What is a coding agent?
A coding agent is an AI agent that carries out programming tasks inside a code repository. Given a bug report, a feature request, or a ticket, it searches the codebase, reads the relevant files, and edits them. It then runs builds, tests, and linters, reads the output, fixes what failed, and repeats until the change works. The result usually arrives as a pull request with a summary of what changed and why.
A code completion assistant, often called a copilot, suggests the next few lines while a developer types. A coding agent plans the change, runs commands, and checks its own work across many steps. Developers use coding agents interactively in a terminal or editor, or assign them tickets that run in the background and return as pull requests.
Why coding agents matter
Most companies carry more routine software work than their developers can finish: dependency upgrades, missing tests, small bug fixes, integration scripts, and reports that need one more column. Coding agents suit this work, because each task is well defined and the test suite shows whether the change succeeded. The developer’s job shifts toward writing clear tasks and reviewing changes.
The same abilities create risk. A coding agent runs commands it wrote itself, so with too much access it can delete files, call outside services, or expose credentials. It can also produce code that passes the tests and still contains a security flaw. Every request sends source code to whoever hosts the model. Companies that treat their code as a trade secret can run coding agents against an open-weight model on infrastructure they control.
Coding agents in practice
A managed service provider maintains several hundred PowerShell and Python scripts that onboard client users, check backups, and compile monthly patch reports. When a vendor retires an API version, dozens of scripts need the same change. A coding agent works through them in a container that holds credentials for a test tenant only. It updates each script, runs it against the test tenant, fixes what fails, and opens a pull request with the test output attached. An engineer reviews every diff before it merges.
What to watch for
- Run the agent in a sandbox or container with no production credentials and limited network access.
- Keep branch protection on, and require passing tests and a human review before any merge.
- Check every new dependency the agent adds for license terms and provenance.
- Keep secrets out of the repository, since the agent sends what it reads to the model.