The prompt that makes AI coding agents check each other’s work

Short answer

Plan mode helps an AI coding agent think before it edits. But plan mode does not solve the hardest problem: judgment.

If you are not technical, you may not know whether the plan is too complex, whether the agent guessed an API, whether the implementation missed edge cases, or whether the tests actually prove anything.

The better workflow is to make agents check each other’s work: one agent plans, one critiques, one builds, one reviews, and the docs-check step keeps everyone honest.

Why plan mode is not enough

Plan mode gives you a better starting point, but it can still produce a confident bad plan.

Coding agents are very good at sounding reasonable. They can explain a path, create files, and finish a task while still missing the right API surface, ignoring an existing pattern, skipping a test, or overbuilding the solution.

That is especially risky for non-coders. The interface makes the plan look official, but the user still needs a way to evaluate the plan before trusting it.

The better workflow

Use a second loop. Instead of treating one agent as the only brain in the room, split the work into roles.

The point is not to create bureaucracy. The point is to catch the mistakes that one model is likely to miss when it reviews its own work.

Role 1: plan arbiter

A plan arbiter compares two possible plans before implementation starts.

This is useful when the task is bigger than a small text change: refactoring a component, adding auth, changing a database schema, building a new page, integrating an API, or fixing a bug you do not fully understand.

Ask one agent for a plan. Ask another agent to critique it, propose alternatives, and identify risks. Then ask for a final merged plan that keeps the simplest safe path.

Role 2: agent watchdog

An agent watchdog reviews the implementation against the original goal and the approved plan.

This is different from asking the same agent, "Does your work look good?" Self-review is weak because the model often defends its own path. A second agent has a cleaner job: inspect the diff, compare it to the plan, run or request tests, and point out what is missing.

For real work, the watchdog should care about behavior, not compliments. It should look for regressions, missing states, broken mobile layouts, missing tests, stale docs, and unnecessary changes.

• Give it the original request.
• Give it the final plan.
• Give it the changed files or diff.
• Give it the test command.
• Ask for only risks, missed requirements, and concrete fixes.
• Have the builder apply the fixes, then review again if the task is high-risk.

Role 3: docs checker

A docs checker exists because coding agents often guess APIs from memory.

That is dangerous for anything involving fast-moving tools: SDKs, payment APIs, auth libraries, AI model APIs, framework updates, database clients, browser APIs, and deployment platforms.

Anthropic’s Claude Code best-practices guidance emphasizes giving Claude context, using tools, and making it explore the codebase before acting. OpenAI’s Codex guidance similarly points to project instructions and repo-specific guidance. For integrations, add one more rule: check the current docs before writing code.

Sources: Anthropic: Claude Code best practices, OpenAI: Codex best practices

Where Claude Code and Codex fit

You do not need to turn this into a tool rivalry. Claude Code and Codex can play different roles in the same workflow.

One practical pattern is to use the stronger planner for exploration and risk analysis, then use the faster repo-focused agent for implementation, then bring the first agent back as reviewer. The exact pairing matters less than the separation of roles.

This also connects to context engineering. The reviewer should not start from zero. It needs the goal, plan, files, constraints, and acceptance criteria.

The non-coder version

If you are not technical, do not try to judge the code line by line. Judge the process.

Ask the second agent to explain risk in plain English and require it to name the file, behavior, and test that proves the issue. That keeps the review grounded instead of turning into abstract advice.

• What user flow could break?
• What did the builder change that was not requested?
• What existing pattern did it ignore?
• What docs should be checked before trusting this?
• What test or manual check would prove this works?
• What is the simplest fix if something is wrong?

A reusable prompt

Use this when you want a second agent to review AI-generated code.

• You are the reviewer, not the builder.
• Compare the implementation against the original request and approved plan.
• Inspect the changed files and identify only concrete risks.
• Check whether the implementation follows existing project patterns.
• Flag guessed APIs and tell me which official docs should be checked.
• List missing tests or manual checks.
• Do not rewrite everything. Give the smallest fixes needed.

What to avoid

The goal is better judgment, not more AI chatter.

If you ask five agents vague questions, you get five vague answers. Keep each role narrow and force the review to point back to the request, plan, files, docs, and tests.

• Do not let the same agent be the only reviewer of its own work.
• Do not accept "looks good" as review.
• Do not run a plan debate for tiny changes.
• Do not let reviewers propose giant rewrites unless the task truly needs one.
• Do not skip official docs for integrations.
• Do not ship without at least one test or manual verification step.

Final answer

Plan mode is useful, but it is not enough for serious AI coding work.

The practical upgrade is a review loop: plan, critique, build, check docs, review the diff, and fix what was missed. That is how non-coders and builders can use Claude Code, Codex, and other coding agents with more confidence instead of hoping the first answer is right.