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An AI coding agent holds many conversations at once. Not only is the user prompting it, the agent also talks to the IDE, showing diffs and asking before it touches a file. At the same time it talks to tools, pulling a failing build or querying a database. Two open protocols standardize those conversations. This guide compares ACP vs MCP in practical terms: what each protocol does and when each applies. ACP (Agent Client Protocol) connects a code editor to an AI coding agent.

A test suite can be all green and hit 100% line coverage and still miss bugs. Coverage measures which lines ran during the tests, not whether the assertions actually caught a defect. A test that calls a function but never checks the return value still counts toward the coverage number. The bug it would have prevented still ships.

Agentic coding tools like Claude Code can write, refactor, and debug across an entire codebase, but by default they read code as plain text, the way grep does. The Language Server Protocol (LSP) changes that: it’s the same code-intelligence layer an IDE uses, and wiring it into an agent lets it read code by meaning instead of by string match. The bigger the codebase, the more a wrong guess about a symbol costs, and the more that structural view pays off.

When you’re building with AI, you can get a lot done in 30 seconds. Waiting minutes for CI feedback on your latest change can feel like an eternity. Chunk sidecars are designed to give you feedback fast, running your full test suite against the same Linux environment as CI, directly inside the agentic loop. Traditional CI pipelines can take five or ten minutes to catch a basic lint error or failing unit test.

Previously, I described some core approaches to validating agent written code: feedforward and feedback techniques. Feedforward techniques are about avoiding errors up front, for example by coming up with better prompts and planning strategies. Feedback gives agents a signal that they have actually achieved a task. Feedback is a key part of common agentic patterns like Ralph loops or the /goal commands in Codex and Claude Code: keep working until some known condition passes.

How to set up blue-green deployments with CircleCI.

This post covers some techniques to get agents to validate their work. We’ll cover why this is important, a variety of types of checks you can employ, and mechanisms you can use to enforce them.

AI agents can write code faster than any developer. But for most teams, the feedback loop hasn’t kept pace. The agent generates code, pushes it to CI, and minutes later a full pipeline run catches a simple linting error or a failing unit test. By then the agent has moved on. Getting back to a working state means rebuilding context from scratch and burning tokens just to fix something that should never have shipped in the first place.

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.

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.