What Is an AI Coding Agent? Four Practical Ways to Use One

A broken build is harder to recover when you cannot tell which changes the AI made or why. An AI coding agent is a tool that can work through a coding task using project context, propose or make changes, and, depending on its permissions, run commands. That makes it useful for getting unstuck. It also means a vague request can affect more of your project than you intended.

You do not need to understand every line of code before asking for help. You do need a way to inspect the result and remember the decisions that matter. These four mistakes are especially easy to make when the first result looks good.

1: Treating an AI coding agent like an answer box

A chat answer gives you something to consider. An agent may change files or run a command as part of completing the task. It is easy to overlook that difference when both interactions begin with a prompt. The surprise comes when a request to fix one screen also changes a shared component or configuration file.

Before you delegate, name the boundary of the work. Try “Fix the validation message on the invite form. Show me the files you plan to change before editing shared components.” Then inspect the proposed scope and the resulting diff. If the work has spread beyond the invite form, ask why before accepting it.

2: Giving the agent a goal without a way to check it

“Make the login better” leaves several decisions open. The agent could change copy, error handling, redirects, or the authentication flow. A result can look polished while still allowing a user into the wrong account or leaving an error undisplayed. More prompting will not help much if you cannot say what success looks like.

Give the task one observable outcome. For example, “When a user enters an incorrect password, keep them on the login screen and show an error without revealing whether the email exists.” Test that behavior yourself, including a successful login. For changes involving auth or database writes, review the diff and understand what changed before deploying.

For a larger feature, this guide to spec-driven development for AI builds explains how to record expected behavior and check the result against it.

3: Letting a useful fix disappear into the conversation

A prompt can solve a stubborn issue, yet the next session starts without the context that made the fix work. You may remember that the settings page broke after a schema change but forget which field was involved. The agent then has to rediscover the same constraint, and you spend your next session repeating work.

After a meaningful fix, save the prompt, the result, and the condition under which you would reuse it. Keep a short note about the decision too, such as “Settings saves only after the profile record exists.” That sentence gives you a starting point when a related bug appears, even if you use a different coding tool next time.

4: Accepting the final summary instead of testing the change

An agent's summary is a description of its work, not evidence that the app behaves correctly. A page can render while its save button fails. A delete action can work for an owner and still be available to someone who should not have access. These gaps are easy to miss when the agent reports that the task is complete.

Run the affected flow yourself and inspect what it changed. Check the unhappy path as well as the happy one, especially for permissions, destructive actions, and data writes. If the result fails, give the agent the observed behavior and the expected behavior rather than another broad request to “fix it.”

Keep the next session recoverable

Before you close the project, write down what changed, what you checked, and the next action you would take. A few lines are enough to make a return visit productive. When a prompt is worth keeping, store it with the result rather than relying on the chat transcript to explain it later.

Keep your build decisions and useful prompts in VibeCrumbs so the next session starts with the context this one produced.