Emmanuel Mumba

Posted on Oct 11

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

#ai #productivity #webdev #javascript

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.