Finding the issue is only half the story. The next frontier is agents that can take incident context, propose a code or config fix, validate it in isolated environments, and prepare a PR for human review. This talk covers the architecture, control points, and failure modes of building AI remediation workflows that are useful without being reckless.

What you’d cover:
• turning logs + RCA into executable change candidates
• sandboxed code/config modification
• automated test harnesses and regression gates
• rollback awareness and blast-radius controls
• PR-first workflows instead of auto-merge fantasies