Connect any coding agent to your system of record to automate engineering loops
AI coding agents have made individual developers faster. But faster individuals working in their local environments do not automatically create faster engineering organizations. The bottlenecks that slow software delivery are usually systemic: work stalls in human queues, triage waits for idle cycles, and routine handoffs depend on manual intervention.
That is why scaling agents isn’t a prompt engineering problem for most organizations. It’s actually an orchestration problem: building automated, event-driven loops that trigger agents when real engineering conditions are met.
Today, we’re expanding Jira Automation into an open control plane for AI coding agents with native support for GitHub Copilot, Cursor, and Claude. An event in your system of record can now automatically package structured context, trigger the right agent, and log the outcome, backed by an engine already saving teams like Rivian 750+ hours per developer every year.
Automate the moment your agent gets to work
Jira Automation now supports GitHub Copilot, Cursor, and Claude as native action steps – available alongside every other action in your automation rules.
Automation rules in Jira are multistep workflows: a trigger (what kicks it off), optional conditions (who or what it applies to), and a sequence of actions (what happens next). Triggers can be event-based (a work item created, a label added, or a status changed) or scheduled to run on a recurring cadence without manual intervention. You can even describe what you need in natural language and let Jira configure the rule for you. Now, invoking a third-party agent is just another action step.
At each step, you can pass a system prompt with custom instructions for the agent. Unlike static automation steps, AI coding agent automations execute adaptive flows – they evaluate what each work item requires first, and then act accordingly. For example:
- If the work item is missing context, post a comment in the work item and label it as ‘Needs Refinement”. If it’s ready, invoke coding agent to implement the fix and open a PR.
- If the vulnerability is low severity, invoke coding agent to fix it and open a PR. If it’s high severity, summarize the scope and escalate it.
Workflow rules like this keep a human in the loop where you need one. These triggers mean agents only run when conditions are genuinely met, preventing your team from wasting tokens.
Every agent invocation is logged in Jira’s audit trail and surfaces in Jira’s For You tab. The loop will run within your set parameters, and you and your team have visibility into it all.
Agentic automations your team can build today
Here are three examples to get you started:
Automated vulnerability patching
When a security flaw is detected in your codebase, whether flagged by a scanning tool or reported by a teammate, a Jira work item is created. Every morning, an automation rule sweeps all open security work items and an agent assesses the severity and scope of each one. If it’s a straightforward fix, it opens a PR automatically. If it’s complex or touches critical infrastructure, it flags it for human review before taking action.
The result: Security response drops to minutes for routine patches, while complex risks get routed to the right engineer with full context pre-assembled.
Want to go deeper? Read our guide to AI-powered vulnerability management.
Stale feature flag clean up
When a feature flag is marked for cleanup, a work item is created and triggers an automation rule that invokes an agent to scan the codebase, remove the flag logic, draft a PR, and ping the team to review.
The result: Technical debt is cleaned up continuously instead of accumulating silently in production.
Continuous bug triage
When an alert fires in Jira Service Management and a bug is logged in Jira, an automation rule triggers the agent loop. Through the Teamwork Graph MCP, the agent gets operational telemetry from JSM and pulls relevant runbooks, system specs, and coding standards directly from Confluence. The agent isolates the root cause, drafts a PR following your documented procedures, and attaches an investigation summary directly to the work item.
The result: Every report that hits the backlog arrives pre-investigated with a proposed fix ready for review.
Get started
Every month, customers run hundreds of millions of Jira and Automation workflows through Atlassian. Bringing AI agents into this engine turns standard workflow rules into complete, self-executing loops.
Because Jira operates as an open control plane, you are not forced to standardize your entire organization on a single AI provider. Different engineering teams, repos, or workflows can leverage different agents across your stack. You gain centralized governance, auditing, and observability in Jira without imposing rigid tool choices on your teams.
Bring the best agent to each job without changing how your organization monitors, logs, or governs software delivery.
To learn more, read the support article.
Coding agent automation actions are available to all Jira customers on a paid plan with Rovo enabled.
