The arrival of coding agents this year has made the AI conversation feel different from the autocomplete tools developers have already been using.
Autocomplete helps in the moment. It suggests a line, completes a function or nudges you through a familiar pattern. A coding agent feels more operational because it can be given a task, inspect parts of a codebase, make changes and return with something that looks like work completed. That changes the kind of responsibility involved.
I find the idea useful, but I do not find it reassuring by default. The more capable the tool becomes, the more important the boundaries become. A developer should not treat an agent as a shortcut around understanding the codebase. It should be treated more like a junior contributor with unusual speed, no lived context and a tendency to sound confident even when the decision needs checking.
The Task Needs To Be Smaller Than The Ambition
The first practical rule I use is to keep tasks smaller than the ambition. Asking an agent to improve a website is too vague. Asking it to inspect a specific component, explain why a test is failing or suggest a safer way to refactor one function is much more useful.
The narrower the task, the easier it is to review the result. That matters because review is where the responsibility returns to the developer. I do not want an agent making broad architectural changes that I cannot evaluate properly. I want it to help me move through contained work with clearer evidence.
This is especially true on client projects. A codebase often carries decisions that are not obvious from the files alone. There may be hosting constraints, plugin dependencies, editor habits or commercial reasons a piece of code exists in a certain way. An agent can read the code, but it cannot automatically understand the history behind it.
The Repository Needs Rules
I have started thinking more carefully about the instructions that live around a project. If an AI tool is going to read or change a codebase, it needs to know the project conventions. How are files organised? How are tests run? Which directories should not be touched? What counts as acceptable output?
That information is useful for humans too, which is probably why I like the exercise. Writing down project rules makes the codebase easier to work with whether AI is involved or not. It also reduces the chance that a tool makes changes in a style that does not fit the existing project.
A small instruction file can save a lot of confusion:
Use existing naming patterns.
Do not introduce new dependencies without explaining why.
Prefer small changes that can be reviewed in isolation.
Run available tests before suggesting the work is complete.
Explain any files that were changed and why they changed.That is not a perfect safety net, but it sets the tone. The agent is not being asked to be creative in every direction. It is being asked to work inside the shape of the project.
Review Is The Work
The dangerous part of AI-generated code is not that it is always bad. The dangerous part is that it can be good enough to stop someone reading it properly. A clean diff can still carry the wrong assumption. A passing test can still miss the behaviour that matters. A confident explanation can still leave out the trade-off that should have been discussed.
That is why review becomes the important part of the process. I want to know what changed, why it changed, what was tested and what was not tested. If I cannot explain the change myself afterwards, I should not merge it.
This is not about being anti-AI. It is about preserving ownership. The client, the website and the codebase do not care whether the bug came from a person or a tool. The person responsible for shipping the change still needs to understand it.
Retrospective Thoughts
I think coding agents will become part of normal development work, but not because they remove the need for judgement. They will be useful when they handle contained tasks, surface information quickly and help developers move through work that still receives proper review.
The best use I have found so far is not handing over the wheel. It is using the tool to create a better first pass, a clearer explanation or a faster route into an unfamiliar part of a project. The boundary is simple. AI can help with the work, but it does not own the consequences.