When to Let Your Coding Agent Run, and When to Stop

A coding agent can save an afternoon or spend your evening rewriting code that already worked. The difference is rarely how long you let it run. It is whether each round produces evidence that the task is getting closer to done. Use this walkthrough to give an agent useful freedom without giving it an unlimited claim on your time and budget.

Start with a task you can actually finish

Before starting the agent, describe one observable result. For example, ask it to fix a search filter that ignores capitalization while keeping existing behavior unchanged. Avoid requests such as improving the whole application. Broad requests make it difficult to distinguish useful discovery from wandering.

Define what counts as finished: the reported bug is covered by a test, the fix passes relevant checks, and unrelated behavior stays intact. If you cannot describe a finish line, stop before the first request and narrow the task.

Set a spending limit and a review point

Choose an amount you are comfortable spending on this attempt, not on the entire project. Then choose a review point, such as after the first proposed fix and its tests. A financial limit protects your wallet; a review point protects you from approving expensive work simply because it is already underway.

With MyRelay, you can inspect each request's tokens and cost while using one key and one budget across supported providers. Use that visibility to check actual spending rather than guessing from elapsed time. Unless your coding tool explicitly supports an automatic limit, treat your budget as something you must monitor yourself.

Let the agent investigate before it edits

Ask the agent to inspect the relevant code, reproduce the problem if possible, and explain its likely cause before making changes. This first checkpoint should produce a concrete hypothesis, not a long tour of the repository.

Let it continue when it identifies a plausible cause and a small way to test it. Pause when it keeps reading unrelated components, proposes a broad refactor, or cannot explain how its planned edit addresses the failure. A brief clarification from you is often cheaper than another round of exploration.

Approve a bounded change and watch the evidence

Once the diagnosis makes sense, allow the smallest useful change and the relevant checks. Several requests can be reasonable if each serves a purpose: inspecting a dependency, updating a test, applying a fix, and checking the result.

Continue when new evidence changes the next action. A failing test that reveals an overlooked edge case is progress. So is a successful reproduction that rules out the original hypothesis. Do not judge progress by confident explanations, the volume of edits, or the number of tools invoked.

Interrupt repetition before it becomes a habit

Stop the run when the agent repeats a failed approach without new evidence, alternates between two implementations, or repeatedly changes tests to match its output. Also pause if it starts weakening validation, removing safeguards, or expanding the task to make the original problem disappear.

Ask for a short handoff: what changed, what passed, what still fails, and the evidence behind the next proposed step. Stopping does not mean throwing everything away. It means withholding further spending until there is a better reason to continue.

Decide whether another attempt earns its cost

Review the changes and remaining uncertainty. If the blocker is missing product knowledge, supply that knowledge yourself. If the task is too large, split it. If the agent needs access you should not grant, take over that part rather than broadening permissions.

A fresh attempt is worthwhile when you can give it a clearer question or better evidence. Restarting with the same vague request usually buys another version of the same confusion. Create a MyRelay account if you want request-level cost visibility to help make these decisions.

Finish with verification rather than reassurance

When the agreed checks pass, inspect the final changes and confirm that the original behavior is fixed. Do not authorize extra polishing automatically. Record the total cost and what required intervention. Over time, that small habit teaches you which tasks deserve autonomy and which need an earlier human checkpoint.