I've been experimenting with AI coding tools daily for the past year with a small engineering team (all senior 15+, FAANG backgrounds). Wrote down everything we learned, the parts that worked and the parts that didn't.

Some things that surprised us:

- AI is slower than writing code yourself on any single task. The win is not speed, it's parallelism. We made our dev environments duplicable and now work on multiple PRs simultaneously.

- AI is dramatically better on clean code than on technical debt. It's like a mirror that shows you how much chaos you've been tolerating.

- Out of the box tools give mediocre results. We invested heavily in configuration (20 AGENTS.md files, 6,553 lines total) and in augmenting the agent with skills: typechecking, test runners, read-only access to tickets, logs, Sentry. The agent self-debugs and fetches its own context now.

- Never trust the output. We built a pr-reviewer agent whose only job is to criticize code changes against the original specs. Strongly typed languages help enormously here, the compiler is your cheapest AI reviewer.

- Mental context-switching is the new must-have skill for engineers. You orchestrate, the AI executes (for now).

Would love to hear what's working for other teams, I'm sure our approach is not the only one.