We’re bringing governed agent loops to the AI-Native SDLC

Introduces Code Context built on the Teamwork Graph for secure, multi-repository intelligence, enabling agents to understand architecture, decisions, and institutional memory. Also introduces Agent Context Controls for platform teams to govern agent access and permissions. Details agent loops in Jira for automating the path from backlog to pull request, including scanning for work, delegating to Jira Coding Agent, and opening PRs. Introduces 'Standards' for defining and mapping organizational coding standards to repositories, and 'AI Review' for automated PR checks against these standards. Introduces 'DX for Agentic Development' for measuring AI impact across throughput, quality, adoption, and cost, and the 'Jira Agent Usage Dashboard' for team leaders to track agent usage and velocity.
More from the AI-Native Software Development Capabilities in Jira trail
- A simpler way to run AI agents in Bitbucket Pipelines
- How we made vulnerability fixes review-ready with Agentic Pipelines
- Agentic automation in practice: putting standard engineering work on autopilot
- Opening the door to agent autonomy: The architecture behind Rovo’s agent harness
- From prototype to production: lessons learned taking AI-built software to enterprise scale