DEV Community: Emmanuel Mumba

How to Build Your Team's First Shared Agent

Emmanuel Mumba — Thu, 05 Nov 2026 06:08:26 +0000

Most teams already use AI. The problem is that everyone uses it alone.

One developer has a prompt that writes great release notes. Someone in support has a chat thread that drafts perfect replies. A PM keeps a document of instructions they paste into a chatbot every Monday. None of it is shared, none of it is consistent, and when that person goes on leave, the workflow goes with them.

A shared agent fixes that. Instead of ten private assistants, the team gets one agent with the same instructions, the same data, and the same rules, available to everyone who needs it.

In this guide, I'll walk through how to build your team's first one: how to pick the right job, set it up safely, choose a platform, and roll it out without it turning into another tool nobody uses.

Personal Agent vs Shared Agent

A shared agent is not just a personal assistant with more users. Once a whole team depends on it, the design questions change.

	Personal agent	Shared agent
Who uses it	One person	A team, channel, or department
Instructions	Lives in one person's head or chat history	Written down, versioned, owned
Data access	Whatever that person can see	Scoped to what the whole team should see
Output quality	Judged by one user	Has to be consistent for everyone
When it breaks	One person notices	The whole workflow stalls
Owner	The user	A named person responsible for upkeep

The last row matters most. A shared agent without an owner slowly drifts out of date until people stop trusting it.

Step-by-Step: Building Your First Shared Agent

1. Pick one repetitive job, not a vision

The biggest mistake is starting with "an AI assistant for the whole team." That's too vague to build and too vague to judge.

Start with one job that already happens every week and follows a pattern. Good first candidates:

Standup or weekly summaries from tickets, commits, or channel activity
Triage of incoming bugs or support requests into the right queue
First drafts of release notes, changelogs, or customer replies
Internal Q&A over your docs, runbooks, and onboarding material

A simple test: if you can't describe the job in one sentence, it's too big for a first agent.

2. Write down what "good" looks like

Before touching any tool, collect three to five real examples of the output you want. Paste in last week's best release notes or the triage decisions a senior engineer made.

These examples become your instructions and, later, your test cases. Without them, every teammate judges the agent against a different picture in their head.

3. Decide where the agent lives

Put the agent where the team already talks. If your team works in Slack, a web app nobody opens will die quietly. If work lives in Jira or Teams, start there.

This single decision usually narrows your platform options to two or three. I cover those in the next section.

4. Connect only the data it needs

A shared agent sees what you give it, and everyone who uses it can potentially surface that data. So scope carefully:

Connect the specific channels, projects, or folders the job needs
Avoid private channels, HR folders, and anything with credentials
Prefer read-only access until the agent has earned trust

When in doubt, start narrower. Adding a source later is easy. Explaining why the agent quoted someone's salary is not.

5. Write instructions like an onboarding doc

Treat the agent's instructions the way you'd brief a new hire:

Role: what it's for and who it serves
Inputs: which sources to use, and which to ignore
Output format: structure, length, tone, with an example
Boundaries: what it should never do, and when to hand off to a human

Keep the instructions in a shared, versioned place, not inside one person's account.

6. Set permissions and an owner

Decide three things explicitly:

Who can use it (a channel, a group, the whole org)
Who can edit it (usually one or two people)
Who owns it (one named person who reviews it monthly)

Most platforms separate "can use" from "can edit." Use that. A shared agent that anyone can quietly rewrite becomes unpredictable fast.

7. Pilot with a small group

Roll it out to three to five people for two weeks. Ask them to flag every bad answer, not just the dramatic ones. Feed those back into the instructions.

8. Measure something simple

You don't need a dashboard. Track one or two things:

How often people actually use it each week
How much of its output gets used with light edits versus rewritten

If usage drops after the first week, the agent isn't saving time yet. Fix that before expanding.

Where to Build It

You don't need to write an agent from scratch for your first one. Most teams already pay for a tool that can host a shared agent, and engineering teams have a dedicated option too. Pick based on where your team works, not on which model is trendiest.

If your team lives in	Option	How sharing works
Slack	Claude Tag (Claude Team and Enterprise, in beta)	One agent per channel that anyone there can @-mention; admins control which tools, data, and channels it can access
Microsoft Teams / M365	Copilot Studio	Publish the agent to Teams, then share it with teammates or submit it to your org catalog
Jira / Confluence	Atlassian Rovo agents (paid Cloud plans)	Agents built in Rovo Studio run on your team's Jira and Confluence data, metered by Rovo credits
Google Workspace	Gemini Gems	Shared like a Google Drive file; recipients can use, edit, or copy the Gem
ChatGPT Business / Enterprise	Custom GPTs	Share invite-only, by link, or with the whole workspace, with "can chat," "can view settings," or "can edit" permissions

A few notes from comparing these:

Chat-native agents (Slack, Teams) get adopted fastest because nobody has to open a new tab.
Gems and custom GPTs are the quickest to set up, but they work best for reusable instructions plus documents, not for agents that act across your tools.
Rovo and Copilot Studio go deeper into their own ecosystems, which is great if your work already lives there and limiting if it doesn't.

Sharkly: for engineering teams sharing coding agents

If your team's first shared agent is a coding agent, the platforms above only solve part of the problem. Tools like Claude Code and Codex are powerful, but they usually run on one developer's machine, in one person's session. The rest of the team can't see what the agent is working on, who assigned it, or whether anyone reviewed the result.

That's the gap Sharkly is built for. It's a shared workspace where people and AI coding agents work from the same task board.

In practice, that means a team can:

Assign tasks to coding agents the same way they assign them to teammates
Run several agents in parallel, each in its own isolated worktree
Keep context, progress, and results together in one shared workflow
Add human review before agent work is accepted
Connect Jira, so agent runs stay traceable in existing project workflows

This maps neatly onto the steps in this guide. The task board gives the agent a clear job, the workspace makes ownership visible, and the review step keeps a human in the loop.

One distinction matters: Sharkly doesn't replace your coding agent. It adds the team layer of assignment, coordination, and review on top of tools your developers already use. According to Sharkly's own guide, it's free for organizations of up to 5 people, and agents don't take up seats.

For a small engineering team, that makes it an easy way to run a first shared-agent pilot without a budget conversation.

If none of these fit, frameworks and agent SDKs let you build your own. For a first agent, though, the platform you already pay for is almost always the right call.

Common Mistakes to Avoid

Starting too broad. "Help the team with anything" produces an agent that's mediocre at everything. One job first.
Connecting everything. More data is not better data. Irrelevant sources make answers noisier and raise the risk of leaking something sensitive.
No owner. Instructions go stale, sources move, and nobody notices until the answers are wrong.
Letting everyone edit. Separate who uses the agent from who changes it.
Skipping the human check. For anything customer-facing or irreversible, the agent drafts and a person approves.
Measuring nothing. If you can't say whether it saves time, you can't justify expanding it.

Final Thoughts

Your first shared agent doesn't need to be impressive. It needs to be useful, trusted, and owned.

Pick one job your team repeats every week. Write down what good looks like. Put the agent where people already work, give it only the data it needs, and assign someone to keep it healthy.

Get that right once, and the second and third agents become much easier, because your team will already know how to brief, test, and trust them.

One job. One owner. One place. That's how teams move from personal AI to shared AI.

How to Use Google Jules in 2026: A Practical Guide Before It Winds Down

Emmanuel Mumba — Fri, 30 Oct 2026 08:18:14 +0000

Google Jules was an interesting step forward for AI-assisted software development. Instead of suggesting code while you work, it could take an assigned task, inspect a GitHub repository, create a plan, modify files, run tests, and prepare changes for review.

That asynchronous approach made it useful for developers who wanted to delegate work without constantly supervising an AI coding assistant.

However, there is an important update developers need to know before getting started.

Google announced on October 29, 2026, that Jules will wind down on December 31, 2026. Starting November 11, 2026, the service will become read-only. Existing code, branches, and pull requests will remain in GitHub, but users should export their Jules data before the service closes.

So, if you have been meaning to try Jules, this guide explains how it works, how to use it while it remains available, and what to consider next.

What is Google Jules?

Jules is Google's asynchronous AI coding agent. It connects to GitHub repositories and performs software development tasks in an isolated cloud environment.

Unlike a traditional code-completion assistant, Jules is designed to handle a task from beginning to end.

For example, you could ask it to:

Fix a bug in an existing application.
Add unit tests for a particular function.
Update outdated dependencies.
Implement a small feature.
Refactor a section of code.
Improve project documentation.

Jules uses Google's Gemini models to understand the task and work with the repository. It creates a development environment in a cloud virtual machine, examines the project, and produces changes that you can review.

The general workflow looks like this:

Connect a GitHub repository.
Describe the development task.
Review the plan generated by Jules.
Allow it to execute the task.
Inspect the changes and test results.
Create a branch and open a pull request.

The important part is that Jules works asynchronously. You can submit a task and continue working on something else while it handles the implementation.

What you need before using Jules

Before starting, make sure you have:

A Google account.
A GitHub account.
A repository containing the project you want Jules to work on.
Permission to authorize Jules to access that repository.

You should also make sure your repository is in a reasonably healthy state. A project with missing dependencies, unclear instructions, or failing tests can make it harder to evaluate the quality of an agent's changes.

Google's documentation also recommends using an AGENTS.md file to provide repository-specific instructions to coding agents.

For example, you can use this file to explain:

The project's architecture.
Which frameworks and libraries it uses.
How to install dependencies.
How to run tests.
Coding conventions.
Important restrictions when modifying the application.

This gives Jules additional context about how the project is expected to work.

Step 1: Open Google Jules and connect GitHub

Start by visiting jules.google.

Next, connect your GitHub account.

Jules needs repository access because it works directly with your existing code rather than requiring you to manually upload individual files.

During the authorization process, you can choose which repositories Jules is allowed to access.

I recommend starting with a specific repository instead of immediately granting access to everything. This gives you better control over which projects the agent can work with.

Once GitHub is connected, return to Jules.

You should now be able to select an authorized repository and choose the branch you want to use.

Step 2: Choose a repository and branch

Select the repository containing the code you want Jules to modify.

Then choose the branch you want it to use as the starting point.

For example:

Repository: my-project
Branch: main

Jules uses the selected branch as the starting point for its work.

For an actual production project, I would avoid treating the main branch as a place for unreviewed changes. Use a development branch or another appropriate starting point, then review the resulting changes before merging anything.

This makes it easier to compare the agent's work with the existing implementation and roll back changes if necessary.

Step 3: Give Jules a specific coding task

This is where the quality of your instructions becomes important.

A vague prompt such as:

"Improve my application"

does not give Jules enough direction.

It may understand the general intention, but it has to make assumptions about which parts of the application need attention and what a successful result should look like.

Instead, describe the problem, the expected behavior, and any relevant restrictions.

For example, consider this task:

Example prompt:

Add unit tests for the parseQueryString function in utils.js.

Requirements:
- Cover empty query strings.
- Test multiple query parameters.
- Test URL-encoded values.
- Test parameters without values.
- Follow the existing testing conventions in the repository.
- Do not modify the function's implementation unless a bug prevents the tests from passing.

Run the relevant test suite and report the results.

This is a much more useful instruction because it gives Jules a defined scope and measurable expectations.

Another example would be:

Investigate the login form validation in the application.

The form currently accepts an invalid email address.

Please:
1. Locate the existing validation logic.
2. Explain the cause of the problem.
3. Propose a minimal fix.
4. Add tests covering valid and invalid email addresses.
5. Avoid changing unrelated authentication functionality.

The second example also asks Jules to investigate before making changes, which is useful when you are not completely sure where the problem originates.

Step 4: Review the plan before execution

After submitting your prompt, Jules generates a plan describing how it intends to approach the task.

Do not automatically approve every plan.

Take a moment to check whether it understands the requirements and whether the proposed changes are within scope.

For example, if you ask it to add tests but its plan involves restructuring your authentication system, that is a reason to stop and clarify the task.

A useful plan should explain:

Which files it expects to modify.
What changes it intends to make.
How it will verify the implementation.
Whether additional dependencies or configuration changes are required.

If something is unclear, provide feedback before approving the plan.

This is one of the useful parts of Jules' workflow: you can review the proposed approach rather than immediately accepting a large set of code changes.

Step 5: Let Jules execute the task

Once you approve the plan, Jules starts working in its cloud environment.

It clones the repository, prepares the development environment, and makes the required changes.

Depending on the task, it can also run commands, install dependencies, and execute tests.

You do not need to keep the Jules interface open throughout the entire process. You can enable notifications to receive updates when a task completes or needs your attention.

You can also provide feedback while the task is running.

For example, you might tell Jules:

The current implementation changes more files than necessary.

Please keep the solution focused on the login validation issue
and avoid modifying the shared authentication service.

The ability to redirect an agent is important. Autonomous execution does not mean you should give up control over the development process.

Step 6: Review the changes and create a pull request

When Jules finishes, it provides a summary of the work.

You can inspect the modified files, review the differences, and examine the results of its execution.

Pay particular attention to:

Whether the changes actually solve the original problem.
Whether unrelated functionality was modified.
Whether tests passed.
Whether new dependencies were introduced.
Whether the implementation follows the project's conventions.

If the changes look acceptable, you can create a branch and use it to open a pull request on GitHub.

The pull request gives you a familiar place to review the code, request further changes, and decide whether the implementation is ready to merge.

Do not treat a completed Jules task as an automatic approval to merge. Tests can pass while an implementation still contains logic errors, security problems, or unexpected behavior.

Human review remains essential.

Step 7: Try starting tasks from GitHub Issues

One useful Jules feature is its connection with GitHub Issues.

Instead of opening Jules and manually describing every task, you can assign work through a GitHub issue.

For supported repositories, add the jules label to an issue. Jules can then pick up the task, work on it, and provide a link to the resulting pull request.

For example, a GitHub issue might look like this:

Title: Add pagination to the product API

Description:

The product endpoint currently returns every product
in a single response.

Implement pagination using the existing API conventions.

Requirements:
- Support page and limit parameters.
- Return pagination metadata.
- Preserve existing response compatibility.
- Add tests for pagination behavior.

After adding the jules label, the agent can begin working on the issue, provided it has the required repository authorization.

This is particularly useful for teams that already manage their development backlog through GitHub Issues.

What are Jules' limitations?

Even before the shutdown announcement, Jules had limitations that developers needed to understand.

1. It still requires good instructions

An autonomous coding agent cannot reliably infer every architectural decision or business requirement. Poorly defined tasks can produce changes that technically work but do not match the intended solution.

2. Generated code still needs review

Passing tests is useful, but it does not prove that a change is correct, secure, or ready for production.

3. Repository access needs to be managed carefully

Jules works with the code and files available in the repository. Avoid committing secrets, credentials, or sensitive information to repositories connected to external coding agents.

4. Usage limits apply

Jules has offered different access levels, including a free tier and higher limits through Google AI subscriptions. Limits and eligibility can change, so consult the official usage limits documentation rather than relying on older pricing articles.

5. The service is being discontinued

This is now the biggest limitation. Google has announced that Jules will become read-only on November 11, 2026, before winding down on December 31, 2026.

That means developers should avoid building long-term workflows that depend exclusively on Jules.

Important: How to export your Jules data before shutdown

Google's October 29 announcement explains that users can export their Jules data into a local folder.

The export includes:

Account settings.
Environment variables.
Scheduled tasks.
Connected repositories.
Memories.
Setup scripts.
Session prompts and plans.
Activity logs.
Code patches.

Your code, branches, and pull requests already stored in GitHub will remain there. However, it is still worth exporting your Jules-specific information so you can preserve the context behind your previous work.

I would prioritize this before November 11, when Jules is scheduled to become read-only.

For the latest instructions, check the official Jules changelog.

What are the alternatives to Google Jules?

The shutdown raises a practical question: where should developers go next?

There are several approaches worth considering, depending on whether you want an individual coding agent or a way to coordinate multiple agents across a development workflow.

Sharkly — For coordinating multiple AI coding agents

One option worth evaluating is Sharkly.

While Jules focuses on executing coding tasks asynchronously, Sharkly approaches the problem from a broader task-management and coordination perspective.

It brings engineering tasks, backlogs, sprints, and AI-agent execution into a shared workflow.

For example, a development team might have several issues to work through:

Fix a bug in the authentication module.
Add tests for the payment service.
Update API documentation.
Refactor a component.
Investigate a performance issue.

Instead of managing each coding agent independently, the team can organize tasks and assign suitable work to supported agents.

Sharkly supports coding tools such as Claude Code, Codex, Gemini CLI, and OpenCode.

Some of its useful capabilities include:

Assigning development tasks to AI agents.
Coordinating multiple agents working on separate tasks.
Keeping task context and execution results together.
Using isolated worktrees for parallel coding work.
Tracking progress through a shared task-management workflow.

This is particularly useful for teams that are moving beyond experimenting with one coding agent and want a more structured way to manage agent-driven development.

It is important to understand the distinction: Sharkly is not a direct replacement for Jules' cloud environment or its exact workflow. It provides a different approach to organizing and coordinating engineering work with supported agents.

You can explore it at Sharkly.ai.

Other options worth exploring

Tool	What it offers	Best suited for
Claude Code	Agentic coding through a terminal workflow	Developers who want an agent working directly with their codebase
OpenAI Codex	Cloud-based coding tasks and agent-assisted development	Developers who want to delegate coding tasks
Gemini CLI	Open-source AI agent in the terminal	Developers who prefer a command-line workflow
Sharkly	Task management and coordination across supported coding agents	Teams managing multiple agents and engineering tasks

The right choice depends on how you work. If you want an individual coding agent, tools such as Claude Code, Codex, and Gemini CLI are worth investigating. If you need to coordinate multiple agents and keep their work connected to a shared backlog, Sharkly offers a different layer of organization.

Final thoughts

Google Jules demonstrated an interesting direction for AI-assisted software development: developers could delegate meaningful engineering tasks, review a proposed plan, and return later to inspect the implementation.

Its asynchronous workflow was useful because it shifted the focus from generating individual code suggestions to completing larger, well-defined tasks.

However, with Google now planning to discontinue Jules by the end of 2026, developers should treat this as a transition point rather than the beginning of a long-term Jules workflow.

If you already use Jules, export your data and preserve the work that matters. If you are looking for an alternative, evaluate tools based on how you want to manage coding tasks, execute changes, and review the results.

And if your team is beginning to work with several AI coding agents, it may be time to think beyond the individual agent and consider how those agents fit into the wider engineering workflow.

Frequently asked questions

Is Google Jules free to use?

Jules has offered a free access tier, alongside higher usage limits through eligible Google AI subscriptions. The available limits and plans can change. Check the official Jules usage limits page.

Does Google Jules work with GitHub?

Yes. Jules connects to authorized GitHub repositories, works from a selected branch, and can create branches and pull requests containing its changes.

Can Google Jules write and test code?

Yes. Jules can modify code, install dependencies, run commands, and execute existing tests in its cloud environment. Developers should still review the resulting changes before merging.

Is Google Jules shutting down?

Yes. Google announced on October 29, 2026, that Jules will wind down on December 31, 2026. It is scheduled to become read-only on November 11, 2026.

What is the best alternative to Google Jules?

There is no single replacement for every Jules workflow. Claude Code, OpenAI Codex, and Gemini CLI offer agentic coding capabilities. Sharkly is worth considering when the challenge is coordinating multiple coding agents and organizing their work across engineering tasks.

Can I still access my Jules projects after the shutdown?

Google says code, branches, and pull requests already stored in GitHub will remain there. Jules-specific information can be exported before the service becomes read-only. Follow the official Jules changelog for the latest instructions.

Which AI Code Review Tools Actually Understand Your Whole Codebase? Qodo vs. Greptile vs. Graphite

Emmanuel Mumba — Thu, 29 Oct 2026 05:38:51 +0000

AI coding agents have changed how quickly developers can write software.

A task that might have taken an afternoon can now involve a few prompts and a coding agent making changes across several files, running tests, and preparing a pull request.

That speed is useful. It also creates a new problem.

The harder part isn't always writing the code anymore. It's understanding what that code change affects.

A function can look perfectly reasonable when viewed inside a pull request while quietly changing behavior that another service depends on. An API response can be modified in one repository while a consumer in another repository still expects the previous structure. A seemingly harmless refactor can also remove behavior that exists for a reason buried in an older pull request.

That's why I'm increasingly interested in a different question when looking at AI code review tools:

If I change this function, what breaks?

That is a much harder question than “Does this tool understand my diff?”

For this comparison, I'm looking at Qodo, Greptile, and Graphite through that lens. The goal isn't to compare feature checklists. It's to understand how much context each tool can bring to a code change and what that means in practice.

Tool	Primary focus	Useful when you need to...	Context / observations
Qodo	Codebase-aware AI review	Understand dependencies, historical context, rules, and potential impact across connected repositories	Persistent context around code relationships, PR history, specifications, and Git state
Greptile	Repository-aware AI code review	Review changes with broader repository context	Can analyze repository context and relevant PR history when brought into the analysis
Graphite	PR workflow	Organize, review, and merge related changes efficiently	Strong focus on stacked PRs, dependencies between PRs, and merge queues

Key Takeaway

The three tools approach code review from different angles.

Qodo focuses on persistent codebase context, including code relationships, dependencies, PR history, specifications, and rules.

Greptile brings repository context into AI-powered code review and can use relevant historical PR information when needed.

Graphite focuses more heavily on the pull request workflow, particularly stacked PRs, dependencies between changes, and merge queues.

The important distinction isn't simply which tool has more features. It's what problem you're trying to solve.

If the question is:

“I changed this function. What else could be affected?”

then codebase context, dependencies, and historical information become especially important.

What Does It Mean to Understand the Whole Codebase?

“Codebase-aware” can mean different things.

At the simplest level, a reviewer needs to understand the code that changed. That's useful for catching obvious bugs, incorrect conditions, missing error handling, or suspicious patterns.

But understanding the impact of a change requires several additional layers of context.

Consider a function like:

validateUser()

Knowing what the function does is only the beginning.

A useful reviewer should also be able to reason about questions such as:

What calls this function?
What does it call?
Which services depend on its return value?
Are there API consumers that expect its current behavior?
Has this behavior changed before?
Was there a previous PR that explains why it was implemented this way?
Are there project-specific rules governing this code?
Could a change affect another repository or service?

That is the difference between understanding a diff and understanding the system around the diff.

And it matters more as software becomes more distributed.

A modern application might look something like this:

Frontend
   |
API Gateway
   |
User Service
   |
Auth Service
   |
Shared Library
   |
Other Services / Repositories

A developer working on the Auth Service may only see the files being changed.

The consequences of the change may exist somewhere else.

The Scenario: “I Changed This Function. What Breaks?”

Let's take a simple example.

Suppose an authentication service has a function called validateUser().

A developer changes it to simplify validation and standardize the response returned by the service.

The change passes the unit tests.

The pull request is relatively small.

At first glance, everything looks fine.

But there are complications:

Another service calls validateUser() directly.
A shared library expects a particular return structure.
One API consumer interprets a specific error response differently.
A previous pull request introduced a workaround around the existing behavior.
The authentication service and one of its consumers live in different repositories.

Now ask the question:

“I changed

validateUser()

. What could this break?”

This is where AI code review tools start to become interesting.

The quality of the answer depends heavily on the context available to the reviewer.

Qodo: Looking Beyond the Current Pull Request

The first thing that stands out with Qodo is that its approach isn't limited to understanding the current PR in isolation.

The engineering problem

Our developer has modified validateUser().

The immediate code looks reasonable, but the function has relationships elsewhere in the application. Some of those relationships aren't obvious from the changed file.

The real review question is therefore about impact, not just correctness.

What Qodo can find

Qodo's Context Engine is designed to bring together the broader context surrounding a code change, including the codebase, dependencies, connected repositories, previous pull requests, live Git state, product specifications, and organizational rules. This context gives its review agents more information to reason about a change beyond the files and lines included in the current pull request.

That changes the type of questions a reviewer can ask.

Instead of only examining the changed lines, Qodo can reason about things such as:

where the function is used
dependencies between components
potential downstream impact
relevant historical changes
project specifications and rules
relationships across connected repositories

In our example, that means the reviewer can move from:

“This function still compiles.”

to:

“What other parts of the system rely on this behavior?”

That's where the Agentic Toolbox fits into the discussion.

Why that matters

The value of broader context isn't necessarily that it produces a longer list of review comments.

It's that it can surface relationships that aren't visible in the diff.

A change to an authentication function isn't just an authentication-function change if several other services depend on its behavior.

That's especially important in larger engineering organizations, where a developer may not have the full history of every service or repository in their head.

Qodo also maintains PR history as part of its context, which adds another dimension to the review.

Sometimes the reason code looks unusual isn't because it was written poorly. There may be a previous decision behind it.

That history can be just as important as the current code.

Greptile: How Far Does Repository Context Go?

Greptile takes a codebase-aware approach to AI code review as well, which makes it useful to compare with a generic diff reviewer.

The question is how much context is available when the question moves beyond the current pull request.

The engineering problem

Again, we've changed validateUser().

We want to know whether another part of the system depends on its behavior.

A reviewer that understands the repository can potentially identify relationships that wouldn't be obvious from the changed lines.

What Greptile can find

Greptile can analyze repository context around a pull request and use that understanding to identify issues that aren't necessarily confined to individual lines.

That makes it useful for questions involving relationships between pieces of code rather than just syntax or local implementation mistakes.

The distinction becomes more apparent when historical context enters the picture.

Greptile can work with previous pull request context, but that historical information isn't tracked continuously in the same way as Qodo's persistent PR history context. Relevant past PRs can instead be brought into the analysis when needed.

Why that matters

There's nothing inherently wrong with a PR-centered approach.

For many teams, the pull request is exactly where review happens, and having strong repository context around that PR can be valuable.

But there's a difference between:

“Here is the context surrounding this change.”

and:

“The review system continuously maintains context about the codebase and its history.”

That difference becomes increasingly important when you're asking questions about why something works the way it does, rather than simply whether the current implementation contains a problem.

Graphite: A Different Approach to the PR Workflow

Graphite is an interesting comparison because its strength lies in a different part of the development process.

Graphite is particularly focused on improving the pull request workflow, including concepts such as stacked PRs and merge queues.

That's valuable when the problem is managing a complicated set of changes.

But our question is slightly different.

We're asking:

“I changed this function. What else could be affected?”

The engineering problem

Imagine the developer has several related changes that need to move through review.

Managing those changes efficiently can become difficult.

You might have dependencies between pull requests, multiple changes waiting for review, and a sequence that needs to be merged in the right order.

What Graphite helps with

This is where Graphite's workflow capabilities become particularly useful.

Stacked PRs can help developers break larger changes into smaller, reviewable pieces while maintaining their relationships.

Merge queues can help teams manage what happens after review and keep changes moving toward integration.

Why that matters

This isn't a weakness so much as a difference in focus.

Graphite is solving:

“How do we make the PR workflow more efficient?”

Our original question is:

“What does this code change affect across the system?”

Those are related problems, but they aren't the same problem.

That's why comparing Graphite and Qodo purely as competing “AI code review tools” can be misleading.

One is particularly strong around the PR workflow.

The other is focused much more heavily on code understanding, context, and review.

So What Can Each Tool Actually Answer?

Looking at the three tools through the same questions makes the differences easier to see.

It's tempting to turn comparisons like this into a simple ranking.

But software teams don't all have the same problem.

If your biggest challenge is managing complex PR workflows, Graphite addresses that directly.

If you want repository-aware AI review, Greptile is relevant.

If you want persistent context around your codebase, dependencies, PR history, rules, and potential blast radius, Qodo is particularly well aligned with that problem.

But What If We Don't Wait Until the PR?

This is where the direction of AI-assisted development gets more interesting.

We've spent most of this article talking about what happens when code is being reviewed.

But coding agents are increasingly involved before the pull request exists.

A developer might tell an agent:

“Refactor the authentication flow.”

The agent starts inspecting files and making changes.

At that point, the most useful context may not be a review comment several minutes later.

It may be the information the agent could have used before writing the first line.

For example:

Which repositories depend on this authentication service?

Why does this function return this particular error?

Are there rules for changing authentication behavior?

Has this code caused regressions before?

That's where Qodo's new Agentic Toolbox fits naturally into this discussion.

Qodo Agentic Toolbox: Bringing Codebase Context Into the Coding Session

Qodo's Agentic Toolbox extends Qodo's capabilities beyond review time.

The idea is straightforward: coding agents can call Qodo's five Agentic Toolbox tools while planning, writing, and reviewing code: Codebase Wisdom, Get Rules, Review, Review Resolver and Manage Standards.

That means the same type of codebase context we've been discussing doesn't have to wait until a PR is created.

Before making a change, an agent can use Qodo's Context Engine to access connected repositories, dependencies, pull request history, specifications, and live Git state.

That matters because understanding the blast radius of a change is often more useful before the change is made.

The Toolbox also brings organizational rules into the coding session.

Instead of an agent writing code first and discovering the team's standards during review, the agent can retrieve those rules as part of the workflow.

Then there's local review.

An AI coding agent can review committed and uncommitted changes, receive structured findings, and work through those findings before opening the PR.

The resulting workflow looks something like:

Understand
    ↓
Plan
    ↓
Write
    ↓
Review
    ↓
Resolve
    ↓
Open PR

That's a meaningful shift.

The review layer isn't being treated as something that happens only after the coding agent has finished.

It's becoming part of the agent's workflow.

The Interesting Part: Agent-to-Agent Review

There's another idea here that I think is worth paying attention to.

The coding agent that writes the code doesn't necessarily have to be the only system evaluating that code.

A coding agent can focus on implementation.

An independent quality layer can challenge the resulting work.

That doesn't mean removing humans from the process.

Human reviewers still need to make decisions about architecture, product requirements, trade-offs, and risks that automated systems can't settle confidently.

But if an independent reviewer can catch obvious problems before the PR reaches a human, the human reviewer can spend more time on those higher-level decisions.

That's the basic idea behind the Agentic Toolbox's review workflow.

The agent writes.

Another agent checks.

The unresolved questions reach the human.

And because the reviewer has access to broader context, the check doesn't have to be limited to:

“Does this line of code look suspicious?”

It can ask:

“Does this change make sense given the rest of the system?”

What I Take Away From the Comparison

The biggest lesson here is that codebase understanding isn't a single feature.

There are several layers to it.

First, there's understanding the current diff.

Then there's understanding the repository around it.

Then dependencies.

Then downstream impact.

Then historical context.

Then organizational rules.

And eventually, relationships across repositories and services.

Different tools emphasize different parts of that stack.

Graphite is a good example of why this distinction matters. If you're primarily trying to improve how a team structures and merges PRs, its workflow capabilities are highly relevant.

Greptile brings AI-powered understanding into the repository and PR review process.

Qodo's approach is broader around persistent context, including code relationships and PR history, while also connecting that context to rules and review workflows.

The interesting development is that Qodo is now making that context available to coding agents through the Agentic Toolbox rather than keeping it solely on the review side.

That moves the question from:

“Can AI review my code?”

to:

“Can my coding agent understand the system before it changes it, and can another layer challenge the work before I open the PR?”

That's a much more interesting problem.

The Bottom Line

AI code review is becoming less about reading a diff and more about understanding the system behind it.

If the question is simply whether a tool can identify problems in a pull request, there are plenty of options.

But if the question is:

“I changed this function. What else could break, and why?”

then context becomes the differentiator.

Dependencies matter.

Historical decisions matter.

Project rules matter.

Cross-repository relationships matter.

And increasingly, that context needs to be available before the code is written, not only after the pull request is opened.

That's what makes the Agentic Toolbox particularly interesting.

As coding agents become more involved in software development, the context available to them becomes increasingly important. Codebase relationships, dependencies, historical decisions, project rules, and review feedback all influence whether an automated change is safe to merge. For teams that need to understand what a code change could break across the system, Qodo is the most complete choice in this comparison. Its Context Engine brings together codebase context, dependencies, connected repositories, and PR history, while the Agentic Toolbox makes that context available to coding agents during development. This allows teams to move beyond reviewing code after it's written and toward identifying potential issues before changes reach a pull request.

For teams adopting coding agents at scale, that could end up being just as important as the ability to generate the code in the first place.

Best AI Collaboration Tools for High-Performing Teams in 2026

Emmanuel Mumba — Wed, 28 Oct 2026 07:43:13 +0000

AI collaboration tools are changing how teams plan projects, share information, and get work done. But I think there's an important distinction that often gets missed: helping people communicate faster is not the same as helping a team deliver work faster.

A tool that summarizes meetings can save time. An AI assistant that finds information across company documents can make decisions easier. And a system that lets AI agents execute engineering tasks can change how development teams approach their entire workflow.

The right choice depends on where your team loses the most time. Is it endless meetings? Unclear ownership? Information scattered across different apps? Or too much work waiting in the engineering backlog?

In this guide, I'll look at seven AI collaboration tools worth considering in 2026, what each one does best, and how to choose the right combination for your team.

Quick comparison: Best AI collaboration tools

Tool	Best for	Main strength
Sharkly	Product and engineering teams using AI coding agents	Connects task management with agent execution
Slack	Team communication	AI-assisted conversations and information discovery
ClickUp	All-in-one project management	Tasks, documents, and AI-assisted workflows
Notion	Documentation and team knowledge	Shared workspaces and AI-assisted knowledge retrieval
Asana	Cross-functional project coordination	Goals, project tracking, and workflow automation
Microsoft Teams	Organizations using Microsoft 365	Meetings, messaging, and connected workplace tools
Google Workspace	Teams collaborating on documents	Shared documents, spreadsheets, email, and AI assistance

1. Sharkly — Best for teams coordinating AI coding agents

Best for: Product and engineering teams that want AI agents to do assigned work, not just provide suggestions.

One of the biggest changes happening in software development is the move from using AI as a coding assistant to treating AI agents as participants in the development workflow.

That creates a new coordination problem. Once a team starts using multiple agents, someone still needs to define tasks, provide context, track progress, manage dependencies, and review the results.

This is where Sharkly stands out.

Rather than being another standalone coding assistant, Sharkly brings task management and AI-agent execution into the same workflow. Teams can manage issues, backlogs, sprints, and assignments while directing work to agents that use coding tools such as Claude Code, Codex, Gemini CLI, and OpenCode.

What makes Sharkly different?

Its central idea is straightforward: the place where a team plans work should also help coordinate the execution of that work.

With Sharkly, teams can:

Assign tasks to AI agents: Route bugs, technical debt, requirements, and other engineering work to agents.
Coordinate parallel work: Run multiple agents on separate tasks while keeping their progress visible.
Keep context attached to tasks: Give agents the relevant requirements and task information instead of relying on disconnected prompts.
Review results before merging: Bring execution results back to the task so a person can inspect the changes and decide what happens next.
Keep people and agents on the same board: Teammates can manage assignments, discuss issues, and monitor progress in one shared workspace.

Sharkly also supports isolated worktrees for agent execution, helping separate parallel code changes.

The important distinction is that Sharkly coordinates the work; the connected coding tools perform the code execution. Teams continue using the coding plans or API accounts associated with those tools.

Where Sharkly fits best

Imagine a development team preparing a sprint with 20 issues. Some tasks require a developer's judgment, while others involve updating documentation, fixing a contained bug, or adding tests.

Instead of treating every issue as a manual assignment, the team can identify suitable tasks for agents, assign them through Sharkly, and review the resulting work alongside the rest of the sprint.

That doesn't eliminate engineering review. It gives the team a more organized way to coordinate the work agents can perform.

Sharkly's website currently lists its core product as free for teams of up to five people, with a Team plan listed at $7 per user per month. Model usage remains tied to the team's existing coding plans and API accounts, so account for those costs separately. Check the official pricing page before making a purchasing decision.

My take: If your team already uses AI coding tools but struggles to organize agent assignments and track their results, Sharkly is one of the most relevant tools to evaluate. Its value is not simply generating more code; it is connecting execution to the team's existing work.

2. Slack — Best for AI-assisted team communication

Best for: Teams that rely on channels, messages, and ongoing discussions to coordinate daily work.

Slack has long been a central communication tool for distributed teams. Its AI capabilities build on that foundation by helping people work with information contained in conversations and connected workplace resources.

Instead of manually scrolling through a long channel history, teammates can use available AI features to summarize discussions, catch up on conversations, and find relevant information more efficiently.

Slack can be particularly useful when decisions are made across several channels and time zones.

Key strengths

Organizes conversations by project, department, or topic.
Helps teammates catch up on discussions and find information.
Supports integrations with project management and development tools.
Keeps routine communication in a familiar shared workspace.

What to consider

Slack is primarily a communication and coordination layer. A summary of a discussion does not automatically turn into a completed task, and teams still need a reliable process for assigning ownership and tracking delivery.

If your biggest problem is information scattered across conversations, Slack is worth considering. If the real bottleneck is executing engineering tasks, you'll probably need to pair it with a task management or agent execution system.

Explore Slack's AI capabilities.

3. ClickUp — Best for all-in-one project management

Best for: Teams that want tasks, documents, project views, and AI-assisted work in one platform.

ClickUp combines project management with documentation, task tracking, collaboration, and AI features. That makes it attractive to teams that would rather manage their work in one environment than maintain several separate applications.

A marketing team, for example, might use ClickUp to plan a campaign, assign content tasks, maintain documentation, and monitor deadlines. An engineering team can also use it to organize project work and track progress.

Key strengths

Multiple ways to view and organize work.
Tasks, documents, and project information in one platform.
AI assistance for supported writing, summarization, and productivity workflows.
Automation options for reducing repetitive project administration.

What to consider

An all-in-one platform can become complicated if the team configures too many spaces, custom fields, statuses, and workflows. AI features also vary by plan and availability, so check what is included before committing.

ClickUp is a good option when the main goal is consolidating project management and collaboration. Teams that need specialized AI-agent execution should separately evaluate how their coding tools fit into the workflow.

Explore ClickUp.

4. Notion — Best for documentation and shared knowledge

Best for: Teams that need a central place for project documentation, meeting notes, internal guides, and decisions.

Poor documentation creates collaboration problems that are easy to underestimate. People repeat questions, new teammates struggle to understand a project, and important decisions disappear inside old conversations.

Notion helps teams organize this information through shared pages, databases, wikis, and project workspaces. Its AI features can assist with tasks such as summarizing content, drafting documents, and finding information across supported sources.

Key strengths

Flexible documentation and knowledge management.
Shared project notes and internal wikis.
AI assistance for supported writing and information-retrieval tasks.
Connected documentation that teammates can update together.

What to consider

Notion works best when people maintain the information they put into it. An AI assistant cannot reliably compensate for outdated documentation, missing decisions, or inconsistent project records.

It can also be tempting to build an elaborate workspace when a simpler structure would be easier to maintain.

Choose Notion when the team needs a better source of truth for information, rather than another tool primarily focused on messaging or execution.

Explore Notion.

5. Asana — Best for cross-functional project coordination

Best for: Teams managing projects that involve multiple departments, stakeholders, and deadlines.

Some projects fail because the people doing the work are not communicating. Others fail because everyone is communicating, but nobody has a clear view of ownership, dependencies, and deadlines.

Asana focuses on the second problem.

It provides project and task management capabilities for organizing work, tracking progress, and connecting individual tasks to larger goals. Its AI features can support selected workflow and productivity tasks, depending on the plan and configuration.

For example, a product launch may involve design, engineering, marketing, and customer support. Each group has its own responsibilities, but the overall project depends on their work coming together at the right time.

Key strengths

Clear task ownership and deadlines.
Project views for monitoring progress.
Support for coordinating work across departments.
Workflow automation and AI-assisted productivity features.

What to consider

Asana is strongest when teams have defined projects and need a structured way to manage them. Teams should evaluate the available plans and AI features carefully, particularly if advanced automation or AI functionality is essential.

It is not primarily a coding-agent execution platform, so engineering teams with that requirement may need an additional tool.

Explore Asana.

6. Microsoft Teams — Best for organizations using Microsoft 365

Best for: Organizations that already use Microsoft tools for communication, documents, meetings, and business workflows.

Microsoft Teams brings chat, meetings, calls, and collaboration together. For organizations using Microsoft 365, its wider ecosystem can make it easier to connect conversations with shared files and workplace productivity tools.

Microsoft 365 Copilot adds AI assistance to supported experiences, helping eligible users work with meeting information, messages, documents, and other organizational context.

Key strengths

Meetings, messaging, and collaboration in one environment.
Integration with Microsoft 365 applications.
AI-assisted workflows through supported Copilot experiences.
Familiar collaboration options for organizations already invested in Microsoft.

What to consider

The experience depends on the organization's Microsoft 365 setup, licenses, and enabled features. Some AI capabilities require additional eligible subscriptions.

Teams may also find that adding AI features does not automatically improve collaboration if meetings lack clear outcomes or responsibilities remain undefined.

Microsoft Teams is a sensible choice when your company already runs on Microsoft 365 and wants to extend that environment rather than introduce an entirely new collaboration platform.

Explore Microsoft Teams.

7. Google Workspace — Best for real-time document collaboration

Best for: Teams that collaborate through shared documents, spreadsheets, presentations, email, and calendars.

Google Workspace is built around the idea that multiple people should be able to work on the same information without constantly sending files back and forth.

Google Docs, Sheets, Slides, Drive, and Gmail provide the foundation. Gemini features available through eligible Workspace plans can add AI assistance to supported workflows, including drafting, summarizing, and working with information.

This is particularly useful for distributed teams that prepare proposals, review reports, maintain shared spreadsheets, or collaborate on presentations.

Key strengths

Real-time collaboration on shared documents.
Straightforward file sharing and version history.
Connected email, calendar, storage, and productivity applications.
AI assistance in supported Workspace experiences.

What to consider

Google Workspace is primarily a productivity and collaboration ecosystem. It may not provide all the structured project management or specialized engineering execution features a team needs.

Also, check the current plan requirements for the Gemini features you intend to use. Availability and functionality can vary.

For teams that already work in Google Docs and Drive every day, improving the existing workflow may be more effective than moving everyone to a new platform.

Explore Google Workspace.

How to choose the right AI collaboration tool

The most useful tool is not necessarily the one with the largest list of AI features. It is the one that removes a recurring obstacle in your team's workflow.

Here is a practical way to narrow the options.

If your main challenge is…	Start by evaluating…
Coordinating AI coding agents and engineering tasks	Sharkly
Finding information in team conversations	Slack
Managing projects and tasks in one workspace	ClickUp
Keeping documentation and decisions organized	Notion
Coordinating projects across departments	Asana
Bringing AI into an existing Microsoft environment	Microsoft Teams
Collaborating on documents and spreadsheets	Google Workspace

Before choosing a tool, ask these questions:

1. Where does work currently get stuck?

Look for the actual bottleneck. It may be unclear ownership, scattered information, manual administration, or slow task execution. Choose a tool that addresses that problem directly.

2. Does the tool fit the team's existing workflow?

A platform can have impressive features and still fail if it forces the team to abandon useful habits or duplicate work across multiple systems.

3. What does the complete cost look like?

Consider user licenses, AI feature access, usage limits, integrations, and any model or API costs. A low starting price does not necessarily mean the full workflow will be inexpensive.

4. How will the team measure improvement?

Pick a few concrete indicators, such as time spent searching for information, tasks completed on schedule, time from assignment to review, or the amount of repetitive administration.

5. Where does human review remain necessary?

AI can help with communication, planning, and execution, but teams still need people responsible for decisions, permissions, quality, and final approval.

Can one AI collaboration tool do everything?

Sometimes a single platform can cover most of a team's needs. But it is not always sensible to force communication, documentation, project management, and engineering execution into one application.

A practical setup might use Slack for communication, Notion for documentation, and a project management tool for task ownership. An engineering team working with multiple coding agents might add Sharkly to connect assigned work with agent execution and review.

The important thing is to avoid creating unnecessary overlap.

For example, if a team already has a project management platform it uses consistently, replacing it solely to gain an AI writing assistant may not be worthwhile. On the other hand, if coding agents are creating more work than the team can track, adding a coordination layer may address a more significant problem.

The goal is not to maximize the number of AI tools. It is to reduce the friction between deciding what needs to happen and getting that work completed.

Final thoughts

AI collaboration tools are most valuable when they improve a team's actual working process, not just its ability to generate text or summarize information.

Slack helps teams manage communication. Notion helps preserve knowledge. ClickUp and Asana organize projects. Microsoft Teams and Google Workspace extend AI assistance across familiar productivity environments.

For engineering teams adopting AI coding agents, Sharkly deserves particular attention because it connects task planning, agent assignment, execution visibility, and human review in one workflow. Instead of treating every coding agent as a separate tool to manage, teams can bring agent-driven work into the same system where they coordinate their projects.

My recommendation is to start with your biggest bottleneck, choose one tool that addresses it, and measure the result before expanding your stack. The best AI collaboration setup is the one that helps your team deliver better work with less coordination overhead.

Frequently asked questions

What is the best AI collaboration tool for teams in 2026?

It depends on the team's needs. Sharkly is worth evaluating for teams coordinating AI coding agents, Slack for communication, ClickUp for all-in-one project management, and Notion for shared documentation and knowledge.

Are AI collaboration tools suitable for small teams?

Yes. Small teams can use AI features to reduce repetitive work, organize projects, and share information more efficiently. However, it is worth checking free-plan limits, AI usage restrictions, and whether the tool adds unnecessary complexity.

Can AI collaboration tools replace project managers?

Not completely. They can assist with task tracking, summaries, reminders, and workflow automation, but people still need to set priorities, resolve conflicts, make decisions, and take responsibility for outcomes.

How is Sharkly different from an AI coding assistant?

An AI coding assistant or agent performs coding tasks. Sharkly focuses on coordinating the work around those agents: task assignment, context, progress, execution results, and human review. It connects to coding tools rather than replacing them.

Should a team use several AI collaboration tools together?

Yes, when each tool serves a distinct purpose and the integrations reduce rather than increase duplicated work. Start with a small set of complementary tools, establish clear ownership of information, and expand only when there is a demonstrated need.

How to Use Jira with AI Agents for Free in 2026

Emmanuel Mumba — Fri, 23 Oct 2026 08:18:04 +0000

I’ve been looking at how developers can bring AI coding agents into their existing workflows, and Jira is an interesting place to start. Many engineering teams already use it to manage bugs, feature requests, sprints, and backlogs. The problem is that having a ticket in Jira does not automatically mean an AI coding agent can pick it up, work on the code, and return the results for review.

At the same time, Atlassian is bringing more AI capabilities into its products through Rovo. These features are useful, but teams that want to use their own coding agents may not want to depend on another AI usage allowance just to connect their existing development workflow.

So I wanted to explore a practical alternative: using Jira with your own AI coding agents without paying for additional Rovo credits.

The idea is straightforward. Keep Jira as your issue tracker, connect it to a tool that coordinates agent execution, and let agents such as Claude Code or Codex work on assigned tasks using the subscriptions or API accounts you already have.

One option for doing this is Sharkly. It connects Jira issues to agent-driven development tasks, lets teams assign work to their existing coding agents, and provides a place to track execution and review results.

In this guide, I’ll walk through how the workflow works, how to set it up, what you can do with it, and what “free” actually means.

Why Use AI Agents with Jira?

Jira is good at organizing work. You can create issues, assign responsibilities, prioritize bugs, plan sprints, and track progress.

But there is still a gap between managing a task and completing it.

Imagine a Jira ticket that says:

Fix the validation error that occurs when users submit the registration form with an invalid email address. Add tests to cover the affected cases.

A developer would normally open the repository, investigate the issue, make the change, run tests, and submit a pull request. They would then update Jira so the rest of the team knows what happened.

An AI coding agent can help with much of that implementation work. It can inspect the code, propose a fix, modify files, run tests, and potentially create a pull request.

The challenge is connecting those steps to the existing Jira workflow.

Without an integration, someone may have to copy the ticket description into an AI tool, manually provide repository context, monitor the run, and paste the results back into Jira.

That works for occasional tasks, but it becomes tedious when a team has dozens of tickets.

Connecting Jira to an agent execution workflow can help teams:

Assign coding tasks to AI agents from existing engineering work.
Preserve ticket descriptions and acceptance criteria.
Reduce manual copying between Jira and coding tools.
Track execution, blockers, and results.
Run independent tasks in isolated workspaces.
Review AI-generated changes before merging them.

The goal is not to remove developers from the process. It is to reduce the repetitive work between identifying a problem and producing a reviewable solution.

Do You Need to Pay for Rovo Credits to Use AI Agents with Jira?

Not necessarily. It depends on which AI capabilities and integrations you use.

Atlassian's Rovo provides AI-powered search, chat, agents, and other capabilities across supported Atlassian products. Rovo usage is measured through credits for certain AI interactions and enriched Teamwork Graph API calls. The included allowance depends on your Atlassian plan and eligible products. See Atlassian's Rovo pricing and usage information.

However, Jira does not require every external coding agent to use Rovo for its code execution.

You can connect Jira to a separate workflow tool and use a coding agent that runs through your own existing subscription or API account. In that setup, the coding agent's model usage is handled by the agent provider rather than being automatically charged as Rovo usage.

For example, Sharkly offers a Jira integration and supports coding agents such as Claude Code and Codex. Its published pricing says the product is free for teams of up to 10 people, while model usage stays on the coding plans and API accounts the team already uses. Check the Sharkly website for current plan details.

Here is the important distinction:

So when I say you can use Jira with AI agents for free, I mean you can avoid paying for an additional Rovo-based coding workflow by using your existing agent setup and a free coordination plan where eligible.

It does not mean that every component of the workflow is free. Your Jira subscription, coding-agent plan, API consumption, and infrastructure can still have costs.

What You Need Before Getting Started

Before setting up the integration, make sure you have the following:

A Jira account with access to the project you want to use.
A Sharkly account.
A coding agent, such as Claude Code or Codex, configured on a computer.
Access to the repository where the agent will work.
Permission to connect the Jira project, or help from a Jira administrator if required.

You do not need to migrate your entire Jira backlog to get started. You can connect a single project and try the workflow on a low-risk task first.

Step 1: Set Up Your AI Coding Agent

Start with the coding agent you already use rather than choosing a new model just for the Jira integration.

For example, you might already have Claude Code or Codex configured on your development machine. Make sure it can access the intended repository and that its authentication is working.

You should also verify that the agent can perform the basic actions required for your task, such as:

Reading relevant source files.
Editing code in the assigned workspace.
Running the project's tests.
Reporting errors or blockers.
Producing a reviewable change.

This matters because Sharkly coordinates work; the connected coding agent is still responsible for performing the coding.

If you have not configured an agent before, start with its official documentation:

Claude Code
OpenAI Codex

Use the provider account or API credentials you are authorized to use. You do not need to switch to a new model provider simply to connect Jira to Sharkly.

Step 2: Create Your Sharkly Workspace

Next, open Sharkly and set up your workspace.

Sharkly provides a shared environment for organizing tasks, assigning agents, tracking execution, and reviewing results. It is designed to work alongside existing development tools rather than replacing the coding agent itself.

Once your workspace is ready, connect a computer that has access to the repository and the coding-agent runtime you configured in Step 1.

A computer here is the execution environment where the agent actually runs. Depending on your setup, this can be your own development machine or another supported computer.

Before moving on, confirm that the computer is available and the intended coding-agent runtime is configured correctly.

Step 3: Connect Jira to Sharkly

Now connect your Jira project.

According to the Sharkly Jira integration documentation, the connection supports Jira Cloud as well as Jira Server and Data Center.

For Jira Cloud, you generally need:

Your Jira host, such as your-company.atlassian.net.
The email address of your Jira account.
An Atlassian API token.

For Jira Server or Data Center, the connection uses the Jira base URL, username, and a supported authentication method.

In Sharkly, open the integrations settings, select Jira, and choose the option to connect your Jira account. Enter the required details and follow the validation steps.

Keep API tokens private. Use an account with access to the project you want to connect, and follow your organization's credential-management policies.

Connecting the account does not automatically import or synchronize all your Jira data. You must choose how you want the project and its tasks to be connected.

Step 4: Link a Jira Project to a Sharkly Space

After connecting Jira, create a sync link between the Jira project and a Sharkly Space.

A Space is where the corresponding tasks and agent work are organized.

The setup process lets you select the Jira project, choose the target Space, configure synchronization, and review status and user mappings.

You generally have two approaches:

Option A: Synchronize a project

Use synchronization if you want Jira and Sharkly to remain connected as work progresses. Depending on the configured sync direction, supported changes to linked issues and tasks can flow between the two systems.

Option B: Import existing issues

Use import if you want to copy historical Jira issues into Sharkly, such as when testing a workflow or planning a migration.

You can also import existing work and enable ongoing synchronization as part of the setup.

For a team that wants to keep using Jira, I would start with a single project and a synchronization link. This allows the team to test agent execution without moving the entire backlog.

Review the issue status mappings and user mappings before confirming the connection. If your setup requires webhook permissions that your Jira account does not have, a Jira administrator may need to configure the webhook.

The official setup instructions are available here: Sharkly Jira integration.

Step 5: Choose a Jira Ticket for Your First Agent Task

Once the project is connected, select a small, well-defined issue.

I would avoid starting with a vague task such as “Improve the application” or a change that could affect production security, payment processing, or critical infrastructure.

Instead, choose something like:

Add validation for a missing field.
Fix a reproducible UI bug.
Add unit tests for an existing function.
Update an isolated component.
Investigate the cause of a failing test.

The quality of the ticket matters. An AI agent needs enough information to understand what success looks like.

For example, a useful ticket could read:

Title: Add validation for invalid email addresses

Description: The registration form currently accepts malformed email addresses. Add validation to reject invalid formats and display a clear error message.

Acceptance criteria:

Invalid email formats are rejected.
Valid email addresses continue to work.
The error message is understandable.
Tests cover valid and invalid inputs.
Existing registration behavior is not unintentionally changed.

The clearer the acceptance criteria, the easier it is to review the agent's result.

Step 6: Assign the Ticket to Your AI Agent

With the Jira project connected, its linked issue can appear as a task in Sharkly.

You can then assign that task to a configured agent, much as you would assign work to a teammate.

The agent configuration determines how the task is executed, including its instructions, runtime, and available resources.

For example, you might assign the registration-validation task to an agent configured to use Claude Code. The agent can inspect the relevant repository, implement the change, run tests, and report what it completed.

The same general approach can be used with other supported coding-agent runtimes.

The important thing is that the agent receives the actual task context instead of relying on someone to manually copy the ticket into a separate conversation.

If this is your first run, keep the scope small and make sure the agent has access only to the resources it needs.

Step 7: Let the Agent Work in an Isolated Environment

Once the task is assigned and the execution environment is ready, the agent can begin working.

Sharkly supports isolated workspaces for repository-backed tasks, including Git worktree-based execution. This helps separate concurrent coding tasks so they do not directly overwrite one another's working files.

That matters when you eventually want to run more than one agent at a time.

Imagine assigning three independent tickets:

Agent A adds a validation rule.
Agent B fixes a layout issue.
Agent C adds unit tests for a separate service.

If every agent edits the same working directory, changes can interfere with each other. Separate worktrees reduce that risk by keeping each task's changes isolated.

Isolation does not eliminate every integration problem. Two agents can still produce changes that conflict when branches are combined, so testing and review remain necessary.

During execution, use the task record to follow the agent's progress, inspect relevant output, and identify blockers.

Step 8: Review the Results and Pull Request

When the agent finishes, inspect its output before accepting the change.

Depending on the task and configured workflow, the agent may report its results, describe the files it changed, run tests, or create a pull request.

If you use Sharkly's GitHub integration, a pull request can be linked to the corresponding task through its task ID in the branch name, title, or body.

This helps connect the coding result back to the work item that initiated it.

Before approving anything, I would check:

Did the agent address the actual requirements in the Jira ticket?
Are the code changes limited to the intended scope?
Did the relevant tests run, and did they pass?
Are there any unexpected changes or dependencies?
Does the implementation introduce security, performance, or compatibility concerns?

Do not treat an agent's claim that tests passed as a substitute for verifying the results when the change matters.

A pull request should still go through the team's normal review process. The agent can produce a proposed implementation, but a person should remain responsible for deciding whether the code is ready to merge and release.

Step 9: Keep Jira as the Source of Work

One of the main benefits of this workflow is that your team does not have to abandon Jira.

Product managers can continue writing requirements. Engineers can prioritize bugs and plan sprints. QA can follow issues through testing. AI agents can help implement selected tasks, while the team retains visibility into the work.

With a configured synchronization link, supported changes can flow between Jira and Sharkly. This reduces the need to maintain two completely separate records of the same task.

It is still worth defining how your team handles statuses, pull-request reviews, and final acceptance. For example, merging a pull request should not be assumed to close a Jira issue automatically unless you have explicitly configured the relevant workflow.

The aim is to make agent execution part of your existing process rather than introducing another disconnected place where work happens.

What Can You Automate Once the Integration Works?

After the first task succeeds, you can gradually expand the workflow.

Bug fixes

Assign agents reproducible bugs with clear acceptance criteria. Ask them to investigate the cause, implement a fix, and add tests.

Test generation

Use agents to add unit tests around existing functionality or cover specific edge cases. Review the tests to ensure they actually verify the expected behavior.

Small feature implementations

For well-scoped features, an agent can inspect the relevant code, implement the change, and return a pull request for review.

Technical investigations

An agent can inspect a codebase, trace a failure, identify likely causes, and document its findings. This can be useful even when you do not want it to modify the code.

Parallel engineering tasks

Once your team is comfortable with isolated execution, you can assign independent tickets to different agents. Start with tasks that do not depend on one another and increase concurrency gradually.

Not every task should be automated. Architecture decisions, ambiguous requirements, sensitive changes, and production releases may still need substantial human involvement.

How Much Can You Actually Save?

The main potential saving is not necessarily the cost of the model. It is the time spent moving between tools and coordinating work manually.

Consider a developer who spends time copying ticket descriptions, opening coding sessions, setting up branches, monitoring execution, and updating Jira after every task.

A connected workflow can reduce some of those repetitive steps.

The exact savings depend on the team's ticket volume, agent reliability, review requirements, and existing process. It would be misleading to promise a fixed productivity improvement without measuring your own workflow.

To evaluate whether the setup is worthwhile, start by tracking:

Time from ticket assignment to a reviewable change.
How much manual setup each task requires.
The proportion of agent tasks that produce usable results.
Time spent reviewing and correcting agent-generated changes.
The number of defects or rework items introduced.

If those metrics improve without compromising quality, the integration is doing its job.

Is This Better Than Using Rovo Directly?

That depends on what you want the agent to do.

Rovo provides AI capabilities within Atlassian's ecosystem, including search, chat, and agent functionality. It can be a natural choice if you want AI features that work directly with Atlassian's connected knowledge and applications.

A separate coding-agent workflow is useful when you want to use tools you already have, such as Claude Code or Codex, to work on a repository.

Here is how I would think about the distinction:

These approaches are not necessarily mutually exclusive. A team can use Rovo for some Atlassian-native tasks and external coding agents for repository work.

One thing to watch: Atlassian documents that certain enriched Teamwork Graph calls made through external tools, including its MCP server, consume Rovo credits. If you want to avoid additional Rovo usage, check whether your chosen workflow invokes those credit-consuming services. A standard Jira synchronization workflow and a workflow that requests enriched Rovo context are not the same thing.

Common Mistakes to Avoid

Giving an agent a vague ticket

A vague issue often produces a vague implementation. Write acceptance criteria and define the expected result before assigning the work.

Starting with a high-risk task

Test the integration on a small, reversible change first. Confirm that repository access, tests, and review work as expected.

Giving the agent excessive permissions

Use a dedicated development environment where possible, and avoid exposing production credentials or unnecessary access.

Assuming synchronization means everything is automatic

Connecting Jira does not automatically import all historical data or guarantee that every event and status transition behaves exactly as your team expects. Review the synchronization settings and test them.

Treating generated code as production-ready

Agents can make mistakes, misunderstand requirements, or introduce regressions. Keep code review, testing, and release decisions in the hands of responsible team members.

Confusing free coordination with free model usage

A free platform plan does not necessarily include the cost of an AI model. Check which subscriptions, API keys, infrastructure, and usage limits apply to your setup.

Frequently Asked Questions

Can I use AI coding agents with Jira without paying for Rovo credits?

Yes, you can use an external coding-agent workflow connected to Jira without necessarily invoking Rovo's credit-consuming AI features. For example, Sharkly can connect Jira tasks to supported coding agents that use your existing subscriptions or API accounts. Avoiding Rovo credits depends on the integrations and features you actually use.

Is Sharkly free to use with Jira?

Sharkly's published pricing says it is free for teams of up to 10 people. Model usage remains with the coding plans or API accounts configured for your agents. Check Sharkly's website for the latest pricing and terms.

Do I have to move my tickets out of Jira?

No. You can connect a Jira project to a Sharkly Space and configure synchronization. You can import historical issues if needed, but you do not have to migrate your entire backlog just to try the workflow.

Which AI agents can I use?

Sharkly supports multiple coding-agent runtimes, including Claude Code and Codex, alongside other supported environments. Confirm that the runtime you want to use is supported and configured on your connected computer.

Can AI agents create pull requests from Jira tickets?

An agent can implement a task and create a pull request when its runtime, repository access, and workflow are configured to support that action. With Sharkly's GitHub integration, pull requests can be linked back to their tasks. The result should still be reviewed by a person.

Can multiple agents work on Jira tickets at the same time?

Yes, with an appropriate setup. Assigning independent tasks to different agents and running them in isolated workspaces can reduce interference. You should still account for compute limits, agent usage, dependencies between tickets, and the review workload.

Final Thoughts

I think one of the most useful ways to bring AI into software development is to start with the workflow a team already uses.

If your team manages its work in Jira, you do not necessarily need to replace that system or pay for another AI usage model just to start experimenting with coding agents.

By connecting Jira to a tool such as Sharkly, you can assign selected issues to existing coding agents, keep execution tied to the original task, and review the resulting changes before they are merged.

The important distinction is that Sharkly coordinates the work while the coding agent performs it. Your existing model subscription or API account still handles model usage, and your team remains responsible for the quality and acceptance of the result.

I would start with one Jira project, one connected agent, and one low-risk ticket. Once the workflow is reliable, expand to more tasks and agents based on what your team actually needs.

That is a practical way to explore AI-assisted development without making a bigger investment in new AI tooling before you know whether the workflow delivers value.

5 Best LLM Gateways

Emmanuel Mumba — Wed, 21 Oct 2026 10:00:36 +0000

As AI applications move from prototypes into production, managing model access becomes a different kind of engineering problem.

At first, connecting an application to OpenAI or Anthropic is relatively straightforward. But as a project grows, teams may need to support multiple providers, control token spending, handle rate limits, recover from provider outages, and monitor how models are being used across different applications.

Building all of that independently can create unnecessary complexity.

This is where an LLM gateway becomes useful. It provides a central layer between applications and model providers, helping teams manage AI traffic without implementing the same infrastructure separately in every project.

I've been looking at how different gateways approach this problem, and one thing stands out: not every gateway is designed for the same environment. Some focus on model access and routing, while others emphasize enterprise governance, API management, or operational visibility.

In this article, I'll compare five LLM gateways worth considering in 2026: Bifrost, LiteLLM, Kong AI Gateway, Cloudflare AI Gateway, and OpenRouter.

TL;DR

Bifrost: A strong option for enterprises that need low-overhead model routing, centralized governance, and integrated MCP capabilities. It also offers virtual keys and other controls for managing AI usage.
LiteLLM: A flexible, open-source option for accessing multiple LLM providers through a common interface, with proxy and cost-management features.
Kong AI Gateway: Best suited to organizations that want to extend existing API infrastructure and governance practices to AI workloads.
Cloudflare AI Gateway: A practical choice for teams looking for AI traffic analytics, caching, rate limiting, and retries within the Cloudflare ecosystem.
OpenRouter: Useful for developers who want a straightforward way to access models from different providers through a unified API.

The right choice depends on whether you prioritize enterprise controls, model flexibility, existing infrastructure, or a managed way to access a broad model catalog.

What Is an LLM Gateway?

An LLM gateway is an intermediary layer between an application and one or more large language model providers. Instead of having every application manage provider-specific endpoints, credentials, routing logic, and usage tracking independently, teams can centralize these responsibilities.

For example, an application might send requests to a gateway rather than directly to a model provider. The gateway then routes the request to the configured model, applies relevant policies, and returns the response.

Depending on the platform, this layer may also handle:

Model routing and fallback: Direct requests to suitable providers and recover from certain failures.
Cost management: Track usage, set budgets, and attribute spending to teams or applications.
Authentication and governance: Manage credentials, access permissions, and rate limits.
Observability: Monitor requests, token consumption, latency, and errors.
Caching: Reuse eligible responses to reduce repeated model calls and costs.
MCP and agent integrations: Manage access to tools and services used by AI agents, where supported.

These capabilities can simplify production operations, but the depth of implementation varies considerably between gateways.

How to Evaluate an LLM Gateway

Before comparing individual products, I would look at five practical considerations.

Evaluation criterion	What to look for	Why it matters
Provider support	Compatibility with the models and APIs you use	Reduces integration work and provider lock-in
Routing and reliability	Fallbacks, load balancing, and provider selection	Helps applications handle failures and changing workloads
Governance	Authentication, budgets, permissions, and rate limits	Makes AI usage easier to control across teams
Observability	Request logs, latency, errors, and cost reporting	Helps teams troubleshoot and optimize production systems
Deployment	Managed, self-hosted, or hybrid options	Determines how well the gateway fits security and infrastructure requirements

Performance also deserves attention, particularly when an application makes multiple sequential model calls. Published latency numbers are only useful when the benchmark methodology, hardware, workload, and request volume are understood.

1. Bifrost: Enterprise AI Routing and Governance

Bifrost is an AI gateway developed by Maxim AI that focuses on routing, governing, and observing AI traffic across multiple providers. It offers an OpenAI-compatible interface, allowing teams to integrate supported models without building a separate connection for each provider.

Its architecture is particularly relevant for teams that need an operational control layer rather than just a convenient way to switch models.

What stands out

Bifrost combines several capabilities in one platform:

Multi-provider routing: Access supported providers through a common gateway and configure routing or fallback behavior.
Performance focus: Bifrost is written in Go, and its maintainers publish benchmarks reporting low gateway overhead under specified test conditions. Teams can review the Bifrost benchmarking documentation to understand the testing approach.
Enterprise governance: Virtual keys, access profiles, budgets, and rate limits help teams manage usage across consumers and applications. Its budget and limits controls provide additional options for managing spending and request volume.
MCP integration: Its MCP gateway capabilities extend management beyond model requests to interactions with external tools.
Operational visibility: Request logs, analytics, and administrative controls help teams understand how AI infrastructure is being used.

The combination is useful when a company wants model routing and governance in the same infrastructure layer. For organizations running AI agents, the ability to manage model requests and MCP tool access can also reduce the need to operate disconnected control systems.

Bifrost is open source, and its GitHub repository provides implementation details for teams evaluating the architecture or considering self-hosted deployment.

Deployment requirements are another consideration. Organizations with specific network restrictions can investigate in-VPC deployment or air-gapped deployment, depending on their infrastructure and security needs.

Best for

Bifrost is worth considering for enterprises that need centralized control over model access, spending, and AI tool interactions. It is especially relevant when performance, deployment control, and MCP governance are important alongside multi-provider routing.

Teams should still validate published performance claims against their own workloads and evaluate which enterprise capabilities they require.

2. LiteLLM: A Flexible Open-Source LLM Proxy

LiteLLM is an open-source project that provides a consistent interface for working with a wide range of LLM providers. It offers a Python SDK and a proxy server that teams can deploy as their own gateway.

This makes it useful for engineering teams that want to standardize model access without committing every application to a single provider. Developers can integrate through a common interface while managing provider connections and gateway configuration centrally.

What stands out

LiteLLM offers several features for teams building their own AI infrastructure:

Unified provider interface: Use a consistent API across supported model providers.
SDK and proxy options: Choose between integrating the Python SDK or running a gateway service.
Authentication and virtual keys: Centralize credentials and control access to configured deployments.
Usage and cost tracking: Monitor model usage and associate costs with different consumers.
Routing and fallbacks: Configure how requests are distributed across supported deployments and how failures are handled.

One of LiteLLM's main advantages is flexibility. Teams can inspect the implementation, customize their configuration, and deploy the proxy in infrastructure they control.

That flexibility also means the team must plan for maintenance, monitoring, scaling, and security. The amount of operational work depends on the architecture and deployment.

Explore the LiteLLM documentation for its SDK, proxy configuration, and supported integrations.

Best for

LiteLLM is best suited for developers and engineering teams that want an open-source, customizable gateway with broad provider compatibility. It is particularly useful for teams that prefer to manage their own infrastructure and want centralized authentication, routing, and cost visibility.

3. Kong AI Gateway: Extending API Management to AI

Kong AI Gateway extends the broader Kong Gateway platform with AI-specific routing, security, and observability capabilities.

For organizations that already use Kong to manage APIs, this can be a natural way to bring AI traffic into an existing governance model instead of introducing an entirely separate infrastructure layer.

What stands out

Kong's approach builds on established API management concepts:

Provider abstraction: Route requests to supported LLM providers through a common layer.
Centralized credentials: Manage provider access rather than distributing credentials across applications.
Routing and load balancing: Configure how AI requests are sent to upstream services.
Security policies: Apply authentication and access controls through the gateway.
AI observability: Track request activity and usage through the platform's monitoring capabilities.
MCP support: Its AI Gateway documentation includes MCP server management and related controls.

A major advantage is that AI traffic can be managed alongside existing API infrastructure. Teams may already have deployment processes, security policies, and operational expertise around Kong, reducing the need to establish a separate platform.

However, organizations that do not already use Kong should assess the learning curve and infrastructure overhead. They should also check which AI-specific capabilities are available in their chosen edition.

Best for

Kong AI Gateway is particularly relevant to enterprises that already rely on Kong and want to extend their API management practices to LLMs, MCP servers, and AI agents. It is less compelling to adopt solely for AI routing if the organization has no existing need for the broader Kong platform.

4. Cloudflare AI Gateway: AI Traffic Management at the Edge

Cloudflare AI Gateway provides a managed layer for observing and controlling AI application traffic. It fits naturally into environments where Cloudflare is already part of the application's networking or security infrastructure.

Rather than requiring teams to build their own monitoring and traffic-management components, it offers a set of features designed to make AI requests easier to observe and control.

What stands out

Cloudflare AI Gateway includes capabilities such as:

Analytics and logging: Inspect request activity, token usage, errors, and costs.
Caching: Serve eligible repeated requests from cache.
Rate limiting: Control request volume within configured limits.
Retries and fallbacks: Improve resilience when requests fail, where configured and supported.
Provider connectivity: Work with supported AI providers through the gateway.

Its integration with the wider Cloudflare ecosystem is a practical benefit for teams already using its infrastructure. The managed model also reduces the burden of operating a separate gateway service.

There are trade-offs. Teams must evaluate the service's deployment model, data handling, logging retention, and governance capabilities against their requirements. Organizations that need to operate the gateway entirely within their own infrastructure should pay particular attention to deployment constraints.

Best for

Cloudflare AI Gateway is a sensible option for teams already using Cloudflare that want visibility and basic traffic controls without managing another self-hosted service. It is particularly attractive when analytics, caching, rate limiting, and integration with existing infrastructure are the main priorities.

5. OpenRouter: Unified Access to a Broad Model Catalog

OpenRouter focuses on making a broad catalog of AI models accessible through a unified API. Instead of integrating separately with each supported provider, developers can use one interface to experiment with and access different models.

This approach is useful when teams are comparing models, building prototypes, or developing applications that may need to switch between different model families over time.

What stands out

OpenRouter emphasizes convenient access to a broad model ecosystem:

Broad model catalog: Discover and access models from different providers through one interface.
Unified API: Reduce the need to build separate integrations for each supported model.
Model selection: Choose models according to the capabilities and requirements of an application.
Provider routing: Use supported routing options to select among available provider routes.
Fallback capabilities: Configure supported alternatives for handling certain provider availability issues.

The main attraction is the ability to experiment with different models without maintaining a separate integration for every provider. This can simplify early development and make model comparisons easier.

OpenRouter is primarily a managed model-access service, so teams with strict requirements around private deployment, data handling, or internal infrastructure control should review whether its operating model fits their needs.

Explore the OpenRouter model catalog to see the available models and provider options.

Best for

OpenRouter is best suited for developers who want convenient access to a broad selection of models through one API. It is particularly useful for model experimentation, comparing different options, and building applications where flexibility in model selection is a priority.

How the Five LLM Gateways Compare

The table below summarizes their primary positioning. Feature availability can vary by edition, deployment, and configuration, so use it as a starting point rather than a substitute for checking current documentation.

Gateway	Primary strength	Routing	Governance and cost controls	Deployment approach
Bifrost	Enterprise AI gateway with MCP capabilities	Multi-provider routing and fallback	Virtual keys, budgets, access controls	Open-source and self-hosted options; enterprise deployment options
LiteLLM	Flexible multi-provider proxy	Provider and model routing	Usage tracking, budgets, rate limits	Open-source, self-hosted proxy
Kong AI Gateway	API management extended to AI	AI proxy and configurable routing	API policies and AI governance features	Self-hosted and managed options
Cloudflare AI Gateway	Managed AI traffic management	Provider connectivity and configured fallbacks	Analytics, rate limiting, usage visibility	Managed Cloudflare service
OpenRouter	Broad model access	Provider selection and fallback options	Centralized access and billing; assess enterprise requirements	Managed service

Comparing Them by Use Case

Sometimes it is easier to choose a gateway by starting with the problem you need to solve.

Your main requirement	Gateway to evaluate	Why
Monitoring and governing AI requests	Bifrost	Combines model routing with governance, usage controls, observability, and MCP gateway capabilities
Open-source gateway with customizable deployment	Bifrost or LiteLLM	Both provide open-source options; compare their routing, governance, ecosystem, and deployment requirements
AI traffic managed through existing API infrastructure	Kong AI Gateway	Extends Kong's established API management approach to AI
Managed analytics and traffic controls in a Cloudflare environment	Cloudflare AI Gateway	Provides caching, rate limiting, logs, and request resilience features
Easy access to many models for experimentation	OpenRouter	Centralizes model discovery and access across providers

The important distinction is that these products overlap without being interchangeable. A gateway that makes model experimentation easy may not provide the deployment control or governance depth an enterprise needs. Likewise, an organization already operating a mature API platform may value integration with its existing infrastructure more than a separate model-routing interface.

What Makes an LLM Gateway Production-Ready?

A gateway can simplify development, but production readiness depends on how well it handles the realities of running AI workloads.

Reliability: Provider failures and rate limits are inevitable. Understand how the gateway handles retries and fallback behavior, and whether it can route requests to suitable alternatives without creating unexpected costs or changing application behavior.

Performance: Measure the gateway's overhead under realistic traffic. Consider not only average latency but also tail latency, throughput, streaming behavior, and the impact of multiple sequential calls in agentic workflows.

Governance: Teams need to know who is using models, how much they are spending, and which policies apply. Look for controls that match your organizational structure, including per-user or per-team limits where required.

Observability: Request-level visibility helps developers investigate failures and understand usage patterns. Check which metrics, logs, and integrations are available, and how sensitive prompt or response data is handled.

Deployment and security: Consider where requests are processed, how credentials are stored, what data is retained, and whether the deployment model satisfies internal security and compliance requirements.

A feature checklist is useful, but a small proof of concept is often more revealing. Test the gateway using the models, request patterns, traffic levels, and policies your application will actually use.

Frequently Asked Questions

What is the difference between an LLM gateway and an API gateway?

A traditional API gateway manages API traffic through functions such as authentication, routing, and rate limiting. An LLM gateway adds model-oriented capabilities such as provider selection, token usage tracking, model-specific fallback behavior, and AI cost management.

Do I need an LLM gateway if I use only one model provider?

Not necessarily. A direct integration may be sufficient for a small application, but a gateway can still help centralize logging, budgets, authentication, and rate limits. The decision depends on whether those operational benefits justify another layer in your architecture.

Which LLM gateways support open-source or self-hosted deployment?

Bifrost and LiteLLM provide open-source options, while Kong offers open-source gateway components alongside additional commercial capabilities. Cloudflare AI Gateway and OpenRouter are primarily managed services, so verify each platform's current deployment options and licensing before deciding.

Can an LLM gateway reduce AI costs?

It can help by providing usage visibility, enforcing budgets, routing requests to suitable models, and caching eligible responses. Actual savings depend on the workload, provider pricing, cache hit rates, and how routing policies are configured.

Can an LLM gateway manage MCP traffic as well as LLM requests?

Some platforms support both, but their capabilities differ. Bifrost and Kong document MCP gateway functionality, while teams evaluating LiteLLM or other gateways should check the current scope of their MCP support and whether it provides the controls they need.

How should I choose the best LLM gateway for my team?

Start by identifying your biggest requirement: model flexibility, production reliability, governance, deployment control, or integration with existing infrastructure. Then compare the relevant capabilities, validate any important performance claims, and test your leading options against a representative workload.

Final Thoughts

Choosing an LLM gateway is less about finding a universally superior product and more about finding the right operational fit.

LiteLLM offers a flexible route for teams that want an open-source proxy, Kong makes sense when AI traffic needs to fit into an established API management environment, Cloudflare AI Gateway simplifies traffic management for Cloudflare users, and OpenRouter makes access to a broad model catalog straightforward.

Bifrost is particularly worth evaluating when the requirement extends beyond model access to enterprise governance, performance, and MCP tool management in a unified gateway.

Before committing, test the options against your actual workload and confirm that their security, deployment, and governance features meet your needs. If you'd like to see how Bifrost could fit into your AI infrastructure, book a demo with the Bifrost team.

5 Best LLM Gateways

Emmanuel Mumba — Wed, 21 Oct 2026 07:07:05 +0000

As AI applications move from prototypes into production, managing model access becomes a different kind of engineering problem.

Building all of that independently can create unnecessary complexity.

In this article, I'll compare five LLM gateways worth considering in 2026: Bifrost, LiteLLM, Kong AI Gateway, Cloudflare AI Gateway, and OpenRouter.

TL;DR

Bifrost: A strong option for enterprises that need low-overhead model routing, centralized governance, and integrated MCP capabilities. It also offers virtual keys and other controls for managing AI usage.
LiteLLM: A flexible, open-source option for accessing multiple LLM providers through a common interface, with proxy and cost-management features.
Kong AI Gateway: Best suited to organizations that want to extend existing API infrastructure and governance practices to AI workloads.
Cloudflare AI Gateway: A practical choice for teams looking for AI traffic analytics, caching, rate limiting, and retries within the Cloudflare ecosystem.
OpenRouter: Useful for developers who want a straightforward way to access models from different providers through a unified API.

The right choice depends on whether you prioritize enterprise controls, model flexibility, existing infrastructure, or a managed way to access a broad model catalog.

What Is an LLM Gateway?

Depending on the platform, this layer may also handle:

Model routing and fallback: Direct requests to suitable providers and recover from certain failures.
Cost management: Track usage, set budgets, and attribute spending to teams or applications.
Authentication and governance: Manage credentials, access permissions, and rate limits.
Observability: Monitor requests, token consumption, latency, and errors.
Caching: Reuse eligible responses to reduce repeated model calls and costs.
MCP and agent integrations: Manage access to tools and services used by AI agents, where supported.

These capabilities can simplify production operations, but the depth of implementation varies considerably between gateways.

How to Evaluate an LLM Gateway

Before comparing individual products, I would look at five practical considerations.

Evaluation criterion	What to look for	Why it matters
Provider support	Compatibility with the models and APIs you use	Reduces integration work and provider lock-in
Routing and reliability	Fallbacks, load balancing, and provider selection	Helps applications handle failures and changing workloads
Governance	Authentication, budgets, permissions, and rate limits	Makes AI usage easier to control across teams
Observability	Request logs, latency, errors, and cost reporting	Helps teams troubleshoot and optimize production systems
Deployment	Managed, self-hosted, or hybrid options	Determines how well the gateway fits security and infrastructure requirements

1. Bifrost: Enterprise AI Routing and Governance

Its architecture is particularly relevant for teams that need an operational control layer rather than just a convenient way to switch models.

What stands out

Bifrost combines several capabilities in one platform:

Multi-provider routing: Access supported providers through a common gateway and configure routing or fallback behavior.
Performance focus: Bifrost is written in Go, and its maintainers publish benchmarks reporting low gateway overhead under specified test conditions. Teams can review the Bifrost benchmarking documentation to understand the testing approach.
Enterprise governance: Virtual keys, access profiles, budgets, and rate limits help teams manage usage across consumers and applications. Its budget and limits controls provide additional options for managing spending and request volume.
MCP integration: Its MCP gateway capabilities extend management beyond model requests to interactions with external tools.
Operational visibility: Request logs, analytics, and administrative controls help teams understand how AI infrastructure is being used.

Bifrost is open source, and its GitHub repository provides implementation details for teams evaluating the architecture or considering self-hosted deployment.

Best for

Teams should still validate published performance claims against their own workloads and evaluate which enterprise capabilities they require.

2. LiteLLM: A Flexible Open-Source LLM Proxy

What stands out

LiteLLM offers several features for teams building their own AI infrastructure:

Unified provider interface: Use a consistent API across supported model providers.
SDK and proxy options: Choose between integrating the Python SDK or running a gateway service.
Authentication and virtual keys: Centralize credentials and control access to configured deployments.
Usage and cost tracking: Monitor model usage and associate costs with different consumers.
Routing and fallbacks: Configure how requests are distributed across supported deployments and how failures are handled.

One of LiteLLM's main advantages is flexibility. Teams can inspect the implementation, customize their configuration, and deploy the proxy in infrastructure they control.

That flexibility also means the team must plan for maintenance, monitoring, scaling, and security. The amount of operational work depends on the architecture and deployment.

Explore the LiteLLM documentation for its SDK, proxy configuration, and supported integrations.

Best for

3. Kong AI Gateway: Extending API Management to AI

Kong AI Gateway extends the broader Kong Gateway platform with AI-specific routing, security, and observability capabilities.

What stands out

Kong's approach builds on established API management concepts:

Provider abstraction: Route requests to supported LLM providers through a common layer.
Centralized credentials: Manage provider access rather than distributing credentials across applications.
Routing and load balancing: Configure how AI requests are sent to upstream services.
Security policies: Apply authentication and access controls through the gateway.
AI observability: Track request activity and usage through the platform's monitoring capabilities.
MCP support: Its AI Gateway documentation includes MCP server management and related controls.

Best for

4. Cloudflare AI Gateway: AI Traffic Management at the Edge

Rather than requiring teams to build their own monitoring and traffic-management components, it offers a set of features designed to make AI requests easier to observe and control.

What stands out

Cloudflare AI Gateway includes capabilities such as:

Analytics and logging: Inspect request activity, token usage, errors, and costs.
Caching: Serve eligible repeated requests from cache.
Rate limiting: Control request volume within configured limits.
Retries and fallbacks: Improve resilience when requests fail, where configured and supported.
Provider connectivity: Work with supported AI providers through the gateway.

Best for

5. OpenRouter: Unified Access to a Broad Model Catalog

This approach is useful when teams are comparing models, building prototypes, or developing applications that may need to switch between different model families over time.

What stands out

OpenRouter emphasizes convenient access to a broad model ecosystem:

Broad model catalog: Discover and access models from different providers through one interface.
Unified API: Reduce the need to build separate integrations for each supported model.
Model selection: Choose models according to the capabilities and requirements of an application.
Provider routing: Use supported routing options to select among available provider routes.
Fallback capabilities: Configure supported alternatives for handling certain provider availability issues.

Explore the OpenRouter model catalog to see the available models and provider options.

Best for

How the Five LLM Gateways Compare

Gateway	Primary strength	Routing	Governance and cost controls	Deployment approach
Bifrost	Enterprise AI gateway with MCP capabilities	Multi-provider routing and fallback	Virtual keys, budgets, access controls	Open-source and self-hosted options; enterprise deployment options
LiteLLM	Flexible multi-provider proxy	Provider and model routing	Usage tracking, budgets, rate limits	Open-source, self-hosted proxy
Kong AI Gateway	API management extended to AI	AI proxy and configurable routing	API policies and AI governance features	Self-hosted and managed options
Cloudflare AI Gateway	Managed AI traffic management	Provider connectivity and configured fallbacks	Analytics, rate limiting, usage visibility	Managed Cloudflare service
OpenRouter	Broad model access	Provider selection and fallback options	Centralized access and billing; assess enterprise requirements	Managed service

Comparing Them by Use Case

Sometimes it is easier to choose a gateway by starting with the problem you need to solve.

Your main requirement	Gateway to evaluate	Why
Monitoring and governing AI requests	Bifrost	Combines model routing with governance, usage controls, observability, and MCP gateway capabilities
Open-source gateway with customizable deployment	Bifrost or LiteLLM	Both provide open-source options; compare their routing, governance, ecosystem, and deployment requirements
AI traffic managed through existing API infrastructure	Kong AI Gateway	Extends Kong's established API management approach to AI
Managed analytics and traffic controls in a Cloudflare environment	Cloudflare AI Gateway	Provides caching, rate limiting, logs, and request resilience features
Easy access to many models for experimentation	OpenRouter	Centralizes model discovery and access across providers

What Makes an LLM Gateway Production-Ready?

A gateway can simplify development, but production readiness depends on how well it handles the realities of running AI workloads.

Frequently Asked Questions

What is the difference between an LLM gateway and an API gateway?

Do I need an LLM gateway if I use only one model provider?

Which LLM gateways support open-source or self-hosted deployment?

Can an LLM gateway reduce AI costs?

Can an LLM gateway manage MCP traffic as well as LLM requests?

How should I choose the best LLM gateway for my team?

Final Thoughts

Choosing an LLM gateway is less about finding a universally superior product and more about finding the right operational fit.

Bifrost is particularly worth evaluating when the requirement extends beyond model access to enterprise governance, performance, and MCP tool management in a unified gateway.

AI Agent Governance Tools and Platforms in 2026

Emmanuel Mumba — Tue, 20 Oct 2026 14:09:37 +0000

I’ve been looking at how AI agents are changing the way teams build software and manage their daily work, and one thing stands out to me: giving an AI agent the ability to do something is easy compared with making sure it does the right thing.

An agent might be able to read a codebase, update files, call APIs, access company data, or execute commands. But should it have permission to do all those things? Who checks its actions? And what happens when it makes a decision that nobody expected?

These questions are becoming more important as companies move beyond AI assistants that simply answer questions and start using agents that can take action with less human involvement.

That is where AI agent governance comes in. It covers everything from managing agent identities and permissions to monitoring actions, enforcing policies, tracking activity, and deciding when a human needs to step in.

I wanted to look at the tools that help organizations manage these risks, from enterprise identity and security platforms to specialized AI governance solutions and tools that coordinate agents within engineering workflows.

Here are six AI agent governance tools and platforms worth considering in 2026, what each one does, and how to decide which approach makes sense for your team.

What Is AI Agent Governance?

AI agent governance is the set of policies, controls, and processes used to ensure that AI agents operate within approved boundaries.

Traditional AI governance often focuses on model performance, fairness, transparency, privacy, and regulatory compliance. Agent governance adds another layer because agents can take actions, use tools, access resources, and sometimes delegate work to other agents.

For example, imagine an AI coding agent assigned to fix a bug. It might need permission to read a repository, modify specific files, and run tests. It probably does not need unrestricted access to production credentials or permission to deploy directly to production.

A governance framework helps define those boundaries and establish what happens when an agent reaches them.

In practice, I would break agent governance into six areas:

Identity and access: Knowing which agent is acting and what resources it can access.
Policy enforcement: Defining which actions are allowed, restricted, or require approval.
Monitoring and auditability: Recording what an agent did, which tools it used, and what results it produced.
Risk management: Identifying potentially harmful behavior, excessive permissions, and policy violations.
Human oversight: Ensuring that people can review, approve, reject, or stop important actions.
Lifecycle management: Tracking agents from creation and deployment through updates, suspension, and retirement.

Not every platform covers all six areas. Some focus on enterprise security and compliance, while others concentrate on controlling tool calls or coordinating the actual work agents perform.

Understanding that distinction is important when comparing the available options.

1. Microsoft Entra Agent ID: Best for Agent Identity and Access Governance

When an organization already uses Microsoft Entra for identity and access management, extending those controls to AI agents is a logical starting point.

Microsoft Entra Agent ID provides identity capabilities designed specifically for AI agents. It helps organizations establish agent identities, manage their lifecycle, govern access, and apply security controls.

The central idea is straightforward: an AI agent should not be treated as an anonymous process with unrestricted access to company resources. Its identity, ownership, and permissions should be managed deliberately.

Microsoft's approach includes agent identity management, access governance, Conditional Access, identity protection, and monitoring capabilities. The available features depend on the particular Microsoft services and licensing in use.

This becomes particularly useful when a company has many agents operating across different applications and departments. Without a consistent identity model, it can become difficult to determine which agent accessed a resource or who is responsible for that agent.

Key capabilities:

Create and manage identities for AI agents.
Establish ownership and accountability.
Govern access to applications and resources.
Apply identity-based security policies.
Monitor agent activity and manage agent lifecycles.

Best for: Organizations that need centralized identity, permissions, and lifecycle controls for agents across their enterprise.

Considerations: Agent identity governance is only one part of the overall picture. Teams may still need separate tools for model evaluation, application-level policy enforcement, workflow orchestration, and engineering task management.

Website: Microsoft Entra Agent ID

2. Amazon Bedrock AgentCore: Best for Runtime Controls and Tool Permissions

An agent can have a valid identity and still perform an action it should not be allowed to perform. That is why identity management needs to be complemented by controls closer to the agent's execution environment.

Amazon Bedrock AgentCore provides infrastructure for building, deploying, and operating AI agents. Its capabilities include runtime environments, identity integration, gateways, and policy enforcement.

One particularly relevant feature is the AgentCore Policy engine. It can evaluate and authorize agent tool calls when configured with a compatible gateway, helping teams enforce rules about which actions an agent can take.

Consider an agent that needs to retrieve customer information through an internal tool. Rather than trusting the agent to follow its instructions every time, a team can establish policies that constrain which actions and resources it may access.

This is an important distinction: instructions tell an agent how it should behave, while enforced policies can place technical restrictions on what it is permitted to do.

Key capabilities:

Run agents in managed execution environments.
Connect agents to tools and services through gateways.
Apply policies to supported agent tool calls.
Integrate authentication and authorization controls.
Configure access to specific resources.

Best for: Developers building agent applications on AWS who need infrastructure-level controls and policy enforcement.

Considerations: AgentCore is an infrastructure and developer platform, not simply a governance dashboard. Teams need to configure policies, identity, and integrations for their particular architecture. It is most attractive when its execution and infrastructure capabilities fit the existing stack.

Website: Amazon Bedrock AgentCore

3. Sharkly: Best for Coordinating and Governing AI Agent Workflows

When I think about AI agent governance, I don't think only about enterprise compliance policies or access permissions. I also think about what happens when agents start performing actual work across an engineering team.

Who assigned the task? What instructions did the agent receive? Is it still running? What changes did it make? And how can someone review the result before it becomes part of the codebase?

These are practical questions that become harder to answer when agents operate through disconnected sessions and developers have to track everything manually.

That is where Sharkly becomes relevant.

Sharkly connects task management with AI agent execution. Teams can organize engineering work, assign tasks to agents, track progress, and review results within a shared workflow. Rather than treating every agent session as an isolated activity, the platform connects execution to the task the team is trying to complete.

For example, a team could assign an agent to investigate a bug, with the task containing the problem description, requirements, and relevant discussion. The agent can execute the assignment, while its progress and results are associated with the task so the team can inspect what happened.

Sharkly also supports isolated working directories and Git worktrees for supported repository workflows. This helps reduce interference when multiple agents are modifying code concurrently. Workspace isolation does not eliminate every merge conflict, but it gives teams a more controlled way to run independent tasks.

Another useful aspect is compatibility with existing coding agents, including Claude Code and Codex, alongside other supported agent environments. Teams can incorporate agent execution into their workflow without necessarily replacing the tools they already use.

Sharkly also supports Jira integration, including importing issues and synchronizing work during migration. This is useful for teams that want to connect AI-assisted development to an existing issue-tracking process instead of rebuilding their entire workflow.

Key capabilities:

Assign engineering tasks to AI agents.
Keep task context, discussions, progress, and results together.
Track execution states and investigate failed or blocked runs.
Use isolated workspaces for supported repository workflows.
Connect Jira and other tools to existing engineering processes.
Keep people involved in reviewing results and making decisions.

Best for: Engineering teams that want better accountability, execution visibility, and coordination around AI agent work.

Considerations: Sharkly addresses the operational side of agent governance rather than replacing a dedicated enterprise AI risk or compliance platform. Organizations that require centralized agent identity governance, organization-wide regulatory reporting, or universal runtime policy enforcement may need additional controls.

Website: Sharkly

4. Credo AI: Best for Enterprise AI Risk and Compliance Governance

For some organizations, the biggest governance challenge is not controlling an individual tool call. It is demonstrating that AI systems across the business meet internal policies, risk requirements, and applicable regulations.

That is the area addressed by Credo AI.

Credo AI focuses on AI governance, risk management, and compliance. Its platform helps organizations assess AI systems, establish governance policies, manage risks, and collect evidence that supports oversight.

Its approach is particularly relevant for organizations that need to manage AI deployments across multiple departments, rather than evaluating every application in isolation.

For example, a company may need to establish which AI systems are deployed, what risks they introduce, who is accountable for them, and whether required reviews have been completed. A governance platform can help bring those responsibilities into a repeatable process.

As organizations introduce more agentic systems, the governance process also needs to account for how agents use tools, interact with other systems, and act within the permissions they receive.

Key capabilities:

AI system and agent discovery, where supported.
Risk assessment and governance workflows.
Policy and compliance management.
Risk visibility and monitoring capabilities.
Audit evidence and accountability processes.

Best for: Enterprises managing AI risk across multiple teams, applications, and regulatory requirements.

Considerations: A governance platform does not automatically secure every agent or enforce every runtime restriction. Organizations should verify which controls are enforced directly and which depend on integrations, monitoring, or organizational processes.

Website: Credo AI

5. LangSmith: Best for Agent Tracing, Evaluation, and Observability

You cannot govern an agent effectively if you cannot understand what it is doing.

When an agent produces an unexpected result, developers need more than the final answer. They may need to inspect the sequence of model calls, tool invocations, intermediate steps, and errors that led to that result.

LangSmith provides tracing, observability, evaluation, and testing capabilities for applications built with language models and agents.

For developers, this makes it useful for investigating failures and measuring how agent behavior changes as prompts, models, tools, or workflows are updated.

For example, suppose a customer-support agent repeatedly provides incorrect answers after calling an internal knowledge tool. Tracing can help a team investigate whether the problem originated in the retrieved information, the tool response, or the agent's subsequent processing.

Evaluation workflows can also help teams test agent behavior against defined criteria before and after changes.

I would distinguish this from a complete enterprise governance platform. Observability gives teams visibility into behavior, but visibility alone does not guarantee that a risky action will be blocked.

Key capabilities:

Trace agent execution and tool interactions.
Debug multi-step workflows.
Evaluate outputs against defined criteria.
Test changes to prompts and agent configurations.
Monitor application behavior during development and operation.

Best for: Engineering teams building AI agents that need detailed execution traces, testing, and ongoing evaluation.

Considerations: Teams may need additional identity, access-control, policy-enforcement, and compliance tools. The available capabilities depend on the application architecture and the LangSmith plan.

Website: LangSmith

6. IBM watsonx.governance: Best for Structured AI Governance Programs

Organizations deploying AI across multiple business functions may need a consistent way to manage risks, document evaluations, and oversee AI systems throughout their lifecycle.

IBM watsonx.governance is designed for AI governance, risk management, and compliance. It supports governance workflows around AI systems, including monitoring and documentation intended to help organizations manage their AI portfolios.

This makes it relevant for teams that need repeatable governance processes rather than handling every model or application as a separate case.

For example, an organization may require AI systems to undergo risk assessments, document their intended use, complete evaluations, and maintain evidence of ongoing monitoring. A structured governance platform can help coordinate those requirements.

When evaluating it for agentic AI, however, I would verify the exact agent-specific capabilities available in the current product configuration. Governance requirements for autonomous agents can extend beyond those for conventional predictive models or generative AI applications.

Key capabilities:

Manage AI governance and risk workflows.
Support documentation and oversight across AI systems.
Help organize compliance-related processes.
Monitor relevant AI performance and risk indicators.
Integrate governance activities into enterprise AI operations.

Best for: Enterprises that need a structured AI governance program across a portfolio of models and applications.

Considerations: Confirm that the specific agent monitoring, runtime controls, and integrations your organization requires are supported by the products and plans being evaluated.

Website: IBM watsonx.governance

Quick Comparison: AI Agent Governance Tools

These platforms address different layers of governance. A tool that provides excellent tracing may not enforce access policies, while a platform that manages agent identities may not organize engineering tasks or evaluate every agent response.

The right combination depends on where your biggest risks lie and how much control your existing infrastructure already provides.

How to Choose the Right AI Agent Governance Platform

I would start by identifying the risk you are actually trying to control.

If your biggest concern is access and permissions

Look at Microsoft Entra Agent ID or the identity and authorization features available in your existing cloud platform. The goal is to ensure that agents have identifiable owners and only the access they need.

If agents can take actions through tools and APIs

Look at runtime controls and policy enforcement, including Amazon Bedrock AgentCore if it fits your architecture. You want technical restrictions that can block prohibited actions, not just instructions asking an agent to behave responsibly.

If compliance and audit evidence are the priority

Evaluate platforms such as Credo AI and IBM watsonx.governance. Check how they support your particular governance framework, reporting requirements, risk assessments, and evidence collection.

If developers need to understand agent failures

LangSmith is worth considering for tracing, debugging, and evaluating agent behavior. Pair observability with access controls and enforcement mechanisms where the risk requires them.

If your team needs accountability for coding-agent work

Consider Sharkly when the main challenge is connecting agents to real engineering tasks and making their progress and results visible. It is particularly relevant when your team already uses Jira and wants to incorporate agent execution into its existing workflow.

Best Practices for AI Agent Governance

Choosing a platform is only part of the process. I would establish a few basic practices before allowing agents to take on more consequential work.

1. Give Every Agent a Defined Purpose and Owner

Document what each agent is intended to do, who is responsible for it, and which systems it needs to access. Avoid deploying agents with broad permissions simply because those permissions make the initial setup easier.

2. Apply the Principle of Least Privilege

Give agents only the permissions needed for their assigned tasks. An agent that reviews code should not automatically have permission to deploy to production. An agent that summarizes documents should not automatically have access to every confidential file.

3. Separate Instructions From Enforced Controls

Agent instructions are useful, but they are not a security boundary by themselves. Use access controls, policy engines, restricted environments, and approval gates to enforce important limits.

4. Record Actions and Preserve Context

Keep useful records of agent assignments, tool calls, decisions, outputs, and errors. The exact level of detail should depend on the sensitivity of the work, but teams should be able to investigate important actions rather than relying solely on the final response.

5. Establish Human Approval for High-Impact Actions

Not every action requires manual approval. Low-risk tasks can often be automated. But actions involving production systems, sensitive data, financial transactions, or irreversible changes may need explicit human authorization.

6. Test Governance Controls Before Deployment

Test what happens when an agent requests unauthorized access, receives malicious instructions, encounters unexpected data, or attempts to perform an action outside its assigned scope.

A policy is only useful if it works under realistic conditions.

7. Review Agent Access Over Time

Agents change. Their tasks, integrations, and responsibilities may expand. Review permissions periodically, disable unused agents, and remove access when it is no longer required.

Frequently Asked Questions

What is an AI agent governance platform?

An AI agent governance platform helps organizations manage the risks associated with agents that can take actions, use tools, and access business systems. Depending on the product, it may provide identity controls, policy enforcement, monitoring, risk assessments, audit evidence, or workflow oversight.

What is the difference between AI governance and AI agent governance?

AI governance covers the broader management of AI systems, including risk, transparency, privacy, compliance, and accountability. AI agent governance adds specific concerns around agent identities, tool access, delegated tasks, autonomous actions, and the boundaries within which agents operate.

Can one platform handle all AI agent governance requirements?

Sometimes a platform covers several areas, but organizations should not assume that one product handles everything. Identity management, runtime policy enforcement, observability, compliance reporting, and engineering workflow management are different capabilities and may require separate tools.

How can engineering teams govern AI coding agents?

Engineering teams can start by assigning agents clear tasks, limiting repository and tool permissions, isolating concurrent work, recording execution results, and requiring review before important changes are merged. Tools such as Sharkly can help connect agent execution to task ownership and progress tracking, while other tools provide deeper identity or runtime controls.

Is monitoring enough to govern AI agents?

No. Monitoring helps teams understand what happened, but it may not prevent a harmful action. High-risk workflows should combine monitoring with appropriate access restrictions, enforced policies, and human approval where necessary.

Final Thoughts

I think AI agent governance will become increasingly important as organizations move from experimenting with assistants to delegating meaningful work to autonomous systems.

The challenge is not simply preventing agents from making mistakes. It is building a workflow in which agents have clear responsibilities, appropriate permissions, observable behavior, and well-defined limits on what they can do.

There is no single tool that solves every part of this problem. Microsoft Entra Agent ID focuses on identity and access, Amazon Bedrock AgentCore offers runtime and policy capabilities, Credo AI and IBM watsonx.governance address broader governance requirements, and LangSmith helps developers understand and evaluate agent behavior.

For engineering teams, I would also pay attention to the operational side of governance: who assigned the work, what the agent was asked to do, where it ran, what it changed, and who reviewed the result. Sharkly is worth considering for that layer, especially when teams want to connect agent execution with their existing engineering workflow.

Ultimately, good governance should not make every agent action unnecessarily difficult. It should make the boundaries clear, the risky actions controllable, and the important decisions accountable to people.

That is how teams can make greater use of AI agents without losing sight of security, reliability, and responsibility.

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.