When you need to apply the same change across hundreds of repositories, manual PRs don't scale and traditional codemods can't handle the unexpected. In this talk, I'll walk through the engineering journey from building a single-repo migration skill to deploying a fleet of parallel AI agents that autonomously process repositories, fix breaking changes, and report progress — all without human intervention.
You'll learn the architecture behind cost-aware model routing, baseline comparison to avoid false positives, race-condition-free parallel execution, and risk-ordered rollout. I'll share what worked, what broke, and why a percentage of repos still needed a human.
This isn't a demo of AI writing code. It's a production playbook for running AI agents as automation at scale, applicable to any fleet-wide change that you can take back and adapt to your own fleet-wide change programmes the week after this talk.
This talk has been presented at AI Coding Summit London, check out the latest edition of this Tech Conference.

















