How coding agents actually work

What happened: Coding agents moved from line-by-line autocomplete to autonomously reading a repo, forming a plan, editing multiple files, running the test suite, and iterating on failures — with no human approving each individual change.

Why it matters: The unit of work shifts from "suggestion you accept" to "task you hand off." That's a different trust model, and it changes what you review — the diff and the test run, not each keystroke.

The loop

• Read: the agent explores the repo — file tree, relevant modules, existing tests — before touching anything
• Plan: breaks the task into steps, often visible as a todo list you can inspect mid-run
• Edit: makes targeted file changes, not a full rewrite
• Verify: runs the test suite or linter — this is the actual feedback loop, not the model's confidence
• Repeat: fixes failures and re-runs until green or it hits a limit and asks for help

What makes it work

Not a bigger model — tool access. Shell commands, file read/write, and a real test runner turn a language model into something that can check its own work instead of guessing. The verification step is what separates a coding agent from very confident autocomplete.

Where it still breaks

Large refactors across an unfamiliar codebase with no tests to verify against. Ambiguous specs where "done" isn't well defined. Silent scope creep on long-running tasks — worth checking the diff, not just the final "done" message.

Originally published on ayraix.com, practical AI for enterprise builders.