Inside Atlassian’s Governed Agent Loops for AI Native SDLC
Governed agent loops are Atlassian’s answer to scaling AI across the software lifecycle. Ming Wu, head of engineering for Dev AI at Atlassian, joins Alan Shimel on Techstrong TV. Furthermore, she explains how Jira becomes the control plane for agents.
About Ming Wu
Ming earned her PhD at Michigan State and then joined Microsoft Bing Search. In addition, she worked across Office and Azure before moving to GitHub Copilot. Consequently, she shifted from search and ranking to developer tooling as GPT models arrived.
She joined Atlassian two years ago and built the Dev AI organization from scratch. Meanwhile, a separate team handles internal productivity for about 6,000 Atlassian developers. Therefore, every new tool is tested internally before customers see it.
What governed agent loops mean
Ming breaks the name into two parts. First, governance gives teams visibility and control, because humans remain accountable for agent output. As a result, governed agent loops keep people in charge of the process.
The loop part is about automation and scale. In addition, teams set conditions so agents work through large batches of jobs instead of manual triggers. Consequently, guardrails and enforcement keep output within expectations.
Why Jira is the control plane
Atlassian is building agent capability at the platform level across its product family. Meanwhile, Jira is the natural first surface because it is already the system of record. Therefore, governed agent loops inherit traceability across team and org boards.
Third party coding agents are also supported. Furthermore, a shared context layer gives every agent the same understanding of team, org and business intent.
Adoption and ROI
Ming sees a wide spectrum, from exploratory software teams to more traditional industries. In addition, customers now ask how to scale and how to prove real velocity gains.
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For more information please visit atlassian.com