What an AI coding agent can and cannot do
An honest picture without the hype. Where agents are strong today, where they go wrong and why a person always has the final say.
An AI coding agent is software that carries out programming work on its own. You give it a task, for example "fix this bug" or "write tests for this module". The agent reads the code, makes a plan, writes the change, runs the tests and delivers a proposal that a developer can review.
That sounds like magic. It isn't. It is a tool, and like any tool it works well for some tasks and badly for others.
Where agents are strong
- Clearly described tasks. The more precise the task, the better the result.
- Work with a clear end point. A test that has to pass. A warning that has to disappear.
- Repetitive work across many files. Updating a library and adjusting every place it is used.
- Tests and documentation. Agents write these faster than most people want to.
- A first version. A small internal tool or an integration that a person then refines.
Where it goes wrong
- Vague tasks. "Make the app better" produces nothing usable.
- Decisions about your business. Which feature matters more, what your customers want, how your product should evolve. That remains human work.
- Code without tests. If the agent cannot check whether something works, it guesses. Sometimes right, sometimes not.
- Major architecture decisions. An agent sees part of the system. An experienced developer sees the whole.
Why a person always reviews
An agent can make mistakes that look convincing. Code that looks tidy is not necessarily code that works. That is why nothing goes to production with us until a person has reviewed and approved it.
That is not a weakness of the approach. It is the approach. The agent does the hands-on work. Your team keeps control and spends its time on what really needs decisions.
What that means for you
The gain is not in replacing people. It is in removing the work that holds your people up. Find out what that work is for you in a free scan.