u/Public-Bug5668

Agents are writing code, reviewing PRs, running in parallel, opening pull requests semi-autonomously and I'm researching how that's changing team-level engineering, not just individual speed.

Specifically: what happens to code review when AI both writes and reviews it, how teams govern shared agent config, whether roles blur, and whether anyone's actually measuring if it helps.

Purpose: Master's thesis at TUM (Software Engineering & AI). Anonymous, no PII, never monetized, academic use only. I'll share the aggregated findings back here when it's done.

If you're building or working with agents in a team, ~10 min of your input would be gold.

Genuinely curious what people are seeing - happy to get into it in the comments.

I'm researching how agentic AI coding tools (Claude Code, Cursor, Copilot, autonomous agents, etc.) are changing team-level software engineering - code review, code ownership, governance and how coordination shifts when AI both writes and reviews code.

Purpose: This is for my master's thesis at TUM (Chair of Software Engineering & AI). The goal is to understand how teams actually adopt and govern these tools - beyond the individual-productivity hype.

Data & privacy: The survey is completely anonymous, collects no personally identifiable information, and the data is used solely for academic research - never monetized or sold.

Giving back: I'll share the aggregated results and key findings back here in this community once the analysis is done.

If you work in a dev team using these tools, ~10 minutes of your input would genuinely help:
https://survey.seai.cit.tum.de/123456?lang=en&newtest=Y

Happy to discuss any of this in the comments.

Seeing a pattern where AI writes the code and reviews it, shared config files (CLAUDE.md, .cursorrules) appear ad-hoc with no clear owner, and nobody's really measuring whether any of it helps beyond "feels faster."

For those working in teams with these tools:

• Has code review shifted from understanding the code toward just catching bugs?
• Do you have shared AI governance/config, or does everyone configure their own?
• Are role boundaries blurring (managers coding, devs doing product/QA)?

Trying to understand how widespread this is. (Doing MSc research on it - will share findings.)