Type Errors for Better Agent-Assisted Development

As AI coding agents take on larger Python tasks, a practical question emerges: what's the best way to catch the bugs they introduce? Tests are thorough but slow. Linting is fast but shallow. Type checking occupies an interesting middle ground: deep enough to catch semantic errors, fast enough to run on every edit, and concrete enough to tell the agent exactly what to fix.

In this talk, I explore connecting Pyrefly, a Python type checker built at Meta, to Claude Code. I'll walk through integration options and discuss practical considerations like token costs and setup complexity. Whether you're building tools for AI agents or using them in your daily work, you'll leave with a clearer picture of where type checking fits in the agentic development loop.