AI-powered workflow capabilities help engineering teams monitor agent sessions, automate recurring engineering loops like vulnerability remediation or test generation, and measure AI spend and token usage against development output.
Engineering leaders and developers still need to oversee governance, interpret performance and cost data, intervene when agents get stuck, and maintain accountability for delivery quality and compliance.
Teams keep a searchable record of which agent worked on which Jira issue, what actions it took, and which pull requests or comments it produced.
Atlassian Rovo Dev / GitHub Copilot EnterpriseEngineering managers track token consumption, seat usage, and output by repo or team to understand the cost of AI-assisted delivery.
GitHub Copilot Enterprise / tokentopAI repeatedly scans for common dependency or code security issues, prepares fixes, and routes the resulting pull requests through normal review controls.
Atlassian Rovo Dev / GitHub CopilotI help companies navigate AI adoption -- from strategy to production. Whether you are building your first LLM-powered feature or scaling an agentic system, I can help you get it right.
Design and build LLM-powered products and agentic systems
Go from idea to production with a clear implementation roadmap
Build AI with human-in-the-loop in regulated environments
New Jira and Teamwork Graph capabilities help engineering teams plan, assign, govern, and measure work across humans and AI agents.