

This makes it particularly useful when developers want to experiment with different models without rewriting their applications around every provider's API.

OpenRouter also provides routing capabilities that can help applications select between models and providers.

Its fallback functionality can automatically try alternative models when a primary model or provider is unavailable, while provider routing gives developers additional control over where requests are sent.

Key capabilities

Unified API for multiple models
Model and provider routing
Automatic fallbacks
Provider availability tracking
BYOK support
Model comparison and discovery
Prompt caching support
Usage and credit management

OpenRouter is particularly attractive for developers who care about model choice.

Instead of building separate integrations for every model provider, developers can work through a common interface and change models through configuration.

Its routing infrastructure can also help with availability and provider selection.

However, organizations evaluating OpenRouter specifically as an MCP gateway should distinguish its model-routing capabilities from the deeper tool governance capabilities offered by MCP-focused platforms.

Best for

Developers and teams that prioritize multi-model access and provider routing.

OpenRouter is a good fit for teams that want to access multiple models through a unified API rather than managing separate integrations for each provider. Its routing and fallback capabilities also make it useful when applications need to switch between providers based on availability, performance, or other routing requirements.

3. Cloudflare AI Gateway

Cloudflare AI Gateway sits at the intersection of AI traffic management and Cloudflare's broader edge infrastructure.

The platform provides a gateway layer for connecting applications to AI providers while adding capabilities such as analytics, caching, rate limiting, retries, and model fallback.

That makes it interesting for teams already using Cloudflare or looking for an edge-oriented approach to AI infrastructure.

Key capabilities

AI request analytics
Request logging
Response caching
Rate limiting
Request retries
Model fallback
Dynamic routing
Spend limits
Guardrails
BYOK
Support for multiple AI providers

Caching is one of its more straightforward cost-optimization features. Repeated requests can be served from Cloudflare's cache instead of being sent to the upstream model provider again.

Cloudflare also provides spend limits and rate limiting, which can help teams prevent unexpected usage from turning into unexpectedly large bills.

For teams already operating applications on Cloudflare's infrastructure, having AI traffic management in the same ecosystem can simplify the architecture.

Best for

Teams already using Cloudflare that want to add observability, caching, rate limiting, and routing to AI applications.

Cloudflare AI Gateway works well for organizations that want to manage AI traffic alongside their existing Cloudflare infrastructure. It provides a centralized layer for monitoring requests and applying controls such as caching, rate limits, retries, spend limits, and provider routing without requiring teams to build these capabilities from scratch.

4. Kong AI Gateway

Kong approaches AI gateways from the perspective of API management and enterprise connectivity.

Its AI Gateway is designed to govern different types of AI-native traffic, including LLM requests, MCP traffic, and agent-to-agent communication.

That broader approach is interesting because production AI systems are increasingly becoming more than simple model APIs.

An application may involve:

User → Agent → LLM → MCP Server → Internal API → Another Agent

A gateway that understands these different traffic types can provide a centralized control layer.

Key capabilities

LLM traffic management
MCP server support
A2A traffic management
Authentication
Access control
Routing and load balancing
AI consumer groups
Semantic caching
Prompt compression
Cost management
Audit logs
OpenTelemetry integration
Guardrails and data protection

Kong also brings its existing API gateway ecosystem into AI infrastructure.

For organizations already using Kong extensively, this can make AI governance feel like an extension of infrastructure they already understand.

Its MCP capabilities are also broader than simply proxying a server. Teams can expose APIs as MCP tools and govern those interactions through the gateway.

Best for

Enterprises that want to apply established API management and governance practices to AI and MCP workloads.

Kong is a strong fit for organizations already using API gateways and looking to extend those patterns to LLMs, MCP servers, and AI agents. It provides capabilities such as authentication, access control, routing, load balancing, analytics, and AI-specific policies through a centralized gateway layer.

5. LiteLLM

LiteLLM has become popular among developers who want an open-source way to standardize access to different LLM providers.

Its proxy server provides a unified interface for multiple providers while adding features for authentication, logging, cost tracking, rate limiting, and routing.

This makes it particularly attractive for teams that want to build their own AI infrastructure rather than rely entirely on a managed platform.

Key capabilities

Open-source LLM proxy
Multi-provider support
Authentication hooks
Logging
Cost tracking
Rate limiting
Model routing
Fallbacks
Budgets
Self-hosted deployment

One of LiteLLM's biggest advantages is flexibility.

Developers can deploy the proxy themselves and configure it around their existing infrastructure.

This is especially useful for teams that want control over deployment and configuration or want to build additional infrastructure around an open-source foundation.

The tradeoff is that more flexibility can also mean more responsibility.

Teams operating their own gateway need to think about deployment, scaling, monitoring, upgrades, and security themselves.

Best for

Best for: Developers and engineering teams that want an open-source gateway for accessing and managing multiple LLM providers.

LiteLLM is useful for teams that prefer a flexible, developer-oriented approach to model access and want to standardize interactions with different providers behind a common interface. Its proxy layer also provides features such as authentication, spend tracking, rate limiting, logging, and cost management for teams operating multiple models.

MCP Gateway Comparison

The five platforms in this list are not identical products. While they can all sit between AI applications and the models or tools they use, they approach that role from different angles.

Some focus primarily on multi-model access and provider routing, while others put more emphasis on AI governance, API management, observability, or MCP-specific controls. The table below provides a high-level view of where each platform fits.

MCP Gateway	Multi-Model Access	Provider Routing	MCP Support	Governance & Access Control	Observability	Best Suited For
Bifrost	Yes	Yes	Strong	Strong	Strong	Enterprise AI and MCP governance
OpenRouter	Strong	Strong	Limited/indirect	Moderate	Strong	Multi-model access and provider routing
Cloudflare AI Gateway	Yes	Yes	Yes	Strong	Strong	AI traffic management and Cloudflare environments
Kong AI Gateway	Yes	Yes	Strong	Strong	Strong	Enterprise API and AI governance
LiteLLM	Strong	Yes	Yes	Strong	Strong	Open-source LLM gateway and model management

The comparison shows why there isn't a single gateway that fits every architecture. A team primarily looking for access to many models may have different requirements from an enterprise that needs centralized MCP governance, API management, access policies, and detailed observability.

Comparing the Gateways by Use Case

Instead of asking which MCP gateway is universally "best," it is more useful to start with the problem you are trying to solve. Different platforms make different trade-offs depending on whether your priority is model access, provider routing, enterprise governance, MCP management, or integration with an existing infrastructure stack.

If Your Priority Is...	Gateway to Consider	Why
Enterprise MCP governance	Bifrost	Provides centralized management for MCP tools alongside access controls, virtual keys, tool groups, observability, and enterprise governance features.
Access to multiple models and providers	OpenRouter	Provides a unified interface for accessing models from different providers, with routing and fallback capabilities.
AI traffic management within Cloudflare	Cloudflare AI Gateway	Adds analytics, caching, rate limiting, retries, spend controls, and routing to AI workloads within the Cloudflare ecosystem.
Enterprise API and AI management	Kong AI Gateway	Extends established API gateway and governance capabilities to LLMs, MCP, and AI agents.
Open-source LLM gateway	LiteLLM	Provides a flexible proxy layer for accessing multiple LLM providers while adding authentication, logging, rate limiting, and spend tracking.

What This Comparison Tells Us

The main takeaway is that an MCP gateway is becoming more than just a way to connect an MCP client to an MCP server. As AI applications move into production, teams increasingly need a control layer that can handle routing, authentication, access policies, monitoring, costs, and tool governance.

That is also why the right choice depends heavily on the architecture and requirements of the organization. A developer experimenting with multiple models may prioritize simplicity and provider access, while a larger organization running AI agents across multiple teams may need much stronger governance and centralized controls.

What Makes an MCP Gateway Enterprise-Ready?

For smaller projects, connecting an MCP server may be all you need.

Enterprise deployments are different.

The gateway needs to become part of the organization's security and infrastructure model.

That means thinking about:

Identity: Who is making the request?
Permissions: What tools is that identity allowed to use?
Governance: What policies should apply to those requests?
Observability: What happened when the request was executed?
Scalability: Can the gateway handle hundreds of tools, users, and applications?
Reliability: What happens if an MCP server or AI provider becomes unavailable?

This is where the difference between an MCP proxy and an enterprise MCP gateway becomes more apparent.

A proxy can forward traffic.

An enterprise gateway needs to help organizations control that traffic.

Verdict: Which MCP Gateway Should Enterprises Consider?

There isn't a single MCP gateway that makes sense for every organization.

OpenRouter is compelling when the main problem is accessing and routing across a broad model ecosystem.

Cloudflare AI Gateway makes sense for teams that want AI traffic management closely integrated with Cloudflare's edge infrastructure.

Kong is particularly relevant for organizations that already think of API management as a centralized governance layer and want to extend that approach to LLM, MCP, and agent traffic.

LiteLLM remains an attractive option for teams that prioritize open-source flexibility and want to operate their own LLM proxy infrastructure.

Bifrost stands out when the requirement is broader enterprise AI infrastructure with MCP governance at its center.

The combination of virtual keys, tool-level permissions, MCP Tool Groups, audit logging, multi-provider routing, cost controls, and MCP gateway capabilities gives enterprises a way to manage not only how models are accessed, but also how AI agents interact with the tools behind them.

That distinction matters.

As organizations move from experimenting with MCP to running AI agents in production, the question won't simply be:

"Can our AI application connect to this tool?"

It will become:

"Who can access this tool, under what conditions, and can we see exactly what happened?"

That's where an enterprise MCP gateway becomes much more than another proxy sitting in front of an API.

It becomes part of the governance layer for the AI stack.

Frequently Asked Questions

What is an MCP gateway?

An MCP gateway is a centralized layer between AI applications and MCP servers. It helps manage connections to tools while providing additional controls for authentication, permissions, routing, and monitoring.

Why do enterprises need an MCP gateway?

As organizations deploy more AI agents and MCP servers, managing every connection independently can become difficult. A gateway provides a centralized layer for controlling access, enforcing policies, and monitoring how AI applications interact with tools.

What should enterprises look for in an MCP gateway?

Important capabilities include authentication, granular permissions, observability, routing, audit logging, and scalability. The right combination depends on how many users, applications, models, and MCP servers the organization needs to manage.

Can an MCP gateway control access to individual tools?

Yes. Some gateways allow organizations to apply permissions at the tool level, rather than giving an application unrestricted access to every tool exposed by an MCP server. This can be useful when different teams or agents require access to different capabilities.

How does an MCP gateway improve AI security?

An MCP gateway can provide a centralized enforcement point for authentication, permissions, and monitoring. For example, enterprise platforms such as Bifrost can combine MCP access controls with broader AI infrastructure governance.

Is an MCP gateway only useful for MCP servers?

Not necessarily. Modern AI gateways can also provide model routing, provider management, observability, and cost controls, making them part of the broader governance layer for an AI stack.

7 AI-Native Postman Alternatives for Terminal-Based API Development

Emmanuel Mumba — Sat, 19 Sep 2026 04:41:36 +0000

I still use graphical API clients when I need to explore an API, inspect a response, or quickly put together a request.

They're convenient.

But the way I work with APIs has changed.

More of my development workflow happens in the terminal now. Tests run there. Git commands run there. CI/CD runs there. And increasingly, AI coding agents are working there too.

That creates a problem for API tools that assume the GUI is always going to be the center of the workflow.

If an AI agent is working on a backend feature, I don't necessarily want it to stop, open a graphical application, click through a collection, and manually run an API test.

I want the API tooling to be accessible from the same environment where the rest of the work is happening.

That's why I'm increasingly interested in AI-native Postman alternatives that work in the terminal.

The important distinction, though, is that "terminal API client" can mean very different things.

A tool like curl is excellent for sending an individual request.

A terminal REST client can make interactive API exploration easier.

But a development team may need much more than sending HTTP requests.

They may need reusable test scenarios, environments, assertions, reports, API schemas, documentation, CI/CD integration, and increasingly, a CLI that an AI agent can actually operate.

That's the category I want to explore here.

What Makes an API Tool AI-Native?

I don't think adding the word "AI" to an API client automatically makes it AI-native.

For me, an AI-friendly API tool needs to work well with the way agents actually operate.

An agent needs to be able to:

Run commands from a terminal
Understand structured output
Execute repeatable tests
Work with API specifications and schemas
Read and modify API resources
Work with environments and variables
Detect failures programmatically
Verify changes after making them
Fit naturally into CI/CD
Avoid depending on a GUI

That's a different requirement from simply having an AI assistant inside an API client.

The terminal itself becomes part of the interface.

And that matters because AI coding agents already spend a lot of their time there.

Why the Terminal Matters More With AI Agents

Consider a typical development workflow.

I ask an AI coding agent to add a new endpoint.

The agent needs to:

Understand the existing API structure
Implement the endpoint
Update the API contract
Run tests
Inspect failures
Fix the implementation
Run the tests again
Confirm the final result

If API testing lives entirely inside a GUI, the workflow becomes fragmented.

The agent can write the code, but then the human has to take over and perform the API testing manually.

That's not necessarily bad.

But it creates a context switch.

A command-line API tool changes that.

The agent can call the API tooling directly, inspect the output, make a correction, and run the test again.

This is where terminal-first API tooling becomes much more interesting.

1. Apidog CLI

Apidog CLI is interesting because it isn't simply a command-line replacement for sending HTTP requests.

It brings a much larger part of the API development workflow into the terminal.

The CLI can run API test scenarios, work with API resources and schemas, manage environments and variables, import and export API data, and integrate with CI/CD workflows. Its command surface extends beyond simply making individual HTTP requests.

What makes it different

The important part is that the CLI doesn't require you to rewrite your existing API tests as shell scripts.

You can create test scenarios in Apidog and then execute those scenarios headlessly from the command line.

For example:

apidog run -t <scenario_id> -e <environment_id> -r cli

The CLI can also produce CLI, HTML, JSON, and JUnit reports, which makes it useful both locally and inside automated pipelines.

That gives you a workflow where the visual interface can be used for authoring and debugging, while the terminal handles repeatable execution.

Where the AI angle comes in

This becomes particularly useful with coding agents.

An agent doesn't need to understand how to click through an API testing application.

It can invoke CLI commands, inspect structured results, and use those results to decide what to do next.

Apidog also documents a workflow specifically for using the CLI with AI agents, including validating structured changes, applying changes through the CLI, reading resources back, and running API tests through an agent.

That makes the CLI more than a CI utility.

It can become part of the agent's development toolbox.

A practical example

Imagine an agent is asked to add a /users/preferences endpoint.

It modifies the backend code.

Then it can run an existing API test scenario from the terminal.

If an assertion fails, the agent can inspect the result, identify the problem, modify the implementation, and run the scenario again.

The entire feedback loop can happen without leaving the development environment.

That's the kind of workflow I mean when I talk about an AI-native API tool.

2. Bruno

Bruno takes a different approach to the terminal API workflow.

Instead of centering everything around a cloud workspace, Bruno is designed around local, Git-friendly API collections.

That makes it interesting for developers who want their API requests and tests to live alongside the codebase.

What makes it different

The basic idea is straightforward:

Your API collection can live as files in your repository.

That means developers can version it with Git, review changes through pull requests, and keep API-related work close to the application code.

The command-line workflow then becomes a natural extension of that approach.

Where the AI angle comes in

This model can work particularly well with coding agents because agents are already comfortable working with files in a repository.

An agent can inspect the API collection, make changes, run the relevant commands, and commit the result.

There's less dependence on a private workspace that exists outside the repository.

A practical example

A developer asks an agent to add a new endpoint and update its API tests.

The agent modifies the application code and the corresponding Bruno collection.

It runs the tests from the terminal.

If something fails, it has both the application code and API test definitions available in the same working environment.

That's a relatively natural fit for Git-based development.

3. Hurl

Hurl takes the terminal-first approach even further.

Instead of providing a traditional graphical API client experience, it lets you describe HTTP requests and assertions in plain text files and execute them from the command line.

What makes it different

A Hurl test can contain a sequence of HTTP requests along with assertions about the responses.

That makes the test itself a text artifact.

You can store it in Git, review it, run it in CI, and execute it locally from a terminal.

For teams that prefer infrastructure-as-code and test-as-code workflows, that's attractive.

Where the AI angle comes in

AI coding agents work extremely well with text files.

An agent can read a Hurl test, understand the request and expected response, modify it, and run it.

There's no special project workspace for the agent to navigate.

The test is simply another file in the repository.

A practical example

Suppose an authentication flow requires:

Login → receive token → request protected resource → verify response

A Hurl file can represent that workflow.

An agent can modify the test when the authentication contract changes and run it as part of its development loop.

That's a very direct terminal-first approach.

4. HTTPie

HTTPie is another familiar option for developers who want to work with APIs from the command line.

Its strength is simplicity.

Instead of opening a graphical API client just to inspect an endpoint, you can send a request directly from the terminal and get readable output.

What makes it different

HTTPie is particularly useful for interactive API exploration.

If I want to quickly test an endpoint, send JSON, add authentication, or inspect a response, I can do it without leaving the terminal.

That makes it useful for the small API tasks that don't justify setting up a full test scenario.

Where the AI angle comes in

HTTPie can also be useful as a low-level tool for AI coding agents.

An agent can execute a request, inspect the response, and use that information while debugging an application.

But there's an important distinction here.

HTTPie isn't trying to be a complete API project management or test-scenario platform.

And that's okay.

Not every API interaction needs to be a large workflow.

A practical example

An agent has just implemented an endpoint and wants to verify that it returns the expected JSON.

Instead of creating a full test suite, it can make a quick HTTP request from the terminal and inspect the response.

For that kind of job, a lightweight CLI can be exactly what you need.

5. cURL

It would be difficult to talk about terminal-based API tools without mentioning curl.

It's probably the most fundamental option on this list.

And its biggest advantage is that it's already everywhere.

What makes it different

curl is simple, scriptable, and available on practically every development environment.

You can send GET, POST, PUT, PATCH, and DELETE requests, add headers, provide request bodies, follow redirects, handle authentication, and pipe the output into other command-line tools.

Where the AI angle comes in

AI coding agents already know how to use command-line tools.

That makes curl an obvious building block for agent workflows.

An agent can use it to test an endpoint, inspect a response, or reproduce a bug.

But again, I wouldn't confuse a useful building block with a complete API testing platform.

A curl command doesn't automatically give you the project-level concepts that a dedicated API platform provides.

You still need to handle test organization, assertions, reporting, environments, and broader API lifecycle management yourself.

A practical example

A developer is debugging an authentication issue.

They need to reproduce one request with a specific header and body.

A simple curl command may be all they need.

There's no reason to introduce a larger tool for every small API check.

6. Schemathesis

Schemathesis takes another route.

Instead of primarily focusing on manually creating requests, it uses API schemas to generate tests and find unexpected behavior.

That makes it particularly interesting for teams that already maintain OpenAPI specifications.

What makes it different

The API contract becomes an important input into the testing process.

Rather than manually writing every possible request, schema-driven testing can generate cases based on the API definition.

This can help uncover edge cases that a handful of manually created examples might miss.

Where the AI angle comes in

Schema-driven testing can complement AI coding agents because the agent doesn't have to invent every test case itself.

The API contract provides a structured description of what the API is supposed to look like.

The testing tool can then explore that contract.

An agent can run the tests, inspect failures, and use the results to investigate the implementation.

A practical example

A developer adds a new OpenAPI endpoint.

The agent updates the implementation and specification.

Schemathesis can then generate tests from that contract and expose unexpected behavior.

The agent can use those failures as another feedback loop during development.

7. Postman CLI

It would be strange to discuss Postman alternatives without mentioning Postman's own command-line tooling.

The Postman CLI allows collections to be run from the terminal and integrated into CI/CD workflows.

What makes it different

The main advantage is continuity.

If a team already has a large collection of Postman tests, moving those tests into a completely different ecosystem may not make sense.

The CLI lets the existing Postman workflow extend into automated environments.

Where the AI angle comes in

The same basic principle applies here as with other terminal tools.

An AI agent can operate command-line tools more naturally than it can operate a graphical interface.

So putting API tests behind a CLI makes them more accessible to automated development workflows.

The bigger question is what you want your terminal tool to control.

A simple collection runner may be enough for one team.

Another team may want the CLI to reach deeper into the API lifecycle.

That's where the differences between these tools become more significant.

The Important Difference: Request Tool vs API Workspace

One thing became clearer to me while looking at these tools.

Not all terminal API tools are solving the same problem.

There is a big difference between:

"Send this HTTP request."

and:

"Work with this API project."

curl and HTTPie are excellent for individual requests.

Hurl focuses on test definitions as files.

Bruno combines API collections with a Git-friendly workflow.

Schemathesis uses API schemas to drive testing.

Postman CLI runs Postman collections.

And Apidog CLI goes further toward bringing the API workspace itself into the terminal, including tests, endpoints, schemas, environments, imports, exports, documentation, mocks, and other project resources.

That distinction matters even more when AI agents enter the workflow.

An agent doesn't necessarily need another tool that can simply send HTTP requests.

It may need to understand and operate on the API project itself.

What I Would Look for in an AI-Friendly API CLI

If I were choosing an API CLI for a team that is increasingly using AI coding agents, I'd look beyond whether it can send requests.

I'd ask:

Can an agent actually operate it?

The commands should be clear enough for an agent to discover and use.

Structured output is particularly useful because the agent can parse results without relying on human-friendly formatting.

Apidog CLI, for example, returns structured JSON and provides --help for command discovery. Its documentation also describes agentHints in command output to guide the next action.

Can it run repeatable tests?

An agent needs to be able to run the same scenario repeatedly.

Otherwise, every debugging cycle becomes manual.

Can it work with environments?

Development, staging, and production rarely use identical configuration.

The CLI should make switching environments straightforward.

Can failures be detected programmatically?

A good CI and agent workflow needs a clear success or failure signal.

Apidog CLI returns a non-zero exit code when tests fail, allowing CI systems to use the result as a quality gate.

Can it generate useful reports?

Terminal output is useful during development.

CI systems often need something more structured.

JUnit, JSON, and HTML reports can make the same test results useful in different environments.

Can it work without a GUI?

This sounds obvious for a CLI, but it's worth checking.

The entire point is to remove the dependency on someone sitting in front of the desktop application.

The CLI should be able to authenticate, run, report, and exit cleanly inside a CI runner or agent environment.

The Agent Changes the API Testing Loop

The biggest shift isn't really about replacing Postman.

It's about changing the feedback loop.

A traditional workflow might look like this:

Developer → API client → test → inspect result → developer

An agent-driven workflow can look more like:

Agent → API CLI → test → result → Agent → code change → API CLI → test

That second loop can happen repeatedly without requiring a human to manually operate the testing interface.

The human still defines the requirements and reviews important changes.

But the mechanical feedback loop can happen much faster.

This is why I think terminal-based API tooling is becoming more important.

The terminal isn't just where developers happen to work.

It's increasingly where automation and AI agents work too.

When a Traditional API Client Is Still the Right Choice

I don't think graphical API clients are going away.

There are plenty of situations where a GUI is simply better.

If I'm exploring an unfamiliar API, visually inspecting requests, debugging an authentication flow, or designing a test scenario for the first time, a graphical interface can save time.

The mistake is assuming that the GUI and terminal have to compete.

A good workflow can use both.

For example, I might visually create and debug a test scenario, then run the same scenario from the terminal every time code changes.

That's one of the reasons the Apidog CLI approach is interesting.

The test scenario can be authored visually, while the CLI handles headless execution. Apidog's documentation explicitly supports this workflow and its CI/CD integration.

The GUI becomes the authoring environment.

The terminal becomes the execution environment.

And an AI agent can participate in the latter.

Final Thoughts

The interesting thing about API tooling right now isn't that developers suddenly don't need Postman.

It's that the terminal is becoming a much more important part of the API development workflow.

Developers work there.

CI/CD works there.

Automation works there.

And AI coding agents work there.

That means API tools need to be accessible from the same environment.

For a quick request, curl or HTTPie may be all you need.

For file-based API testing, Hurl or Bruno can make sense.

For schema-driven testing, Schemathesis is an interesting option.

If you already have a Postman workflow, Postman CLI provides a straightforward path into the terminal.

And if you want something that brings a broader API workspace into the command line, Apidog CLI is worth looking at.

What matters most is not whether a tool calls itself an AI-native API client.

It's whether it can become part of the workflow where the code, tests, automation, and AI agents are already operating.

Because the future of API testing may not be GUI versus terminal.

It may simply be:

Design where it makes sense.Run where the work happens.And let humans and agents use the same API tooling.

How Humans and AI Agents Can Work Together: A Practical Guide to Agent-Based Project Management

Emmanuel Mumba — Tue, 15 Sep 2026 08:49:52 +0000

AI coding agents have changed the way I think about software development.

Not long ago, when I thought about using AI for development, I mostly thought about asking a model a question, getting an answer, copying some code, and then continuing the work myself.

That model is changing quickly.

Today, an AI agent can be given a task, inspect a repository, modify files, run tools, investigate errors, and continue working toward an outcome.

But that creates a new question.

What happens when the AI isn't just an assistant, but becomes part of the team?

If one developer starts an agent on a task, can another developer add context?

Can someone redirect the agent while it is working?

Can another person review the result and continue the task?

Can the work be handed from one person to an agent and back again without losing the context?

These questions are becoming increasingly important as development teams start running multiple AI agents alongside human developers.

The challenge isn't simply getting an AI agent to do work.

It's figuring out how humans and agents can work together without creating another layer of disconnected conversations, prompts, terminals, and status updates.

What Does Human-Agent Collaboration Actually Mean?

I think there is an important distinction between using AI and working with an AI agent.

When I ask ChatGPT to explain an API, that's AI assistance.

When I ask an AI coding tool to suggest a function, that's also assistance.

But an agent can operate differently.

Instead of waiting for me to provide every individual instruction, an agent can receive a broader objective and execute multiple steps toward it.

That changes the workflow.

A developer might create a task such as:

Add authentication to the new API endpoints, update the tests, and make sure existing endpoints continue working.

The agent can then inspect the repository, make changes, run tests, and report what happened.

But the developer doesn't necessarily disappear from the process.

They may realize halfway through that the authentication requirement needs to work differently.

A second developer may notice an edge case.

A reviewer may ask for additional tests.

The product manager may change an acceptance requirement.

The agent needs to respond to those changes.

That's what I mean by human-agent collaboration.

The human isn't simply giving the agent one prompt and waiting for the final answer.

The human and the agent are participating in an ongoing piece of work.

Can Multiple People Join the Same Live AI-Agent Session?

This is one of the most interesting questions around agent-based development.

Imagine that a developer starts an AI agent to work on a feature.

The agent gets halfway through the implementation.

Another developer looks at the task and realizes that the original approach won't work because of a constraint in another part of the system.

In a traditional workflow, that second developer might message the first developer, who then returns to their terminal and tells the agent what to do.

That's a lot of unnecessary handoff.

A better model is to make the task itself the shared context.

Instead of treating the agent session as someone's private conversation, the team can keep the requirement, discussion, execution history, and review connected to the same piece of work.

This is one of the core ideas behind Sharkly.

Sharkly describes itself as a work management system for People and Agents, with requirements, task state, discussion, execution records, and review kept together rather than scattered across private prompts and terminal sessions.

That doesn't mean every person is necessarily typing into the exact same chat window at the same moment.

In fact, Sharkly's documentation makes an important distinction between standalone Agent Chat and the collaboration happening around a Task.

Standalone Agent Chat belongs to the person who created that session. The task-based workflow is different: the Task becomes the shared record where people can collaborate around the work.

That distinction matters.

The goal isn't necessarily "put everyone into one giant chat."

The goal is:

Keep everyone working from the same source of truth.

How Humans and Agents Could Work Together

A realistic development workflow might look something like this:

Product requirement → Human planning → Agent execution → Human feedback → Agent adjustment → Human review → Completion

Let's make that more concrete.

Step 1: A person defines the task

A developer or product manager creates a task describing what needs to be done.

The task can contain the goal, description, acceptance criteria, priority, status, and other project information.

In Sharkly, a Task can also have a person responsible for the work and an Agent or Crew assigned to execute it.

Step 2: The agent starts working

The agent receives the task context and begins execution.

A task-backed run can include the task description, current status, project and sprint information, recent comments, agent instructions, skills, repositories, environment configuration, and runtime settings.

This is important because the agent isn't operating with just a single isolated sentence.

It has structured context around the work.

Step 3: A human adds context

While the work is progressing, a developer might realize something important:

"Don't modify the legacy authentication middleware. The mobile client still depends on it."

That information needs to become part of the work.

In Sharkly, task comments are designed for exactly this kind of discussion. Comments can contain new requirements, constraints, review findings, decisions, files, screenshots, or instructions for the task's Agent Assignee.

Step 4: The agent continues

The comment can trigger a follow-up run for the Agent assigned to the Task.

Sharkly's execution model allows eligible member comments to enqueue follow-up work, including while another run is active. The system can coalesce pending work so rapid comments don't create duplicate pending runs for the same Agent and Task.

This is a very different model from simply starting a new AI conversation every time requirements change.

Step 5: Another human reviews the result

The agent finishes.

Now someone needs to look at what happened.

Was the implementation correct?

Did the tests pass?

Did the agent misunderstand the requirement?

Is there something that needs to be changed?

The agent can leave a result as a Task comment linked to its execution, while the execution log provides additional information about the run.

The human then becomes the reviewer rather than just the person waiting for AI output.

Why Context Is Becoming a Project Management Problem

This is probably the biggest change AI agents introduce into development workflows.

We've always needed project management because software development involves multiple people, tasks, dependencies, priorities, and decisions.

Agents add another problem:

context continuity.

An AI agent can produce good work and still fail because it didn't have the right context.

Maybe the requirement changed.

Maybe another developer made a related architectural decision.

Maybe an edge case was discovered.

Maybe a previous attempt failed.

If that information exists only inside someone's private chat or terminal session, the rest of the team may not know about it.

That's where project management starts becoming more than a list of tasks.

The task can become the shared memory of the work.

In Sharkly, the Task holds the goal, state, assignee, comments, attachments, activity, and agent execution information.

That gives people and agents somewhere to return to.

What Should a Project Management Tool for Agents Actually Do?

I think this is where the definition of a project management tool for agents starts to differ from traditional project management software.

It doesn't necessarily need to replace everything a Jira-style tool does.

But it needs to understand that an agent is capable of actually executing work.

Here are some capabilities I would look for.

1. Assign work to both humans and agents

A task shouldn't have to assume that the executor is always a person.

The system should be able to represent human ownership and agent execution clearly.

Sharkly's task model supports People, Agents, and Crews as assignees.

2. Preserve context

The agent needs access to the relevant task information rather than receiving a disconnected prompt every time.

That includes requirements, comments, repositories, skills, and other execution configuration.

3. Keep execution history

If an agent makes changes, the team should be able to understand what happened.

Sharkly separates Task workflow status from Agent run state and maintains execution information such as queued, running, completed, failed, and canceled states.

4. Allow humans to intervene

Agents shouldn't operate as black boxes.

Humans should be able to add context, answer questions, provide corrections, review results, stop a run, retry work, or reassign a task.

Sharkly's workflow supports follow-up comments, human attention states, cancellation, retry, and reassignment.

5. Make handoffs possible

The person who starts a task shouldn't necessarily be the person who finishes it.

And the agent that starts the work might not be the agent that handles the next stage.

A project management system should make those transitions visible rather than hiding them inside private conversations.

From One Agent to Agent Teams

The next step becomes even more interesting when you stop thinking about one agent.

Imagine a software project where different agents have different responsibilities.

One agent handles implementation.

Another specializes in testing.

Another handles documentation.

Another investigates production issues.

Now the question becomes:

How do you coordinate all of them?

Sharkly uses the concept of a Crew for this.

A Crew combines a leader Agent with other Agents and People so that a complex Task can be assigned to a reusable team rather than a single executor.

This starts to look less like "AI autocomplete" and more like a development team with specialized capabilities.

For example:

Feature request

↓

Planning

↓

Implementation Agent

↓

Testing Agent

↓

Review

↓

Documentation Agent

↓

Human approval

The human doesn't necessarily need to manually orchestrate every transition.

The important thing is that the work remains connected.

The Agent Isn't the Project Manager

There's another distinction I think is important.

An AI agent performing a task doesn't mean the agent should become the manager of the entire project.

There is still a human responsibility for deciding:

What should be built?
Why are we building it?
What constraints matter?
What should be prioritized?
What is acceptable?
When is the work ready to ship?

The agent can execute.

The project management layer can coordinate.

The human can make the important decisions.

That separation is actually one of the reasons I think agent-based project management makes sense.

We're not trying to make the AI replace the team.

We're trying to make the team capable of working with more capable AI systems.

A Practical Human + Agent Workflow

Let's say a team wants to add a new payment method to its application.

Here's how I could see the workflow operating.

1. Create the requirement

A developer creates a task describing the payment integration and its acceptance criteria.

2. Assign the implementation

The implementation task is assigned to an appropriate coding agent.

3. The agent investigates

The agent examines the repository and determines how the existing payment architecture works.

4. The agent implements

It makes the required changes and runs the relevant tests.

5. A developer intervenes

The developer notices that the implementation doesn't account for an existing mobile workflow.

They add that information to the task.

6. The agent adjusts

The agent receives the follow-up context and continues the work.

7. QA reviews

Another person checks the implementation and asks for additional test coverage.

8. The agent handles the follow-up

The agent runs another execution based on the new requirement.

9. Human approval

A developer reviews the final result and decides whether it is ready to merge.

Notice what happened.

There wasn't a single giant prompt.

There wasn't a completely autonomous AI process.

And there wasn't a person manually copying every piece of context between tools.

Instead, the work moved through a shared task.

That's the model I find most interesting.

Turning Agent Work Into Team Work

Once AI agents start handling real development tasks, the conversation becomes bigger than the agent itself.

You need somewhere to keep the requirement, the context around it, the conversation, the execution history, and the eventual result. Otherwise, the work can easily become tied to one person's terminal or one private AI session.

This is where Sharkly.ai takes an interesting approach.

Instead of treating the agent session as the center of everything, the Task becomes the shared workspace around the work. People can contribute context and decisions, while the assigned agent handles the execution.

The different parts of the workflow have their own roles. An Agent defines how a particular type of work should be handled. A Computer provides the environment in which that work can run. The Runtime handles the actual execution. The Task then brings the work back into a shared context that the rest of the team can follow.

That separation is useful because the agent doesn't have to be rebuilt from scratch for every task.

A team might have one agent configured for backend development, another for testing, and another for documentation. Each can have its own instructions, Skills, repositories, environment, and Runtime configuration, while individual Tasks provide the specific requirements and context for what needs to be done.

That creates a different workflow from simply opening another AI chat whenever there's work to do.

The agent becomes a reusable capability.

The Task becomes the piece of work.

And the people on the team remain part of the process.

For teams experimenting with multiple agents, that distinction can become increasingly important. The challenge isn't just getting an agent to complete a task. It's making sure the work it does can be understood, reviewed, redirected, and continued by the rest of the team.

What Happens When the Agent Gets Stuck?

This is another place where human-agent collaboration matters.

Agents will still get stuck.

They can encounter a failing test, missing credentials, an unclear requirement, a dependency problem, or a situation where a human decision is required.

A useful system shouldn't hide that.

It should surface the need for human attention.

In Sharkly, an Agent can leave a Task waiting for a human reply or review. Failed or blocked work can also surface as an attention item in Inbox, where the responsible person can open the Task, respond, review the result, retry, or decide whether to reassign the work.

That's an important part of the workflow.

Autonomy doesn't mean humans disappear.

It means humans spend their attention where it is actually needed.

The Shift From AI Assistant to AI Teammate

I think we're approaching a point where calling every AI development tool an "assistant" becomes less useful.

An assistant traditionally waits for you.

An agent can take responsibility for executing a defined piece of work.

That creates a completely different collaboration model.

The team may eventually have:

Human developers

AI coding agents

Testing agents

Research agents

Documentation agents

Operations agents

And all of them may contribute to the same product.

The difficult part won't necessarily be finding an agent capable of writing code.

There are already many capable options.

The difficult part will be coordinating all that work.

Who assigned the task?

What context did the agent receive?

What did it change?

What happened during execution?

What does the next person need to know?

What requires review?

What should happen next?

Those are project management questions.

Final Thoughts

The interesting question about AI agents isn't simply whether they can write more code.

It's whether our development workflows are ready for them to become actual participants in the work.

If an agent can investigate a problem, modify a repository, run tests, and continue working across multiple steps, then treating it like a simple chatbot starts to feel limiting.

The better model is collaboration.

A person defines the goal.

An agent executes.

Another person adds context.

The agent adapts.

A reviewer checks the result.

Another agent can take over the next stage.

And the team remains connected to the entire process.

That's why I think project management for agents will become its own important category.

The goal isn't to remove humans from software development.

It's to create a workflow where humans and agents can work on the same problems without losing context every time responsibility changes.

AI can do more of the work now.

The next challenge is figuring out how we work together while it does.

7 Free Jira Alternatives for Software Teams in the Agent Era

Emmanuel Mumba — Thu, 10 Sep 2026 09:15:24 +0000

I’ve used enough project management tools to know that the hardest part usually isn’t creating a board.

It’s keeping the board useful once the project gets busy.

Tasks multiply, priorities change, developers open issues, product requirements evolve, and eventually the project management tool becomes another thing the team has to maintain.

Jira has been around this problem for a long time. It gives software teams a structured way to manage issues, sprints, backlogs, workflows, and releases.

But software development is changing.

AI coding agents can now take a task, inspect a codebase, make changes, run tests, investigate failures, and report back. That means the person doing the work is no longer always a person.

And that changes what I look for in a project management tool.

A good Jira alternative today shouldn't just help me track what humans are doing. It should fit into a workflow where humans and AI agents may both be contributing to the project.

The good news is that teams don't necessarily need to spend a lot of money to get started.

There are several free and open-source Jira alternatives worth considering, although "free" means something different for each one. Some have free cloud plans, while others are free because you can self-host the open-source edition.

Here are seven worth looking at in 2026.

What Makes a Good Jira Alternative in the Agent Era?

Before looking at individual tools, I think it's worth changing the way we evaluate them.

A traditional project management tool needs to answer basic questions:

What needs to be done?
Who is responsible?
What is the priority?
What's in progress?
What has been completed?
What is blocking the team?

With AI agents involved, there are additional questions.

Can an agent receive a clearly defined task?

Can the task contain enough context for the agent to actually execute it?

Can humans see what the agent is working on?

Can the results of an agent's work be reviewed before something is merged or released?

And can the project management system remain useful when several agents are working in parallel?

Not every Jira alternative needs to solve all of these problems. In fact, most traditional project management platforms don't.

That's okay.

The important thing is understanding where each tool fits.

1. Plane

Plane is one of the more interesting options if you're looking for a modern Jira alternative without giving up the structure that software teams expect from a project management platform.

It is open source, can be self-hosted, and also provides a free cloud tier. Its current Community Edition is available under the AGPL-3.0 license, with unlimited users and projects for the self-hosted version.

Best for

Software teams that want a modern project management experience with the option to self-host.

What you get

Plane brings together projects, work items, cycles, modules, pages, dashboards, estimates, and multiple ways of viewing work.

The free Community Edition includes things like work items, cycles, modules, pages, dashboards, REST APIs, webhooks, and several project views.

That makes it considerably more than a simple Kanban board.

How it fits the agent era

The interesting part isn't necessarily that Plane is an AI-agent orchestration platform. It isn't.

Instead, I see it as a solid control layer for the work being handed to developers and agents.

For example, a team could create a work item describing a new API endpoint, attach acceptance criteria and technical context, and then use an AI coding agent to handle the implementation.

Plane remains responsible for organizing the work.

The coding agent remains responsible for execution.

That separation can actually be useful.

A realistic scenario

Imagine a small SaaS team preparing a new billing feature.

The product manager creates the work items in Plane. A developer breaks the feature into smaller tasks. One task is assigned to an AI coding agent to implement the API changes.

The agent completes the implementation and reports the results.

The developer reviews the code and moves the work item forward.

Plane doesn't have to become the AI itself. It simply needs to remain the place where everyone understands what is happening.

2. OpenProject

OpenProject takes a different approach.

Instead of trying to make project management feel lightweight, it provides a much broader project management platform, particularly for teams that want an open-source and self-managed solution.

Its Community Edition is free, open source, self-hosted, and available without a license fee. OpenProject says the Community Edition supports unlimited users and projects.

Best for

Teams that want a comprehensive open-source project management platform and are comfortable managing their own infrastructure.

What you get

OpenProject covers work packages, tasks and subtasks, milestones, dependencies, project timelines, dashboards, project planning, and other traditional project management capabilities.

You can install it on your own infrastructure using supported Linux packages, Docker, Kubernetes, and other deployment options.

The important distinction here is that the free version is primarily a self-hosted option. If you want OpenProject to host it for you, you're looking at the paid cloud offerings rather than the free Community Edition.

How it fits the agent era

OpenProject makes sense when AI agents are simply another source of work within a broader project.

For example, an engineering team might have a large migration project with dozens of dependencies.

Some work is performed by humans.

Some repetitive implementation work is delegated to agents.

The project manager still needs the overall timeline, dependencies, milestones, and project status.

That's where a more traditional platform like OpenProject can still be valuable.

A realistic scenario

A company is migrating a monolithic application into several services.

The migration has architecture tasks, infrastructure tasks, testing tasks, documentation, security reviews, and deployment milestones.

AI agents can help implement individual changes, generate tests, or update documentation.

But someone still needs to manage the migration as a project.

OpenProject is much better suited to that broader planning problem than a simple issue board.

3. YouTrack

If you're already familiar with the JetBrains ecosystem, YouTrack is one of the Jira alternatives I would put on the shortlist.

The current YouTrack free plan supports teams of up to 10 users, with the full feature set available except for custom logos. It also includes AI Assistant capabilities through free AI credits.

Best for

Small software teams that want a feature-rich issue and project management platform without paying for a small team.

What you get

YouTrack combines issue tracking, agile boards, project management, knowledge management, reporting, and helpdesk capabilities.

For a small development team, the free limit of 10 users is particularly interesting because it isn't just a restricted demo version. JetBrains describes the free plan as providing the full functionality available with paid subscriptions, apart from the custom logo feature.

How it fits the agent era

YouTrack is interesting because AI isn't treated purely as something outside the project management system.

Its AI Assistant can help with things such as creating tasks, summarizing content, and drafting replies. JetBrains has also added ways to connect external AI tools, IDEs, and agents through its remote MCP server.

That makes YouTrack a more interesting option for teams thinking about how AI should interact with project context.

A realistic scenario

A five-person development team manages its backlog in YouTrack.

A developer uses an AI coding agent to investigate a bug.

Instead of treating the agent's work as a completely separate activity, the team can keep the original issue as the source of truth.

The agent helps with execution.

The issue remains the place where the team tracks the problem, requirements, status, and final resolution.

That's a relatively natural transition from traditional issue tracking toward AI-assisted development.

4. Sharkly

Sharkly is the unusual entry on this list because I wouldn't describe it as simply another Jira replacement.

The problem it is designed around is different.

Instead of only organizing human work, Sharkly is built around coordinating humans and AI coding agents in the same work system.

Its current free offering is positioned as a multiplayer workspace for humans and agents, with support for assigning work to agents, running multiple agents in parallel, and keeping context, progress, blockers, results, and human review visible throughout the workflow.

Best for

Development teams that are already using AI coding agents and need a way to organize and coordinate that work.

What you get

The idea is to bring tasks, projects, agents, execution environments, shared context, and human review into one workflow.

Instead of replacing tools such as Claude Code, Codex, or Gemini, Sharkly sits around them.

The agents still perform the actual development work.

Sharkly provides the coordination layer.

How it fits the agent era

This is where I think the difference becomes important.

Imagine assigning three developers three tasks.

That's a familiar project management workflow.

Now imagine assigning three AI agents three different implementation tasks.

The problem isn't only tracking the tasks anymore.

You also need to know:

Which agent is working on which task?
What context did it receive?
What has it already done?
Did it encounter a blocker?
What were the results?
What needs human review?
Can another agent work on something else in parallel?

That's a different coordination problem.

Sharkly is built specifically around that kind of workflow.

A realistic scenario

A development team has a product release coming up.

One agent handles a backend implementation.

Another works on automated UI testing.

A third prepares documentation and changelog updates.

The developers aren't sitting around watching each agent.

They need visibility into what each one is doing, the results it produces, and what requires human approval.

That's where an agent-oriented workspace can become more useful than simply adding an "AI" button to a traditional project management system.

5. Trello

Sometimes the best Jira alternative is simply the one that doesn't try to be Jira.

Trello is a good example.

Its free plan currently supports up to 10 collaborators per Workspace, with unlimited cards, up to 10 boards, unlimited Power-Ups per board, assignees, due dates, automation capabilities, and other basic collaboration features.

Best for

Small teams and projects that don't need a complicated issue-tracking system.

What you get

Trello's core model is straightforward:

Boards contain lists.

Lists contain cards.

Cards represent work.

That simplicity can be a feature.

A small software team doesn't always need complicated workflows, dozens of custom fields, or a large backlog hierarchy.

Sometimes it just needs to know what needs to be done and who's doing it.

How it fits the agent era

Trello can work well when AI agents are treated as another participant in a lightweight workflow.

For example:

Backlog → Ready → Agent Working → Human Review → Done

An agent can work on cards just like a human developer.

The limitation is that Trello itself isn't designed primarily around managing autonomous coding agents.

But if the team's agent workflow is relatively simple, that may not matter.

A realistic scenario

A two-person startup is building an MVP.

They have a developer, a designer, and occasionally an AI coding agent helping with implementation.

They don't need a heavyweight project management system.

A Trello board gives everyone a shared view of the work without creating another complicated process to maintain.

Sometimes that's exactly what a team needs.

6. Taiga

Taiga is another open-source option worth considering, particularly for teams that prefer agile methodologies.

It provides Kanban and Scrum workflows and is positioned as a free and open-source project management platform.

There is an important detail with its hosted offering, though.

Taiga currently has a free cloud tier with one public project and one private project, up to 10 MB of storage, and unlimited users. There are also paid hosted tiers. Self-hosting remains an option for teams that want to run the open-source software themselves.

Best for

Agile software teams that want an open-source alternative with Scrum and Kanban workflows.

What you get

Taiga provides backlog and sprint planning, user stories, epics, tasks, Kanban boards, Scrum boards, estimation tools, WIP limits, filters, and other agile features.

It also provides a REST API, webhooks, importers, and integrations with development platforms.

How it fits the agent era

Taiga's value isn't that it suddenly becomes an AI-agent platform.

Instead, it gives teams a structured agile environment where agent-generated work can be tracked alongside human work.

That distinction is important.

The project management system doesn't necessarily have to execute AI work.

It needs to make the resulting work visible and manageable.

A realistic scenario

A Scrum team has a two-week sprint.

The team identifies several small implementation tasks that are suitable for AI coding agents.

Those tasks can enter the sprint backlog alongside normal developer work.

An agent completes one task, the developer reviews the resulting pull request, and the corresponding work item moves forward.

The sprint structure remains familiar even though some of the execution has changed.

7. Another Way to Think About Free Jira Alternatives

The interesting thing about the current Jira alternative landscape is that these tools aren't all competing in exactly the same way.

Some are trying to replace Jira directly.

Others are trying to simplify project management.

Some focus heavily on open source and self-hosting.

And newer tools are beginning to question whether project management should be designed around humans alone.

That's why I don't think there is one universally best free Jira alternative.

The right choice depends on what your team actually needs.

If You Want a Modern Jira-Style Platform

Plane is probably one of the first options I'd investigate.

It has the familiar concepts of projects, work items, cycles, modules, and views, while also giving teams the option to self-host.

If You Want Open Source and Full Control

OpenProject is particularly interesting.

The Community Edition is free, open source, self-hosted, and supports unlimited users and projects.

Taiga is another option if your team is more focused on Scrum and Kanban workflows.

If You Have a Small Development Team

YouTrack is worth a serious look if you're under the 10-user free limit.

The fact that its free plan includes the full feature set makes it especially compelling for small teams.

Trello is another good choice when simplicity matters more than deep software-specific functionality.

If AI Agents Are Becoming Part of the Team

This is where the decision gets more interesting.

Traditional project management tools can still work.

But if you're running multiple coding agents, assigning them tasks, managing their execution environments, reviewing their results, and coordinating several pieces of work simultaneously, you may eventually need more than a traditional task board.

That's the problem space Sharkly is targeting.

How AI Agents Are Changing Project Management

The biggest change isn't that AI can now create code.

We've already established that.

The bigger change is that the unit of work is changing.

For years, project management systems were largely built around a human receiving a task and completing it.

Now a task might be:

Human → AI agent → human review

Or:

Product requirement → research agent → coding agent → testing agent → human approval

Or even:

Large feature → multiple agents working in parallel → integration → human review

That creates a much more complicated workflow.

A project manager may eventually need to think about agent availability alongside developer availability.

A task may need machine-readable acceptance criteria.

Execution history may become just as important as task status.

Context management may become part of project management.

And human review becomes an explicit stage rather than an informal part of the process.

That doesn't mean every team needs an AI-native project management platform today.

It does mean the tools we choose now should not make these workflows unnecessarily difficult later.

A Practical Human + Agent Workflow

For me, the most realistic workflow isn't one where AI agents completely replace project management.

It's one where project management becomes the coordination layer between humans and machines.

A simple workflow might look like this:

1. Define the requirement

A human defines what needs to be built and why.

2. Break it into tasks

The feature gets divided into implementation, testing, documentation, infrastructure, and review tasks.

3. Decide what should be delegated

Some tasks remain with developers.

Others are suitable for AI agents.

4. Give the agent enough context

The agent needs the relevant requirements, constraints, files, APIs, acceptance criteria, and expected behavior.

5. Let the agent execute

The agent writes code, runs tests, investigates failures, or performs another assigned task.

6. Review the result

A human developer reviews the output.

This step matters even more as agents become capable of making larger changes.

7. Update the project

The task is updated with the result, remaining issues, and next steps.

8. Continue the workflow

Another developer or agent can pick up the next dependency.

The important thing is that the project management tool remains the shared source of truth.

Free Doesn't Always Mean the Same Thing

One thing I would pay close attention to when comparing these tools is what "free" actually means.

There are at least three different models here.

Free cloud tier: You can sign up and use the hosted service without paying, but there are limits.

Free open-source edition: You can use the software without licensing fees, but you may need to host and maintain it yourself.

Free for small teams: The product may provide a complete experience up to a certain number of users.

Those are very different propositions.

For example, Plane offers a free cloud tier as well as a free open-source Community Edition.

OpenProject's free Community Edition is self-managed, while its hosted options are paid.

YouTrack is free for teams of up to 10 users.

Trello's current free plan supports up to 10 collaborators per Workspace.

Taiga has a free hosted tier with limitations, while its open-source software can also be self-hosted.

So before migrating from Jira, I'd look beyond the headline price.

Ask what happens when the team grows.

Ask whether you need hosting.

Ask how much administrative work self-hosting creates.

And, increasingly, ask how well the platform fits the way your team uses AI.

Final Thoughts

Jira isn't suddenly irrelevant because AI coding agents exist.

For many teams, it still does the job.

But the way software teams work is changing, and that makes the Jira alternative conversation more interesting than simply comparing issue trackers.

A small team may be perfectly happy with Trello.

An agile team might prefer Taiga.

A team that wants open-source project management could look at Plane or OpenProject.

A small development team might find YouTrack's free plan more than sufficient.

And a team building an increasingly agent-driven development workflow may start looking for something designed around humans and AI agents working together.

The important thing is not choosing the tool with the longest feature list.

It's choosing the one that matches how your team actually works.

Because in the agent era, project management is no longer just about tracking what developers are doing.

It's increasingly about coordinating who — or what — is doing the work, understanding the result, and keeping humans in control of the final decision.

7 Collaboration Platforms for Humans and AI Agents in 2026

Emmanuel Mumba — Mon, 07 Sep 2026 11:29:12 +0000

I've noticed a shift in how software development teams work over the last couple of years.

AI used to be something you opened when you needed help.

You'd ask it to explain an error, generate a function, write a test, or maybe help you understand an unfamiliar codebase.

Now, that's changing.

AI coding agents can work through repositories, modify files, run commands, execute tests, investigate bugs, and complete much larger development tasks with less direct intervention.

That creates an interesting problem.

If AI can actually do work, how do we manage that work?

A normal collaboration platform assumes that the people on the project are... well, people.

You assign a task to a developer. They work on it. They leave a comment. They open a pull request. Someone reviews it.

But what happens when one of those "workers" is an AI agent?

Do you just paste its output into a Jira ticket?

Do you keep everything inside a terminal session?

Or do we need collaboration platforms designed around humans and agents working together?

I think we're going to see more of the latter.

In this article, I'll look at seven platforms that can support modern software collaboration, from established developer platforms to newer tools built around AI-assisted workflows.

What Does Human-Agent Collaboration Actually Mean?

Before looking at the tools, I think it's worth clarifying what we're talking about.

Human-agent collaboration isn't simply using ChatGPT to write a message or asking an AI assistant to summarize a meeting.

The more interesting scenario is when an AI agent becomes an active participant in the development workflow.

For example, imagine a team working on a web application.

A developer creates a task:

Add password reset functionality.

An AI agent could potentially:

Inspect the existing authentication code
Identify the relevant files
Implement the changes
Add tests
Run the test suite
Report what it changed
Flag anything it couldn't complete

The developer still decides whether the implementation is correct.

That's an important distinction.

The goal isn't necessarily to replace the developer.

It's to create a workflow where humans provide direction and judgment while agents handle parts of the execution.

That requires collaboration tools to evolve.

What Should You Look for in a Collaboration Platform?

Not every team needs an AI-native platform.

But if you're introducing AI agents into development, there are a few things I'd pay attention to.

Context

Can the people and agents working on a task access the information they actually need?

Task Management

Can work be broken down into clear, trackable pieces rather than living inside conversations?

Development Integration

Can the platform connect with repositories, issues, pull requests, CI/CD, and other development tools?

AI Support

Does AI simply assist with administrative tasks, or can agents actually participate in execution?

Human Review

Can developers see what happened and review the result before changes are accepted?

Collaboration History

Can the team understand why something was done, what changed, and what happened along the way?

With that in mind, let's look at the platforms.

1. GitHub — Collaboration Around the Codebase

Best for: Development teams that want collaboration closely connected to source code.

GitHub is already a central collaboration platform for millions of developers.

Its strength is that conversations, issues, pull requests, code reviews, repositories, and automation can all live close to the code.

Key features

Git repositories
Issues
Pull requests
Code reviews
Discussions
GitHub Actions
Project management
AI-assisted development through GitHub Copilot

How AI fits into the workflow

GitHub's AI ecosystem makes it possible for developers to use AI throughout the software development process.

An AI coding assistant can help generate or modify code, while GitHub remains the place where the resulting changes can be reviewed, discussed, and merged.

This creates a useful separation:

AI helps produce the work. GitHub provides the collaboration infrastructure around the work.

Real-world scenario

A developer could create an issue for a bug, use an AI coding tool to investigate and implement a fix, open a pull request, and have other developers review the resulting changes.

For teams already using GitHub, this workflow requires very little context switching.

2. Jira — Structured Collaboration for Software Teams

Best for: Large engineering organizations with established development processes.

Jira has been a standard project and issue-management platform for software teams for years.

Its biggest advantage isn't necessarily simplicity.

It's structure.

Teams can build workflows around issues, sprints, backlogs, releases, dependencies, and approvals.

Key features

Issue tracking
Scrum and Kanban boards
Backlogs
Sprints
Roadmaps
Workflow automation
Reporting
Integrations with development tools
AI-assisted features

How AI fits into the workflow

AI can help teams summarize issues, generate content, organize information, and reduce repetitive project-management work.

The more interesting question is how platforms like Jira will evolve as AI agents begin performing actual development tasks.

Real-world scenario

Consider a large company with several engineering teams.

A product requirement becomes an Epic, which gets broken into stories and tasks. Developers and potentially AI agents can then work on individual pieces while Jira remains the central record of project progress.

For organizations already heavily invested in Jira, adding AI capabilities to an existing workflow may be more practical than migrating everything to a new platform.

3. Sharkly — Collaboration Between Humans and AI Agents

Best for: Teams that want AI agents to become active participants in their development workflow.

Sharkly takes a different approach to collaboration.

Rather than treating AI purely as an assistant inside an existing project-management application, Sharkly is designed around a shared work system where people and AI agents can work together.

The distinction becomes important when agents start doing more than answering questions.

If an agent is researching a feature, modifying code, running tests, or handling another development task, the team needs somewhere to manage that work.

Key features

Task-based work management
AI agent task assignment
Multiple agents working on tasks
Connected Computers and AI coding runtimes
Shared project context
Reusable Agents and Skills
Projects and Sprints
Agent execution history
Human review and acceptance
Integrations with existing workflows

How AI fits into the workflow

The basic idea is to connect the planning side of development with the execution side.

A task can contain the requirements and context an agent needs. The agent can then perform the assigned work, return results, and leave the human team with something that can be reviewed.

That creates a workflow closer to:

Task → Agent execution → Results → Review → Acceptance

rather than:

Prompt → AI response → Copy and paste

Real-world scenario

Imagine a team building an e-commerce application.

The team could create separate tasks for:

Investigating a checkout bug
Updating the frontend
Adding API tests
Writing documentation
Refactoring a service

Different agents could work on different tasks while developers retain visibility over the work and review the results.

That's where the idea of treating an AI agent as a team participant becomes much more interesting.

The agent isn't replacing the team.

It's becoming another part of the team's workflow.

4. Linear — Lightweight Collaboration for Product Teams

Best for: Fast-moving software teams that want simple issue and project management.

Linear has become popular among modern product and engineering teams because it keeps project management relatively lightweight.

Instead of making teams navigate a huge hierarchy of project-management features, Linear focuses heavily on issues, projects, cycles, and roadmaps.

Key features

Issue tracking
Projects
Cycles
Roadmaps
Team workflows
GitHub integration
Automations
AI-assisted workflows

How AI fits into the workflow

AI can help teams create, organize, summarize, and work with project information.

This can reduce the administrative work surrounding development without forcing teams to completely change how they track projects.

Real-world scenario

A startup building a mobile application might organize its work into weekly cycles.

Developers create issues for features and bugs, connect their work to GitHub, and use AI to reduce repetitive project-management tasks.

For a small team, that simplicity can be more valuable than having hundreds of configuration options.

5. GitLab — Collaboration Across the DevOps Lifecycle

Best for: Teams looking for development, collaboration, and DevOps workflows in one platform.

GitLab goes beyond source-code hosting.

It provides a broader DevOps platform covering planning, source control, CI/CD, security, and deployment.

Key features

Git repositories
Issues
Merge requests
Code review
CI/CD
Planning
Security scanning
Deployment tools
AI-assisted development capabilities

How AI fits into the workflow

AI can assist developers and teams across different stages of the development lifecycle.

Instead of having an AI tool completely disconnected from project management and deployment, teams can keep much of the workflow within the same platform.

Real-world scenario

A development team could create an issue, implement the change, open a merge request, run automated CI/CD pipelines, perform security checks, and deploy the application.

AI can assist with parts of this workflow while GitLab maintains the overall development record.

This makes GitLab particularly useful for teams that care about connecting collaboration with DevOps execution.

6. Notion — Knowledge and Collaboration for Development Teams

Best for: Teams that need project information, documentation, and collaboration in one flexible workspace.

Notion is not primarily a software development platform.

But that's also part of its appeal.

Many development projects involve more than tickets and code.

There are product requirements, meeting notes, technical decisions, onboarding documentation, specifications, research, and internal knowledge.

Key features

Documents
Databases
Task management
Wikis
Project planning
Team collaboration
AI-assisted writing and search

How AI fits into the workflow

Notion's AI capabilities can help teams search their knowledge, summarize information, generate content, and work with existing documentation.

Real-world scenario

A startup could maintain its product requirements, technical specifications, development roadmap, and engineering documentation in Notion while using GitHub for the actual code.

AI can then help team members find information without manually searching through dozens of pages.

For teams where knowledge sharing is as important as task tracking, this can be valuable.

7. ClickUp — Broad Collaboration With AI

Best for: Organizations managing development alongside product, marketing, operations, and other teams.

ClickUp takes a broad approach to work management.

It combines tasks, documents, goals, dashboards, automations, and collaboration features into one platform.

Key features

Task management
Documents
Goals
Dashboards
Automations
Team collaboration
AI-assisted productivity
Custom workflows

How AI fits into the workflow

AI can help teams create content, summarize information, organize tasks, and automate repetitive work.

The advantage is that AI isn't limited to engineering.

It can support different departments working on the same project.

Real-world scenario

Imagine a company launching a new web application.

Engineering has development tasks.

Design has UI work.

Marketing has launch campaigns.

Customer success has onboarding materials.

ClickUp can provide a shared environment for managing these different workflows while AI helps reduce some of the repetitive work.

The Bigger Shift: From Human Teams to Hybrid Teams

Looking at these platforms together, there's a bigger trend underneath all of this.

Traditional software teams look something like this:

Product Manager → Developer → Reviewer → Release

AI-assisted teams can look more like:

Product Manager → Developer + AI Agent → Tests → Human Review → Release

And that changes the meaning of collaboration.

Previously, collaboration meant making sure people could communicate with each other.

Now we also need to make sure that:

Agents have the right context
Agents know what they're supposed to accomplish
People can see what agents are doing
Results can be reviewed
Tasks don't overlap unnecessarily
Failures and blockers are visible
Important decisions remain under human control

That's a different problem.

And I think this is where we'll see some of the biggest changes in development tooling.

A Realistic Human + Agent Development Workflow

Let's put all of this into a practical example.

Imagine I'm working on a SaaS application and need to add a new billing feature.

Instead of immediately opening an AI coding tool and saying:

Build billing.

I'd break the work into smaller tasks.

Step 1: Define the requirement

The product team explains what the billing feature should do.

Step 2: Break it into tasks

For example:

Research existing billing architecture
Design the API changes
Build the frontend
Implement backend logic
Add tests
Update documentation

Step 3: Assign work

Some tasks might be handled by developers.

Others could be suitable for AI agents.

Step 4: Let the agent execute

An agent can inspect the relevant code, make changes, run tests, and report its results.

Step 5: Review

A developer checks the changes.

Were the requirements followed?

Do the tests actually prove the implementation works?

Did the agent modify anything it shouldn't have?

Step 6: Merge and release

Only after the appropriate review does the work become part of the production codebase.

This is where a collaboration platform becomes more than a place to store tasks.

It becomes the coordination layer between humans and automated execution.

How to Choose the Right Collaboration Platform

There's no reason every team should immediately move to an AI-native platform.

I'd start with your existing workflow.

If your work revolves around repositories

GitHub or GitLab may be the natural choice.

If you have complex enterprise processes

Jira provides extensive workflow and project-management capabilities.

If you want lightweight developer collaboration

Linear is worth considering.

If documentation and knowledge are central

Notion can be a strong addition to the development stack.

If multiple departments share the same projects

ClickUp provides broader work-management capabilities.

If AI agents are becoming actual contributors

This is where a platform like Sharkly becomes particularly interesting.

The question isn't simply:

"Which project management tool has the best AI features?"

It's:

"Which platform can help my team manage work when both humans and AI agents are doing it?"

That distinction is going to matter more as agents become capable of handling increasingly complex development tasks.

Where Collaboration Tools Are Going

I don't think we're going to stop using Slack, GitHub, Jira, Linear, or other collaboration platforms just because AI agents exist.

Instead, I think these tools will gradually have to understand a new type of participant.

Not just:

Developer

but also:

Agent

That agent might have:

A specific role
Assigned tasks
Access permissions
Skills
Project context
Execution history
Results
Limitations

And just like a human developer, its work needs to be visible to the rest of the team.

The interesting part is that this doesn't necessarily mean AI agents should be treated exactly like humans.

They have different capabilities and limitations.

But the work they perform still needs to fit into the same development process.

That's the real challenge.

Final Thoughts

AI has already changed how developers write software.

The next change may be how development teams organize and collaborate around that software.

As AI coding agents become capable of handling larger tasks, project management and collaboration platforms need to account for something that wasn't there before: non-human participants doing real development work.

Some platforms are adding AI assistants to existing workflows.

Others are connecting AI more closely to repositories and development processes.

And newer platforms such as Sharkly are exploring what it looks like when humans and AI agents share the same work environment.

I don't think we know exactly what the final model will look like yet.

But I do think we're moving toward a world where the software team isn't made up exclusively of people.

And when that happens, our collaboration tools will need to evolve with it.

The future isn't humans versus AI. It's figuring out how to build workflows where both can actually work together.