{"slug": "how-to-review-ai-generated-code-with-multiple-ai-agents", "title": "How to Review AI-Generated Code with Multiple AI Agents", "summary": "Entire, a developer tooling company, introduced a workflow that lets developers review AI-generated code with multiple AI agents from different model families, such as using Codex to build and Claude Code to review. The company's platform captures the full agent session context, including prompts and decisions, enabling reviewers to compare the final code against the original intent. In a test case, this approach caught 'intent drift' where the agent built three ad cards instead of the requested two, a discrepancy a traditional code review missed.", "body_md": "Having one model review code written by a model from a different family is becoming increasingly popular.\n\nYou might build a feature with Codex, then ask Claude Code to review it. The idea is that each provider trains its models differently, so the reviewing model may catch problems the original model missed.\n\nThat sounds useful, but it raises an important question: what information are we giving the reviewing model?\n\nA typical AI code review might include the final code, the diff, and the commit history.\n\nThat tells the reviewer what changed, but not why it changed.\n\nIn the era of coding agents, much of the reasoning behind an implementation lives inside the [agent session](https://docs.entire.io/guides/sessions/overview). That session can include:\n\nWithout that context, a model can identify potential bugs, questionable patterns, or missing tests. What it cannot reliably determine is whether the final implementation matches what the developer actually requested.\n\nI built an Arcade feature with Codex, then asked Claude Code to review the result.\n\nThe review produced reasonable feedback, but it felt incomplete and superficial. Claude could inspect the implementation, but it did not have the conversation that produced it.\n\nIt could review the code as written. It could not compare that code against my original intent.\n\nThis is the missing piece in many cross-agent reviews. The reviewing model sees the destination, but not the journey that led there.\n\n[Entire](https://entire.io/) captures the context behind agent-assisted work and connects it to Git through [checkpoints](https://docs.entire.io/guides/checkpoints/overview).\n\nInstead of giving a reviewer only the final diff, Entire can provide the prompts, responses, tool activity, file changes, and decisions that produced it.\n\nFrom an Entire-enabled repository, either you or your coding agent can start a review by running:\n\n```\nentire review\n```\n\nOn the first run, Entire guides you through creating a [review profile](https://docs.entire.io/guides/configuration/review-profiles). A review profile is a reusable configuration that defines what should be checked, which agents should perform the review, and which agent should consolidate the results.\n\nYou can select multiple reviewers. Entire runs them in parallel, then sends their findings to a judge that combines them into one final report.\n\nThe judge resolves contradictions, removes duplicate findings, and prioritizes issues supported by evidence.\n\nBecause Entire stores the original prompts and agent responses, I could add another check to the review profile:\n\nCompare the final code against what I actually prompted for.\n\nTo edit the checks in a review profile, run:\n\n```\nentire review --edit\n```\n\nThis turns the original prompt into part of the review criteria.\n\nThe reviewer is no longer limited to asking, “Is this code correct?”\n\nIt can also ask:\n\nOnce the profile is ready, start the configured review:\n\n```\nentire review general\n```\n\nThe selected agents perform their reviews in parallel, and the judge produces a consolidated verdict. You can learn more about this workflow in the [cross-agent review tutorial](https://docs.entire.io/learn/review-and-recap-agent-work).\n\nIn my Arcade example, I asked for two ad cards, one for each game.\n\nThe agent built three.\n\nA traditional code review might not flag that. Three cards are not necessarily broken code. The implementation could be clean, tested, and technically correct.\n\nBut it was not what I requested.\n\nBecause Entire gave the reviewers access to the original prompt, the final report identified the extra card as intent drift. The implementation had drifted away from the stated requirement, even though the code itself appeared valid.\n\nThat is the difference between reviewing only the output and reviewing the complete body of work.\n\nCross-agent adversarial review is a useful start. Different models can notice different problems.\n\nBut changing the reviewing model is only part of the solution. The reviewer also needs the context behind the implementation.\n\nWhen the review includes the prompts, responses, decisions, and final code, it can evaluate more than technical correctness. It can determine whether the agent understood the task and delivered what was actually requested.\n\nThat is code review built for the era of agents.\n\n[Try Entire today](https://entire.io/) or explore the [ entire review documentation](https://docs.entire.io/cli-reference/review).", "url": "https://wpnews.pro/news/how-to-review-ai-generated-code-with-multiple-ai-agents", "canonical_source": "https://dev.to/entire/how-to-review-ai-generated-code-with-multiple-ai-agents-gdc", "published_at": "2026-08-14 22:40:09+00:00", "updated_at": "2026-08-14 23:11:21.416242+00:00", "lang": "en", "topics": ["ai-agents", "developer-tools", "ai-products", "ai-tools", "mlops"], "entities": ["Entire", "Codex", "Claude Code"], "alternates": {"html": "https://wpnews.pro/news/how-to-review-ai-generated-code-with-multiple-ai-agents", "markdown": "https://wpnews.pro/news/how-to-review-ai-generated-code-with-multiple-ai-agents.md", "text": "https://wpnews.pro/news/how-to-review-ai-generated-code-with-multiple-ai-agents.txt", "jsonld": "https://wpnews.pro/news/how-to-review-ai-generated-code-with-multiple-ai-agents.jsonld"}}