Coding agents can inspect repositories, edit files, run tests, and explain changes. They are most effective when the repository already communicates how work should be done.
Prepare the environment
Document setup commands, architecture boundaries, test expectations, and security rules. Keep changes reviewable and make automated checks fast enough to run often. An agent with a clean feedback loop is more useful than a stronger model operating blindly.
Choose low-risk starting tasks
Good candidates include adding tests, updating documentation, resolving narrow bugs, and performing mechanical migrations. High-impact authentication, billing, permissions, and data deletion changes deserve tighter human review.
Evaluate the patch, not the prose
A convincing explanation is not evidence that code works. Review the diff, run tests, inspect security-sensitive paths, and verify behavior in a realistic environment. Measure accepted changes, escaped defects, review time, and rollback frequency.
OpenAI describes modern agent harnesses as controlled environments that combine file inspection, commands, editing, and long-running work. The engineering discipline around those tools remains the differentiator.