Agents Could Kill Overhiring
Cloud killed the overprovisioned server rack. Agents might do the same thing for engineering teams.
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AI coding agents produce code faster than most teams can validate it. Without a validation step between the agent and CI, every problem gets caught after the push, and feedback arrives long after the agent has lost context. Agents need consistent feedback while they’re working so that small failures get fixed locally and CI stays focused on moving code into production.
The Problem With AI Token Caps
Your engineers don't suddenly become less valuable because they hit a token cap.
Same team, but building more ft. Chris Kelly of Augment Code
Most teams obsessing over token costs are measuring the wrong thing. The real savings from AI aren't in lines of code written faster. They're in the coordination overhead that disappears when fewer humans need to align before anything gets built. Chris Kelly, Head of Product at Augment Code, joins Rob to cover why prototypes have replaced specs, how agents enable dynamic team capacity the way cloud replaced over-provisioned servers, and what "good code" even means when your primary reader is an LLM. In this episode.
Getting started with Codex and CircleCI
Codex is OpenAI’s coding agent, powered by the GPT-5 family of models. It reads your files, proposes edits, and runs commands directly in your local environment. It ships as both a desktop app and an open source CLI, and it extends through plugins that connect it to external tools and services. Like any AI coding tool, Codex is strongest when the code it generates gets validated automatically.
AI writes the code. Chunk writes the docs.
Did you know Chunk can analyze your entire codebase and open a PR with accurate, up-to-date documentation?
How to Improve Your Documentation with AI (CircleCI Chunk Tutorial)
AI coding assistants help you ship features fast, but documentation almost never keeps up. In this Ship Smarter session, we'll show you how CircleCI's Chunk autonomous CI/CD agent automatically analyzes your codebase, identifies documentation gaps, and opens a pull request with improvements. No manual writing required. In this video.
Introducing Chunk sidecars: Inner loop validation that keeps up with your agents
Local development and remote validation were always meant to work together: developers iterate on their machine, run a few manual checks, then push to CI to clear code for production. But AI development broke that balance, flooding CI with a volume of commits no developer has read, let alone tested. Chunk sidecars restore the balance: lightweight, preconfigured environments that run alongside your local workflow and validate changes as they happen.
Introducing Chunk sidecars
Fast, pre-configured environments that bring validation and feedback into the inner loop in 60 seconds or less.
AI debugging your CI pipeline
From failing pipeline → fixed PR Here's how Chunk analyzes logs, finds real bugs, and ships working fixes automatically.