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When AI writes the code and something breaks, nobody knows what it was supposed to do.

AI agents write code fast, but without a validation layer, fast just means faster bugs. In this video, we connect Claude Code to the CircleCI MCP server so Claude can trigger pipelines, pull build failures into context, and iterate until everything is green. No context switching. No copy-pasting logs.

Technical proposals can be like campfire stories. You start at the beginning, walk through every discovery & technical detail, and eventually arrive at the point. The problem? By then, you've lost the audience.

Same repo. Same tasks. Same validation gates. The only difference? Where validation happened. Here's how Chunk sidecars returned feedback in 22 seconds versus about 69 seconds through a traditional CI pipeline, giving the agent enough time to catch the failure, fix it, and revalidate before pushing.

Run a few prompts. Looks good. Ship it. According to Laurie Voss, that's still how a surprising amount of AI software gets tested.

People want to support your community. Sometimes you just have to invite them in.

The real unlock is flagging what actually needs human review, not shipping 5,000-line PRs and hoping for the best.

Devs have a habit of solving their own problems before anyone else's and the AI boom is repeating that pattern.

Your agent writes code fast, but you shouldn't have to see it until it's right. Chunk sidecars are lightweight microVMs that work inside the agent loop, requiring agents to pass pre-push validation in a CI-like environment before they declare they're "done." That means no massive CI pile-ups, no long round-trips that risk resetting your agent's context. You're sending code you already know is good.

Most AI applications in production right now were shipped on vibes. A developer ran a few queries, liked what they saw, and pushed to prod. Laurie Voss argues that's the core reason so many AI products feel broken, and the fix is simpler to name than it is to do: write the tests.