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Neither Sharkly nor Jira is established as the universally better project-management tool for AI development teams. Sharkly is built around assigning work to coding agents and reviewing their progress and results alongside team tasks; Jira combines mature project tracking with Atlassian’s expanding Rovo and coding-agent features. The better fit depends on how your team wants agents to work, the controls and integrations it needs, and the full cost of its people, plans, and AI usage.
As of October 25, 2026, Atlassian describes coding-agent workflows in Jira, so a comparison that treats Jira as having no agent capabilities is out of date. Choose by piloting representative work in the system your team could realistically use—not by comparing feature lists or headline prices alone.
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What is the main difference between Sharkly and Jira?
The key difference is how each product connects AI agents to the work-management loop, not whether it can track projects and tasks. Sharkly presents agents as teammates assigned work through its shared work system. Jira is a broader work-tracking platform whose AI features include Rovo functions and, according to Atlassian, workflows for assigning work items to coding agents and reviewing their activity.
That distinction matters most when a team is deciding where task context lives, how agent work is supervised, and whether the tool fits existing planning and administration. It does not establish that one platform produces better code, finishes work faster, or costs less overall. No independent head-to-head performance result or comparable total-cost analysis is established here.
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| Decision area | Sharkly | Jira |
|---|---|---|
| Project and task management | Issues, projects, sprints, views, shared agents, crews, skills, and runtime connections are part of Sharkly’s described product. | Projects, work items, backlogs, boards, views, automation, and wider planning capabilities are part of Jira’s described product. |
| Agent workflow | Sharkly describes assigning an agent to a task, tracking execution and blockers, and returning results for human review. | Atlassian describes Rovo agents and coding-agent assignment and session review in Jira; what is available depends on the organization’s plan, settings, and feature rollout. |
| Integration or migration path | Sharkly documents Jira Cloud and Jira Server/Data Center connections, with import and sync as distinct operations. | Jira can remain the work-tracking system while teams assess an integration or a change in workflow. |
| Price and AI usage | Sharkly lists a free tier and a paid Team price; its model use runs through the coding plan or API account connected to the runtime. | Jira lists Free and paid plans; paid tiers include Rovo features with plan-specific credit allowances, while external services may have separate charges. |
These are product descriptions, not independent verification of quality or results. Sharkly’s execution and review loop is described by Sharkly; Jira’s features and study figures are described by Atlassian. Confirm the exact capabilities in the account and deployment your team would use.
How does Sharkly’s AI-agent workflow work?
Sharkly’s proposition is to place agent execution in the same work system the team uses to plan and track tasks. Its described loop connects a computer or runtime, puts work on a board, assigns an agent, and brings progress and results back to the task for a person to review.
What Sharkly says teams can connect
Sharkly lists coding runtimes including Claude Code, Codex, Gemini CLI, and OpenCode, among others. Its product material also describes issues, projects, sprints, views, shared agents, crews, skills, and connections. That list is an indication of the intended workflow, not proof that every runtime, capability, or configuration will be available in every organization.
Where human review fits
Sharkly describes isolated worktrees and human review before a change is merged. In its model, the task can carry execution status, blockers, and returned results so a teammate can inspect the work rather than treating an agent assignment as an automatic acceptance. These are Sharkly’s product claims; no independent test here establishes agent accuracy, concurrency, or delivery outcomes.
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This approach may suit teams that want to add an agent to a repeatable task without immediately replacing all existing coordination. Before relying on it for production work, clarify which actions the agent can take, what context it receives, how it reports failures, and which person approves changes.
What AI and agent features does Jira have in 2026?
Jira is not simply a traditional task tracker without AI. Atlassian says paid Jira plans include Rovo Search, Chat, and Agents, and its support materials describe AI-assisted work-item search, writing and editing, summaries, natural-language automation, duplicate detection, related work, and suggested fields. Exact access and behavior depend on plan, organization settings, geography, and rollout.
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Rovo agents and configured actions
Atlassian describes Rovo agents as configurable and available through places such as chat, automation rules, editing workflows, and Studio. Depending on configuration and permissions, an agent can take actions such as creating or editing Jira work items. Teams should distinguish an agent feature being present from an action being enabled for their users: configuration and permission boundaries still matter.
Coding-agent assignment and review
Atlassian’s AI-native development page describes assigning work items to coding agents including Claude, Cursor, Codex, GitHub Copilot, and its native Jira Coding Agent. It also describes reviewing agent activity and session history. This means a Jira-dependent team should check its own tenant’s available agent integrations and controls rather than assuming it must leave Jira to explore coding-agent workflows.
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How to interpret Atlassian’s study figures
Atlassian’s Jira for AI-Native Software Development page attributes figures of 44% better agent output and 48% less token usage to its Atlassian Engineering Productivity Study, 2026. These are vendor-published findings, not an independent Sharkly-versus-Jira comparison; they do not show that Jira will improve output or reduce token use by those amounts for a particular team. Treat them as claims to examine in context, not as a forecast for your own workflow.
Which tool fits different AI development teams?
Choose based on the operating model you want to support. A team seeking an agent-first execution loop tied closely to a task board may want to evaluate Sharkly; a team invested in Jira’s workflows, administration, integrations, and Atlassian context may prefer to extend Jira. Neither description guarantees a better result without testing the team’s own work.
Sharkly may be a better fit when
- You want to assign an agent work through a board and keep task status, blockers, results, and human review associated with that work.
- Your team is willing to connect and govern the computer or runtime used for execution.
- You want to try an agent on a bounded, repeatable task before considering a broader change to coordination.
- Your team’s existing project-management workflows are not so dependent on Jira-specific configuration that a new work system would create disproportionate overhead.
Jira may be a better fit when
- Your organization already depends on Jira’s backlog, board, reporting, permissions, automation, or cross-team planning.
- You want to evaluate AI capabilities inside an established work-tracking environment before introducing another system of record.
- Atlassian context and existing integrations are important to developers, administrators, and other teams.
- Your tenant has the Rovo or coding-agent functionality and controls your team needs, subject to its plan and rollout.
Questions that can overturn an apparent fit
- Agent permissions: Which tasks can an agent read or change, who configures those permissions, and what must a human approve?
- Context and audit: Can reviewers see the task context, progress, blockers, session history, outputs, and decisions they need?
- Project-management depth: Which workflows, reporting, backlog and sprint handling, integrations, and administrative controls are essential rather than merely familiar?
- Operations and policy: Is the runtime or hosting arrangement compatible with the organization’s security rules and supported environment?
- System of record: Is the goal to supplement the current tool, synchronize selected work, or move planning and history elsewhere?
What do Sharkly and Jira cost?
The visible prices below were listed on vendor pricing pages checked October 25, 2026. They are not a universal quote: currency, billing selection, location, team size, plan configuration, and later pricing changes can affect what an organization pays.
| Product and plan | Published price or allowance | Important qualification |
|---|---|---|
| Sharkly Free | $0 for organizations with up to 10 people | Sharkly-published plan terms; verify current eligibility and limits. |
| Sharkly Team | $7 per person per month, billed yearly, for organizations over the 10-person limit | Sharkly-published price; confirm the applicable billing terms before purchase. |
| Sharkly self-hosted | Custom priced | Sharkly’s listed pricing; no fixed amount is stated. |
| Jira Free | $0 for up to 10 users | Atlassian’s listed plan; Free does not include Rovo. |
| Jira Standard | $7.91 per user per month | Figure shown on Atlassian’s pricing page when checked; billing options and price calculator apply. Paid Jira plans include Rovo, with the page listing 25 credits per user per month for Standard. |
| Jira Premium | $14.54 per user per month | Figure shown on Atlassian’s pricing page when checked; billing options and price calculator apply. The page listed 70 Rovo credits per user per month for Premium. |
| Jira Enterprise | Contact sales; annual | The pricing page listed 150 Rovo credits per user per month for Enterprise. |
Do not read the listed monthly figures as a direct verdict on affordability. The Sharkly Team figure is explicitly billed yearly, while the Atlassian page provides billing options and a price calculator; a fair calculation must use the team’s actual billing cadence and circumstances. It must also account for what is included, the number of people, and services outside the subscription.
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Sharkly says model usage is through the coding plan or API account already connected to its Computer/Runtime. That usage is therefore a separate part of the team’s cost calculation, not a reason to assume the plan price covers unlimited model work. Jira’s paid tiers include Rovo features and differing listed credit allowances, but a team may also incur charges for integrations or agent services. The listed credits should not be treated as interchangeable with an external coding plan or API allowance unless the relevant product terms establish that relationship.
Atlassian’s page showed $7.91 per user per month for Standard and $14.54 for Premium when checked on October 25, 2026. Those figures can vary with billing period, currency, location, and plan configuration; check the price displayed for the team’s own purchasing context. Sharkly’s published Team price is likewise subject to change.
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Sharkly documents connections for Jira Cloud and Jira Server/Data Center. Its documentation separates importing existing data from syncing selected project changes: connecting an account by itself does not launch either operation, and sync alone does not import historical data.
Import, sync, and history are different jobs
- Import: Copy existing Jira data; Sharkly says history is included where supported.
- Sync: Keep selected project changes connected after the link is configured. The documentation describes directions including bidirectional sync and Jira-to-Sharkly sync.
- Import followed by sync: Sharkly documents this sequence for teams that need existing data brought over as well as an ongoing connection.
Plan the mapping before enabling the connection. Sharkly documents issue and epic links, status mappings, credentials, permissions, and the sync direction. A Jira project can have one active Sharkly Space link, while multiple Jira projects can link to one Space. If a Jira instance is on a private network, the documentation describes conditions involving proxies. Confirm the required setup for your environment rather than assuming that a credential alone will make the systems communicate.
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How to make a low-risk pilot
- Select one Jira project and a representative set of issues. Include the kinds of work the team expects to use with agents, not just easy demonstration tasks.
- Decide whether history must be imported. If the team needs historical records, plan an import; do not assume sync will bring history across.
- Confirm mappings and direction. Review issue and epic links, statuses, and whether changes should flow Jira-to-Sharkly or in both directions.
- Check access and connectivity. Verify credentials, required Jira permissions, webhook permissions where applicable, and any private-network proxy requirements with the administrators responsible for the systems.
- Keep the scope controlled while validating behavior. Check how representative changes appear, how failures or mismatches are handled, and which system is authoritative for each field or workflow.
- Expand only after reviewing the operational result. Track cycle time, review effort, agent failure or rework, administration, and total cost using the team’s actual process.
Sharkly says token material is encrypted at rest and recommends using only the Jira permissions needed and rotating credentials according to the organization’s security policy. Treat those statements as Sharkly documentation, and have the organization’s security owners review the proposed connection and permissions.
How should a team decide without relying on vendor claims?
Run a small pilot in the workflow the team would actually adopt. A feature list cannot tell you whether task context is sufficient, whether review is manageable, or whether the administrative burden is acceptable for your organization.
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- Choose representative work. Use a modest set of real tickets that vary in complexity and dependencies, with a clear definition of what “done” means.
- Use the same evaluation criteria. Record elapsed cycle time, time spent reviewing agent output, failure and rework, blockers, admin effort, and the complete cost of people and usage.
- Test governance, not just task completion. Check whether the right people can see what an agent did, whether permissions match policy, and whether review gates are clear.
- Compare like with like. Normalize team size, billing period, existing integrations, AI allowances, and external coding or API costs before comparing expense.
- Choose the system the team can operate consistently. Include migration and sync overhead, the cost of maintaining two connected systems, and the consequences of changing the system of record.
At the end of the pilot, prefer observed results from the team’s own work over general product claims. A workflow that looks compelling in a demo may still be a poor operational fit if it adds review burden, complicates permissions, or duplicates work tracking.
Frequently Asked Questions
Does Jira Free include Rovo credits?
No. Atlassian says Jira Free does not include Rovo; Rovo Search, Chat, and Agents are included in paid Jira plans. Check the current plan and tenant entitlements before planning a rollout.
Are Jira Rovo credits the same as coding-model API usage?
The listed Rovo credits are plan-specific allowances, while Sharkly describes model use through a connected coding plan or API account. Do not assume those allowances are interchangeable; confirm the terms for the specific service and integration.
Does moving Jira issues to Sharkly move our source-code repositories too?
The documented Sharkly-Jira integration concerns Jira project data such as issues, epics, mappings, and sync. The available documentation does not establish repository migration as part of that process, so evaluate source control separately.
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