AI Coding Agents

Autonomy across multiple steps is what separates an agent from an assistant. Given a task such as fixing a failing test or bumping a dependency across a monorepo, AI Coding Agents plan, open files, make edits, run the test suite, read the error output, and try again before presenting a result. They use tools rather than only producing text: a shell, a package manager, a test runner, version control, sometimes a headless browser. Some run in a terminal against a local checkout; others run in a sandboxed container triggered by a label on an issue and finish by opening a pull request.

Maintainers point them at backlogs of small, well-specified work, at lint sweeps, at migrations spanning many files, and at reproducing bugs. Honest comparison points are the sandbox and permission model, which commands run without approval, repository size handled, cost per completed task, integration with issue trackers and continuous integration, self-hosting, and code retention terms.

Try one on a real ticket and measure how often the result merges without rework. Long runs can loop and burn budget, destructive commands need guarding, and thin test coverage gives an agent nothing to check itself against. Treat every pull request as untrusted, require passing tests and human approval, and scope credentials narrowly. Billing is typically usage-based, per-seat, or credits per completed task.