2026 年如何透過 Sharkly 將 Jira 連接至 AI 編碼智能體，避免額外 Rovo 費用

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:

1. Did the agent address the actual requirements in the Jira ticket?
2. Are the code changes limited to the intended scope?
3. Did the relevant tests run, and did they pass?
4. Are there any unexpected changes or dependencies?
5. 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.