{"slug": "your-ai-coding-agent-just-finished-now-ask-it-to-attack-its-own-work", "title": "Your AI Coding Agent Just Finished. Now Ask It to Attack Its Own Work.", "summary": "A developer recommends that after an AI coding agent completes a task, users should ask it to perform an adversarial review of its own implementation, assuming the code contains bugs and trying to break it. This technique, which separates diagnosis from repair and uses role framing like 'You did not write this code,' can surface subtle issues that standard reviews miss.", "body_md": "AI coding agents are remarkably good at getting from “here’s what I want” to “here’s a working implementation.”\n\nThey are also remarkably good at being satisfied with what they just built.\n\nThat second trait is a problem.\n\nOne of the highest-leverage habits I’ve picked up when working with coding agents is surprisingly simple:\n\n**After the agent finishes a meaningful piece of work, ask it to perform an adversarial review of its own implementation.**\n\nNot:\n\nReview your work and make sure everything looks good.\n\nThat tends to produce a polite little victory lap.\n\nInstead, change the objective.\n\nAct as an adversarial reviewer. Assume this implementation contains subtle bugs, incorrect assumptions, security issues, race conditions, missing edge cases, or architectural problems. Your job is to find them. Do not defend the implementation. Try to break it.\n\nThe difference can be dramatic.\n\nWhen an agent is implementing a feature, its working objective is roughly:\n\nFind a plausible path to satisfying the requirements.\n\nOnce it has found that path, everything it sees is colored by the solution it just constructed.\n\nHumans do this too.\n\nYou write a function, run the obvious tests, and your brain quietly becomes the function’s defense attorney.\n\nThe code looks reasonable because you know what it was *supposed* to do.\n\nAn adversarial review gives the model a different role:\n\nAssume the implementation is wrong. Find the evidence.\n\nThat changes what it searches for.\n\nInstead of asking:\n\nit starts asking:\n\nSame model. Same context. Very different search space.\n\nSomething like this works well:\n\n```\nPerform an adversarial review of the implementation you just created.\n\nAssume there are bugs.\n\nDo not explain why the current implementation is good. Your job is to attack it.\n\nLook specifically for:\n\n- incorrect assumptions\n- edge cases\n- race conditions\n- security vulnerabilities\n- data corruption risks\n- failure/retry problems\n- backwards compatibility issues\n- performance regressions\n- missing validation\n- incorrect error handling\n- tests that pass without proving the intended behavior\n\nFor every issue you find:\n\n1. Describe the failure mode.\n2. Explain how it could occur in practice.\n3. Rate its severity.\n4. Point to the relevant code.\n5. Propose a concrete fix.\n\nDo not modify the code yet. First produce the review.\n```\n\nThat last instruction matters.\n\nI usually want the **review before the repair**.\n\nIf you immediately ask the agent to “find and fix any problems,” it can silently patch things while skipping the explanation. Separating diagnosis from remediation makes the reasoning inspectable.\n\nIt also lets you decide which findings are real.\n\nBecause the agent can absolutely invent problems too.\n\nFor larger changes, I sometimes push this further and create two explicit roles.\n\nFirst:\n\n```\nYou are the implementation engineer. Complete the feature.\n```\n\nThen:\n\n```\nYou are now a senior engineer reviewing this change before production deployment.\n\nYou did not write this code.\n\nAssume the implementation engineer was competent but may have made subtle mistakes.\n\nTry to reject this change.\n```\n\nThe phrase **“You did not write this code”** is surprisingly useful.\n\nObviously the model did write it. We are not performing metaphysical surgery on the transformer.\n\nBut role framing affects the kind of analysis the model performs. Removing psychological ownership, even fictitiously, tends to produce a more skeptical review.\n\nFor especially important code, you can go further:\n\n```\nImagine this change caused a production incident three months from now.\n\nWork backwards and identify the most plausible ways this implementation could have caused it.\n```\n\nNow you’re effectively asking for a miniature pre-mortem.\n\nThat often surfaces issues a generic code review misses.\n\nOne of the easiest ways to make AI review more useful is to demand concrete failure cases.\n\nBad:\n\nIs this implementation robust?\n\nBetter:\n\nGive me five concrete inputs, system states, or event sequences that could cause this implementation to behave incorrectly.\n\nEven better:\n\nFor each suspected bug, construct the smallest reproducible scenario that would demonstrate it.\n\nThis forces the critique toward falsifiable claims.\n\nFor example, instead of:\n\nThere may be a race condition here.\n\nyou want:\n\nRequest A reads balance=100. Request B reads balance=100. Both subtract 80. Both persist 20. The system has processed $160 of withdrawals from a $100 balance.\n\nThat is something you can reason about.\n\nAnd test.\n\nThis is where the workflow becomes particularly powerful.\n\nAfter the adversarial review, ask:\n\n```\nFor every credible issue you identified, write a regression test that fails against the current implementation.\n\nDo not change the production code yet.\n```\n\nNow the loop becomes:\n\n**Implement → Attack → Reproduce → Repair → Verify**\n\nThat is much stronger than:\n\n**Implement → Looks good → Ship**\n\nAnd it makes the agent prove its criticism.\n\nIf the supposed bug cannot be reproduced, maybe the review was wrong.\n\nIf the test fails, you now have both evidence and permanent coverage.\n\n“Review this code” is extremely underspecified.\n\nI get better results by running multiple targeted reviews.\n\nFor example:\n\n```\nReview this implementation as a hostile application security engineer.\n\nLook for ways an attacker could abuse inputs, authentication, authorization, state transitions, serialization, file access, network calls, or resource consumption.\nReview this as a distributed systems reliability engineer.\n\nFocus on partial failure, retries, duplicate execution, idempotency, ordering, timeouts, race conditions, stale state, and recovery after crashes.\nReview this as the maintainer of clients that depend on this API.\n\nLook for undocumented behavior changes, ambiguous contracts, backwards compatibility problems, surprising defaults, and error semantics.\nAssume this works correctly at 100 requests per day but fails badly at 10 million.\n\nFind the scaling problems.\n```\n\nThese prompts constrain the search.\n\nAnd constrained searches are often much better than asking a model to vaguely “think harder.”\n\nThe adversarial pass does something beyond finding implementation bugs.\n\nIt often discovers that your **requirements were incomplete**.\n\nSuppose the agent asks:\n\nWhat should happen if two users update the object simultaneously?\n\nMaybe you never specified that.\n\nOr:\n\nIs deleting this resource supposed to cascade to associated records?\n\nAlso unspecified.\n\nOr:\n\nShould this endpoint reveal whether an email address already exists?\n\nCongratulations, your coding agent just wandered into a product/security decision disguised as an implementation detail.\n\nThis is one of the more useful properties of adversarial review.\n\nIt exposes the negative space around your specification.\n\nThe original implementation task asks:\n\nWhat did the user tell me to build?\n\nThe adversarial task asks:\n\nWhat did the user forget to tell me?\n\nThat second question can be much more valuable.\n\nBecause “double-check” preserves the original frame.\n\nThe model is still trying to validate the solution.\n\nAdversarial review changes the success criterion.\n\nSuccess is no longer:\n\nThe implementation appears correct.\n\nSuccess becomes:\n\nI found a credible way this could fail.\n\nThat small prompt-engineering shift matters.\n\nIt is basically the software equivalent of red teaming.\n\nYou don't ask the red team to confirm that the defenses look sensible.\n\nYou tell them to get in.\n\nThere is an important caveat.\n\nAI-generated criticism is not automatically correct.\n\nA sufficiently determined model can find imaginary bugs with impressive confidence.\n\nSo I treat adversarial findings as hypotheses.\n\nThe hierarchy is roughly:\n\nThe farther down that list a finding sits, the less weight I give it.\n\nThis is also why asking the agent to produce reproduction cases and tests is so useful.\n\nIt converts prose into evidence.\n\nIf your tool supports it, another useful instruction is:\n\n```\nReview the actual git diff and all directly affected code.\n\nDo not rely on your memory of what you intended to change.\n```\n\nIntent is dangerous during review.\n\nThe implementation may not match the agent’s mental model of the implementation.\n\nThe diff is reality.\n\nFor significant changes, I also ask it to inspect neighboring code and call sites. Bugs frequently live at boundaries rather than inside the newly written function.\n\nFor meaningful changes, my preferred agent loop is increasingly something like:\n\n```\n1. Understand the task.\n2. Inspect the existing code.\n3. Propose an implementation plan.\n4. Implement the change.\n5. Run relevant tests.\n6. Perform an adversarial review.\n7. Produce concrete failure cases for credible findings.\n8. Add regression tests.\n9. Fix confirmed issues.\n10. Run the full relevant test suite.\n11. Review the final diff again.\n```\n\nYou can put this directly into an agent instruction file.\n\nThe marginal cost is tiny.\n\nThe value can be enormous.\n\nThe interesting thing here isn't really the prompt.\n\nIt's that AI coding agents become more useful when we stop treating them as a single programmer with a single continuous train of thought.\n\nThey can be the implementer.\n\nThen the reviewer.\n\nThen the attacker.\n\nThen the test engineer.\n\nThen the maintainer wondering what lunatic wrote this six months ago.\n\nThose roles optimize for different things.\n\nAnd one of the cheapest ways to improve AI-generated software is to deliberately make the model disagree with the version of itself that wrote the code.\n\nSo the next time your coding agent announces:\n\nImplementation complete. All tests pass.\n\nDon't congratulate it yet.\n\nTell it to try to destroy what it just built.", "url": "https://wpnews.pro/news/your-ai-coding-agent-just-finished-now-ask-it-to-attack-its-own-work", "canonical_source": "https://dev.to/jlmartel/your-ai-coding-agent-just-finished-now-ask-it-to-attack-its-own-work-54g3", "published_at": "2026-08-21 01:39:27+00:00", "updated_at": "2026-08-21 02:14:06.692206+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-agents", "developer-tools"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/your-ai-coding-agent-just-finished-now-ask-it-to-attack-its-own-work", "markdown": "https://wpnews.pro/news/your-ai-coding-agent-just-finished-now-ask-it-to-attack-its-own-work.md", "text": "https://wpnews.pro/news/your-ai-coding-agent-just-finished-now-ask-it-to-attack-its-own-work.txt", "jsonld": "https://wpnews.pro/news/your-ai-coding-agent-just-finished-now-ask-it-to-attack-its-own-work.jsonld"}}