Operations | Monitoring | ITSM | DevOps | Cloud

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.

Learn how to connect Cursor to CircleCI using MCP in this short demo. This video shows the workflow end to end so you can see how the integration works in practice.

Here’s what happens when you fork a repository and trigger a pipeline in CircleCI. This demo shows the workflow end to end so you can see how CircleCI responds to a forked repo in real time.

In this episode of The Confident Commit, Rob Zuber sits down with Meg Adams, Senior Director of Engineering at The New York Times, for a deep dive into leading engineering teams through the AI revolution while staying true to organizational mission. Meg shares how the Times approaches AI adoption with a "measured but focused" strategy, emphasizing experimentation and opinion-formation over mandates, and why she believes AI serves as a force multiplier for what already exists in your organization and workflows.