How to Review AI-Generated Code with Multiple AI Agents

Having one model review code written by a model from a different family is becoming increasingly popular. You 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. That sounds useful, but it raises an important question: what information are we giving the reviewing model? Is reviewing the code enough? A typical AI code review might include the final code, the diff, and the commit history. That tells the reviewer what changed, but not why it changed. In the era of coding agents, much of the reasoning behind an implementation lives inside the agent session. That session can include: The original prompts The agent’s responses Tool calls and commands Files the agent inspected Constraints provided by the developer Decisions and alternatives considered along the way Without 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. Trying an adversarial review without context I built an Arcade feature with Codex, then asked Claude Code to review the result. The 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. It could review the code as written. It could not compare that code against my original intent. This is the missing piece in many cross-agent reviews. The reviewing model sees the destination, but not the journey that led there. Reviewing with the full agent session Entire captures the context behind agent-assisted work and connects it to Git through checkpoints. Instead of giving a reviewer only the final diff, Entire can provide the prompts, responses, tool activity, file changes, and decisions that produced it. From an Entire-enabled repository, either you or your coding agent can start a review by running: entire review On the first run, Entire guides you through creating a review profile. 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. You can select multiple reviewers. Entire runs them in parallel, then sends their findings to a judge that combines them into one final report. The judge resolves contradictions, removes duplicate findings, and prioritizes issues supported by evidence. Checking the code against the original prompt Because Entire stores the original prompts and agent responses, I could add another check to the review profile: Compare the final code against what I actually prompted for. To edit the checks in a review profile, run: entire review --edit This turns the original prompt into part of the review criteria. The reviewer is no longer limited to asking, “Is this code correct?” It can also ask: Did the agent build what was requested? Were any requirements missed? Did the implementation add behavior that was never requested? Did the agent drift away from the original intent? Once the profile is ready, start the configured review: entire review general The 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. Catching intent drift In my Arcade example, I asked for two ad cards, one for each game. The agent built three. A traditional code review might not flag that. Three cards are not necessarily broken code. The implementation could be clean, tested, and technically correct. But it was not what I requested. Because 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. That is the difference between reviewing only the output and reviewing the complete body of work. Code review for the era of agents Cross-agent adversarial review is a useful start. Different models can notice different problems. But changing the reviewing model is only part of the solution. The reviewer also needs the context behind the implementation. When 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. That is code review built for the era of agents. Try Entire today or explore the entire review documentation.