Operations | Monitoring | ITSM | DevOps | Cloud

Treating problems as systems issues instead of people problems changes everything.

But someone who knows how to use AI well might.

If a company sells tools that promise faster engineering teams, they need to prove it internally too.

AI can generate code in seconds. But validation, testing, and review still take time.

Even when you stop writing code every day, the way engineers think about systems, feedback, and problems stays with you.

AI agents used to comment on issues and pull requests, now they’re pushing code to repositories.

As AI-assisted coding tools increase the amount of code, commits, and builds, optimizing your CI pipeline becomes more important than ever. In this tutorial, we walk through how to use Chunk, CircleCI’s autonomous agent that validates your code at AI speed, to analyze your pipeline history, identify performance bottlenecks, and suggest optimizations to your CI/CD configuration. Chunk leverages critical CI/CD context like build history, test results, and execution data to keep pipelines healthy and moving at AI speed.

When tooling, tech debt, and friction get in the way, even simple tasks take five times longer.

AI hype is everywhere, but real results depend on your team, your scale, and your workflows.

What does it actually look like to build an AI-native product and lead an engineering team through the AI era when you've been doing it longer than most? Rob Zuber sits down with Loïc Houssier, CTO at Superhuman, to talk about what it meant to be an AI company before AI was everywhere, and how that early foundation shapes the way they build, ship, and think today.