Most project-management tools answer one question:
What work exists?
In the AI era, that is no longer enough.
Teams also need to know which Agent is doing the work, where it is running, what context it received, whether it is blocked, what changed, and who reviewed the result.
If the workflow is still “copy the Jira ticket into Claude Code, wait, then paste the answer back into Jira,” your project tool is tracking the request—not the work.
That is where Sharkly.ai takes a different approach.
A concrete situation: a payment retry bug
Imagine a payments team receives this report:
Some customers are charged twice when the payment webhook is retried.
The project manager creates a ticket:
Title: Prevent duplicate charges on webhook retries
Acceptance criteria:
- The same webhook event must never charge a customer twice.
- Retried events must be safely ignored or handled idempotently.
- Existing successful payments must continue working.
- Add regression tests.
- Do not change the payment provider integration.
This is a useful Agent task because it has a clear outcome, boundaries, and a way to review completion.
The conventional Jira workflow
The ticket is assigned to an engineer. The engineer opens the repository, copies the Jira description into an AI coding tool, explains the project context, runs the Agent, reviews the changes, opens a pull request, and manually updates Jira.
Jira may show In Progress, but the project manager may not know whether the Agent has started, is waiting for a machine, used the right repository, or failed a test. The work is split between Jira, a terminal, an AI chat, and a pull request.
The ticket remains visible. The execution does not.
What Sharkly changes
Sharkly can connect a Jira project to a Sharkly Space. The Jira issue becomes a Sharkly Task while the original Jira workflow remains available.
The Task can have two kinds of ownership:
- a human owner who is accountable for the outcome; and
- an Agent or Crew that executes the work.
For the payment bug, the team could configure a Payments Fix Agent with access to the payments repository, Claude Code or Codex as its Runtime, a connected Computer with the required VPN and development tools, testing instructions, and a run timeout.
When the Task is ready, Sharkly queues and dispatches the run. The team can see whether it is Queued, Dispatched, Running, Completed, Failed, or Canceled.
That turns “Why has this ticket not moved?” into an answerable operational question.
Parallel work without shared-checkout chaos
The Payments Fix Agent can implement the change while a second Test Agent creates regression coverage.
Each Agent works in an isolated Git worktree. Two runs can target the same repository without overwriting each other’s files or switching the same branch underneath one another.
The results return to the same Task:
Task: Prevent duplicate charges on webhook retries
Human owner: Payments team lead
Execution Agent: Payments Fix Agent
Test Agent: Regression Test Agent
Execution: Completed
Verification: Test results and changed files attached
Decision: Waiting for human acceptance
If the lead comments, “Also cover provider timeout retries,” that comment can start a follow-up run. The new context and result stay attached to the original work item.
Nothing merges automatically. The Agent reports what it did and what it verified. A person reviews the diff against the acceptance criteria and decides whether to accept, request changes, merge, or release.
Jira is not “non-AI” anymore
Jira has a serious AI answer. Atlassian’s Rovo features can assign work to Agents, break down work, use organizational context, and run Rovo Dev against Jira work items.
In an Atlassian-published engineering account, principal engineer Viktor Adam reported using Rovo Dev for an estimated 95% or more of his development work and opening and merging more than 100 pull requests in two weeks. His workflow was Slack request → Jira work item → Rovo Dev session → remote sandbox → draft pull request → review.
That is a strong workflow. If your repositories, environments, and preferred Agents fit inside Atlassian’s supported setup, Jira may be enough.
The difference is the center of gravity:
| Project need | Jira with Rovo | Sharkly |
|---|---|---|
| Main focus | Organization-wide work tracking | Agent execution and human review |
| Agent assignment | Assign supported Agents from a work item | Assign an Agent or Crew as the execution owner |
| Execution environment | Atlassian-supported Agent environments | Computers and Runtimes your team connects |
| Parallel work | Depends on the selected Agent workflow | Isolated worktrees and bounded concurrency |
| Execution record | Work item history and linked code | Task timeline, run state, trace, blockers, and result |
| Migration | Remain in Jira | Import or sync Jira projects and issues |
Where Sharkly has the advantage
Sharkly is strongest when Agents are doing a meaningful share of delivery work—not merely summarizing tickets.
- Execution is a first-class part of project management. An Agent has a role, instructions, tools, a Runtime, and a place to run.
- The environment is explicit. Agents can run on a connected laptop, server, container, or supported cloud host with the tools the work requires.
- Parallel work is safer. Isolated worktrees keep independent runs separate until review.
- Evidence stays with the work. Progress, logs, blockers, test results, and Agent responses remain on the Task.
- Adoption can be gradual. Sharkly supports Jira Cloud and Jira Server/Data Center, with import, one-way sync, and bidirectional sync options.
The honest recommendation
Use Jira alone if Rovo already fits your repositories, environments, governance, and Agent choices.
Add Sharkly when Agents run on your own machines, your team uses several coding tools or custom Runtimes, multiple Agents need to work in parallel, or project managers need to see execution rather than a single status label.
Do not migrate your entire backlog first. Connect one low-risk Jira project to one Sharkly Space. Assign one bounded bug to one Agent. Measure the manual handoffs, blocked runs, review rework, and time from assignment to a useful change.
Jira tells your organization what work exists. Sharkly is built for the next question:
Can an AI Agent do the work in a visible, controlled, reviewable way?
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