Seven months ago, our engineers said letting an AI approve pull requests was too risky. They were right, so we didn’t start there.

Instead, we built a bot for code reviews and capped it at 200-line PRs, watched every decision, and turned every mistake it made into a rule or a lint check. Then we raised the limit.

This talk covers that loop: small experiments, proof instead of promises, and a system that gets a little better every week.

You’ll leave with a playbook you can start on Monday, whatever tools your company gives you.

Our coding agents are getting smarter, but the workspace we drop them into is probably not enabling them to use their full potential. The decisions, failed attempts, conventions, and product knowledge are still crucial context scattered across people’s heads, Confluence, old conversations, and forgotten pull‑request comments.

During this talk, we'll walk through the shift from the “good old” prompt engineering to agent experience, then I'll show a practical AX experiment at my current company that led to a better workspace for both agents and the people using them.

Everyone wants end-to-end tests, nobody wants to maintain them — the real cost isn't typing the test, it's knowing what to assert and keeping it alive as the UI shifts every sprint. Martin hands both jobs to an AI agent live on stage: exploring the app, generating Playwright tests in a real browser, and repairing them after he deliberately breaks the UI. The second half drops the demo and measures instead — four setups, one real product, and an analysis plan written before the first run.

Shipping one application is hard enough. Coordinating 20 releases in a single week is a monumental challenge. In this talk, I'll take you behind the scenes of how our engineering organization pulled off this ambitious project. Whether you're looking to accelerate delivery or strengthen team collaboration, you'll walk away with actionable insights for your engineering toolkit.