What Is Agentic Coding? 4 Mistakes That Derail a Build
Agentic coding lets an AI tool carry out multi-step development tasks. Learn where that autonomy can go wrong and how to keep a fast build reviewable.
An agent can change several files before you notice it misunderstood the task. That is the appeal and the risk of agentic coding. It means giving an AI coding tool a goal and allowing it to take multiple steps toward that goal, such as inspecting code, editing files, and checking its work. Coding agents such as Devin AI are designed for this kind of delegated task. You still decide what should change and whether the result is safe to keep.
The mistakes below tend to happen because the first result looks finished. A working screen can hide an unnecessary rewrite, a missing authorization check, or a decision nobody recorded. Avoiding those failures takes a few deliberate pauses, not a heavy development process.
Mistake 1: Giving the agent a goal without boundaries
“Add customer accounts” leaves a lot for the tool to infer. Should it change the database? Who can create an account? Does an existing sign-in flow need to stay intact? An agent trying to be helpful may choose answers that differ from yours, then spread those assumptions across the project.
Before the task starts, write a short request that names the desired behavior, the files or areas it may change, and anything it must leave alone. Ask it to explain its plan before making a broad edit. For a small SaaS, that might mean specifying that customers can view only their own requests while the existing admin flow remains unchanged. The boundary gives you something concrete to check against the resulting diff.
Mistake 2: Treating a passing demo as a reviewed change
A screen that renders proves less than it seems. AI-generated code can mishandle database writes, expose data through a poorly checked route, or break an older flow while satisfying the latest prompt. The temptation is strongest when the agent also reports that it tested its own work.
Inspect the changed files and run the relevant behavior yourself. For an account feature, try access as different users, validate writes, and test what happens when input is missing or malformed. Check destructive actions and keep secrets in environment variables rather than prompts or committed files. You do not have to audit an entire app after every small change, but the review should match the risk of what changed.
Mistake 3: Letting a local fix become a project-wide rewrite
When an agent encounters awkward code, it may simplify the task by reorganizing more than you asked. Sometimes that produces a cleaner result. It also makes a small fix harder to understand and can remove behavior you meant to preserve.
Keep the first pass narrow. Ask for the smallest change that addresses the bug, then compare the diff with the original request. If a wider refactor is genuinely needed, make it a separate decision and record why. This gives you a useful stopping point: you can accept the fix without also accepting every structural preference the tool introduced.
Mistake 4: Leaving the reason for a change in chat history
A few days later, the code may tell you what changed without telling you why. You might remember that an agent adjusted a permission check, but not the edge case that prompted it or the prompt that finally exposed the bug. The next session starts with guesswork, and a reasonable-looking cleanup can undo the earlier fix.
End the session with a brief recovery note: the goal, the decision made, what you verified, and the next action. Save a prompt when its wording helped solve a hard issue, along with the result it produced. Keep unfinished work visible outside the conversation so that returning to the project does not depend on finding the right chat thread.
Keep the speed and make the work recoverable
Agentic coding is useful when you can delegate a bounded task, inspect the result, and carry the important context forward. For your next change, write the boundary before the prompt and the recovery note after the review. Those two small habits make it easier to benefit from the tool's initiative without losing track of your own decisions.
For a repeatable routine, this guide to an AI coding workflow covers how to prompt, review, test, save context, and resume.
If those decisions and useful prompts need a durable home, keep your build notes in VibeCrumbs.