What happens when two AI agents review each other's code?

Teams are wiring AI coding agents together so that one agent writes code, a second agent reviews it, and the two iterate until any issues have been cleared. The appeal is easy to see, with faster feedback, a second perspective on every pull request, and less time spent on routine review work.

The pattern is useful, but it has a limit that teams need to design around. When two agents agree, you just learn that they reached the same conclusion, but you still have no evidence that the conclusion is right. This page explains how a two-agent review loop works, what a second agent adds, where the model breaks down, and how to build the loop so you can trust what merges.

What happens when two AI agents review each other's code?

When two AI agents review each other's code, one agent generates a change and a second agent evaluates that change in a separate, isolated session, flagging issues and suggesting fixes until the two converge on a resolved state. Teams often call this multi-agent code review, or "AI checking AI." The reviewing agent runs with its own prompt, its own role framing, and no memory of the writing agent's reasoning, so it approaches the change as a critic instead of as the original author.

In practice, the loop runs inside your pull request workflow. The writing agent opens a change, the reviewing agent reads the diff and the surrounding code and posts findings, and the writing agent revises. The cycle repeats until the reviewer raises no further blocking issues.

Both agents write and judge code by predicting the most likely next piece of text based on patterns from their training, so the same input can produce a different answer on another run. In a two-agent loop, one probabilistic system checks another, and neither agent runs a repeatable test against your standards, which limits what the pattern can prove.

Why does multi-agent code review matter now?

Coding agents have moved from experiment to daily practice, and teams now receive pull requests that arrive larger and faster than human reviewers can read them. Many teams reach for a second agent to absorb some of that load.

The truth is, human reviewers make a weak safety net at agent volume. People follow AI advice about 80% of the time even when the AI is wrong (Wharton, 2026), so a reviewer skimming a large pull request coming from an agent is likely to accept what the agent suggests. A second agent that reads every line looks like relief from this.

The risk appears when teams mistake agreement for assurance. An indication that something was reviewed by an agent invites people to lower their scrutiny at the moment a change deserves more of it. Teams that understand what the second agent contributes, and what it cannot, keep the pattern from wearing down their confidence in what ships.

How does a two-agent review loop work?

A two-agent review loop has four parts, and each one shapes what the pattern catches.

• The writing agent produces a change against a task or issue. It holds the full context of its own intent, which helps it build the change and also makes it likely to defend the approach it already chose.
• The reviewing agent receives the diff in a fresh session. Its prompt and role framing push it to look where the writer did not, such as edge cases, error handling, input validation, and names that obscure intent.
• The iteration step carries findings back to the writing agent, which revises and resubmits. Each cycle consumes time and tokens.
• The exit condition ends the loop when the reviewer reports no blocking issues or a person steps in.

Teams most often get the exit condition wrong. When the loop converges, the two agents have stopped disagreeing, and nothing yet shows that the code builds, passes tests, meets your security policy, or does what the issue asked.

What can a second agent add?

A second agent in a separate session brings a different vantage point, and that vantage point catches real issues.

• Different prompts surface missed edge cases. A reviewing agent framed as a security critic probes input handling that the writing agent treated as an afterthought, and a reviewer framed for maintainability flags the function nobody will understand in six months.
• Role framing reduces author bias. The writing agent reasons toward justifying its own design. A fresh agent with no memory of that reasoning evaluates the change on its merits, which is why the isolated session matters as much as the second agent.
• Consistent coverage on large diffs. A reviewing agent applies the same attention to the end of a long diff as to the beginning, which helps catch the mechanical issues a fatigued person skims past.

These gains are real, and they stop at the point where both agents share the same weaknesses.

Why is agent agreement different from verification?

Two agents reaching the same verdict feels like confirmation, yet several structural reasons keep that agreement from counting as evidence.

When the writer and reviewer run on the same model family, they inherit the same training biases, the same gaps in context, and the same assumptions about your codebase. A blind spot in the model shows up in both agents at once, so they tend to agree in exactly the places where both are wrong.

LLM judges also fail to stay consistent with themselves. Researchers found that frontier LLM judges change 7% to 17% of their verdicts when asked the same question again, and that 7% to 34% of verdicts flip when another model argues against them, with most flips moving away from the right answer (Kumar et al., 2026; Zhao et al., 2026). A reviewer that changes its answer on a rerun cannot be the gate that decides what merges.

Correctness, security, and compliance are properties that teams demonstrate with repeatable checks. Two probabilistic systems can reach consensus on a change, and teams still need an independent method to produce the evidence that the change is ready.

What failure modes should you design against?

Two-agent loops fail in specific, predictable ways. Name them early and build controls against each one.

• Shared blind spots. Both agents miss the same requirement or edge case because they share a model and its limitations. The loop converges cleanly, and the team ships a defect that two agents reviewed.
• Unproductive feedback loops. The agents rewrite code back and forth, polishing surface details while the underlying design problem stays in place. The iteration count climbs while quality stays flat.
• Context mismatch. The reviewing agent lacks the architectural or business context to judge whether a change fits your system, so it approves a change that is reasonable in isolation and wrong for the codebase. Agents also perform worse as codebases grow, with pass rates falling from 91% under 10K lines to 15% over 50K lines (RepoMod-Bench, 2026).
• False confidence. A "reviewed by AI" label signals to people that the change is safe, so reviewers lower their scrutiny when they should raise it.
• Cost and latency growth. Each iteration consumes tokens and wall-clock time. Repeated cycles can burn significant spend without a matching gain in quality, and the loop grinds hardest on the large, complex codebases enterprises run.

Each of these failure modes traces back to a loop that has no fixed reference point. Teams fix that by giving the loop one.

How do you put deterministic verification in the middle?

Teams make a two-agent loop trustworthy when they stop treating agent agreement as the exit condition and require independent evidence on every iteration.

Require deterministic checks on every candidate change. Builds, tests, code quality analysis, security analysis, and policy checks give you a verdict that returns the same result every run, regardless of which agent wrote the code or which agent reviewed it. The AI agents reason about intent and surface anomalies, and the deterministic layer confirms what rules can confirm.

Route high-signal findings back to the right agent. Send each finding with its location, severity, and remediation context, so the next iteration acts on specific evidence instead of another round of opinion. A loop built this way converges on a passing standard.

Keep merge gates independent of agent agreement. Base the merge decision on checks that are consistent, repeatable, explainable, and auditable. Trustworthy verification uses a different method from the one that generated the code, with a clear separation of duties between the tools that write, the tools that review, and the gate that decides.

How can SonarQube and Gitar help you verify a multi-agent review loop?

SonarQube gives a multi-agent review loop an independent, deterministic layer that returns the same verdict every run. It analyzes every change against your defined quality and security standards, across syntax, data flow, control flow, and architecture, so the result stays the same when an agent reruns or reconsiders.

The SonarQube quality gate decides what merges. It evaluates each change against the conditions your organization sets and returns a consistent pass or fail that agents and people can both trust, which keeps the merge decision independent of whether the agents in the loop agree. Because SonarQube applies the same standard to every change, whichever agent or person wrote it, your team gets a consistent, auditable record of every check.

Gitar provides the agentic layer on the same pull request. Gitar reviews every pull request in context for logic and intent, diagnoses CI failures, and commits fixes under rules you write in plain language, iterating until CI is green. Gitar is a separate solution that works alongside SonarQube, and together they give every pull request deterministic verification and agentic review, with each layer catching what the other cannot.

To get started, connect SonarQube to your repository and set a quality gate that every change must pass before merge, whatever your review agents report. Then add Gitar to review, diagnose, and fix each pull request until it's green.

Learn more

• Deterministic vs probabilistic code analysis: learn how each method produces evidence and where each fits in your review workflow.
• Set the pass or fail standard every change must meet before merge.
• What is zero-trust verification in software delivery?: see how layered, independent checks close the review gap that agent volume creates.
• See how Gitar diagnoses CI failures and iterates on fixes until the build is green.