Make AI Coding Agents Ship Review-Ready, Tested Code

How can I make an AI coding agent produce tested code ready for review

Give the agent a narrow task, require it to inspect the existing test setup first, and make it run the smallest relevant test slice before it writes a summary. GitHub’s review flow is built around pull requests, checks, and review comments, so your agent should hand back code that already fits that shape, not a rough draft that still needs discovery work.

Start by telling the agent exactly what is in scope. Name the file, the behavior, the test framework if you know it, and the output you want: changed code, added or updated tests, and the command it used to verify them. Visual Studio Code’s agent guidance says to inspect the project’s testing setup first, reuse the project’s conventions, and run baseline tests so you can separate old failures from new ones.

A good prompt is specific enough that the agent cannot wander. Ask it to identify where tests belong, what command runs one test file or one related suite, and which existing test shows the project’s naming and mocking style. Then tell it not to edit implementation code until it has described the plan and confirmed the test command. That sequence matters because generated tests often fail review when they mirror the code instead of the requirements.

Make the agent prove the current state before it changes anything. The baseline run should show which tests already pass or fail, because a pull request review is easier when the checks tab and the diff tell the same story. GitHub documents checks as the mechanism that shows whether changes passed validation, and required checks must pass before merge on protected branches.

Keep the change small enough that one reviewer can inspect it in one sitting. GitHub recommends pull requests as the place to discuss and validate changes, and it also supports stacked pull requests when work is too large for one review. For AI-assisted work, that means one behavior, one branch, one test target, one review. Large scopes push the agent toward broad refactors and vague tests, which are harder to trust and slower to review.

Require the agent to write tests before it claims the code is done. The useful pattern is: first ask for a test plan, then ask for the tests, then ask for the implementation if the test exposes a missing behavior. VS Code’s testing guide for AI says to define the behavior to test, keep expected results explicit, and avoid computing the expected value with the function under test. That is the part people get wrong, because a test that reuses the same bug as the implementation is not a real check.

Tell the agent to run the new or affected tests, not just the whole suite. The output you want is concrete: command, pass count, fail count, skipped tests, and a short explanation of any failure. If the tests fail, force the agent to label the cause as setup, incorrect expectation, or a possible implementation bug. That extra classification is inconvenient, but it prevents the common move where the agent weakens the assertion until the test turns green.

Make review a separate step from generation. A review-ready result includes a clean diff, passing checks, and comments that tell the reviewer what changed and why. GitHub’s review flow supports inline comments, suggestions, and decisions such as approve or request changes, so ask the agent to present the work in the same structure: summary, files changed, tests run, and known limitations. A reviewer should not have to reconstruct the process from the commit history.

Use checkpoints or a rollback point if your tool supports them. VS Code’s agent best practices recommend checkpoints so you can review progress, rewind when the agent drifts, or request a code review on the resulting pull request. That is useful when the agent starts solving adjacent problems, because adjacent fixes are how a focused task turns into an unreviewable rewrite.

Ask for tests that fail for the right reason. Good tests assert observable behavior, not internal implementation details, and they keep expected values independent from the code under test. If the project uses mocks, make the agent explain what the mock protects and what behavior still needs to run for real. The inconvenient part is that mock-heavy tests can look thorough while skipping the thing you actually care about, so review the assertions, not just the coverage report.

If the project already has CI, align the agent’s local verification with that pipeline. GitHub describes status checks as the result of builds, tests, code scanning, or deployment checks, and merge request pipelines in GitLab are configured to run jobs on merge request events. The practical move is simple: run the same kind of tests locally that the branch protection or merge request pipeline will enforce, so the agent is not handing you code that only passes in its own environment.

A reliable prompt sequence looks like this: inspect the test setup, report the baseline, propose the smallest test set for one behavior, add the tests, run them, fix failures, then summarize what is ready for review. That sequence keeps the agent in the same order a human reviewer expects: evidence first, code second, explanation last. It also makes it obvious when the agent skipped testing and wrote a story instead.

Example prompt:

Inspect this project’s test setup first. Do not edit files yet. Identify the framework, one existing test that matches the project’s style, and the command to run the smallest relevant test slice. Then add or update tests for this one behavior only: <describe behavior>. Run the affected tests, report the exact command and results, and stop if a failure looks like an implementation bug.

That prompt works because it forces the agent to show its assumptions before it changes code. It also blocks the two failure modes that waste review time: a code-only change with no test evidence, and a test-only change that encodes the wrong expectation. When the agent returns, you should be able to open the diff, read the failing or passing command, and decide whether the branch is ready for review without re-running the whole investigation.

For teams, save the rules as project instructions. VS Code recommends keeping workflow safeguards in version control so the agent follows the same testing and review steps across sessions. Put the non-negotiables there: inspect the setup first, do not weaken assertions to make a test pass, run the relevant tests after every code change, and report failures plainly. That is the cheapest way to make AI output look like work a reviewer can actually accept.

FAQ

Should the agent write tests before implementation or after?

Write the tests first when the behavior is clear. If the project has no tests, ask the agent to propose the framework and the smallest setup before installing anything. That keeps the first change reviewable and avoids mixing architecture decisions with the feature itself.

What if the agent says the tests pass but I do not trust them?

Read the diff, the test file, and the exact command output. Passing tests are useful only when the assertions cover the intended behavior and the command was the one your project actually uses in CI or review.

How do I keep the agent from making unrelated edits?

Constrain the prompt to one file or one behavior, ask for no unrelated refactors, and use checkpoints or a branch you can compare against the baseline. GitHub’s pull request model and review comments work best when the diff stays small and focused.

What should I ask the agent to report before I review the branch?

Ask for the files changed, the tests run, the exact command, the result counts, and any known limitations. That gives the reviewer the same core facts they would look for in a pull request check summary and review notes.