How to Set Up Automated Code Review with Multiple AI Agents

Why One AI Agent Can’t Review Its Own Code

Ask an AI agent to write a function, then immediately ask it to review that same function in the same conversation — and you’ll mostly get agreement. It won’t find the off-by-one error it just introduced. It won’t notice the missing null check it glossed over. It already committed to that code. Its context is saturated with the reasoning that produced it.

This is the core problem with single-agent automated code review: the model that wrote the code is too close to it. The same assumptions that led to the implementation carry forward into the review. You don’t get a second opinion — you get a rephrased version of the first one.

The fix is structural, not prompt-based. You need separate agents in separate context windows, ideally using different underlying models, with one playing builder and the other playing adversarial reviewer. This is what makes multi-agent PR review actually useful — not just faster, but qualitatively different from what any single session can catch.

This guide walks through exactly how to set that up.

The Problem: Context Collapse in Single-Agent Review

When a single AI agent generates code and then reviews it in the same session, a few things go wrong.

First, context rot sets in. Long sessions accumulate assumptions, partial reasoning, and stale conclusions that quietly influence later outputs. The agent starts to “remember” its earlier decisions in ways that skew its judgment.

Second, the model inherits its own blind spots. If the agent missed an edge case during generation, it’s unlikely to surface that same edge case during review — it’s operating from the same mental model of the problem.

Third, there’s a kind of confirmation bias at play. The agent produced that code. Criticizing it sharply requires contradicting itself, which most models are architecturally inclined to avoid.

AI agent failure modes often look like this: the model knows something in the abstract but fails to apply it when the context pulls in the opposite direction. A reviewer that wrote the code is always fighting its own context.

The solution isn’t better prompting. It’s isolation.

The Architecture: Builder + Validator in Separate Sessions

The core pattern is simple: split code generation and code review into two completely independent agent sessions.

Builder agent — writes the code. Can be any capable coding model. Context includes the task, codebase context, and any relevant specs.
Validator agent — receives only the diff or PR output from the builder. Has no knowledge of the original task framing or the reasoning that produced the code. Its job is adversarial: find problems.

This is closely related to what’s described in the Claude Code builder-validator chain, where a generation agent hands off to a separate evaluation agent that operates without the accumulated bias of the first session.

The key constraint: the validator must not see the builder’s reasoning. Only the output. This forces genuinely independent evaluation.

Why Different Models Help

Using different underlying models for builder and validator adds another layer of independence. Claude and Codex, for example, have different training approaches and tend to make different kinds of mistakes. A bug that Claude is likely to overlook might be obvious to Codex, and vice versa.

The OpenAI Codex plugin for Claude Code enables exactly this kind of cross-provider review — running one agent’s output past a model from a different provider to catch what the first missed. This isn’t theoretical; teams using cross-provider review report catching meaningfully different categories of bugs than single-model review catches.

Step 1: Set Up Your Builder Agent

Your builder agent handles the actual code generation. Configuration here should optimize for completeness and correctness, not speed.

What to include in the builder’s context:

The task description or ticket
Relevant codebase sections (keep it scoped — only what the agent needs)
Any architectural constraints or style guides
The spec or acceptance criteria

What to exclude:

Historical review comments (these bias the output toward past decisions)
Other agents’ outputs from prior sessions
Anything that would make the agent feel like it’s already been reviewed

A good builder prompt ends with an explicit instruction to output a clean diff or patch file — not just modified files. You want something the validator can consume without needing to reconstruct the change.

You are a senior software engineer implementing the following feature:

[TASK DESCRIPTION]

Context files:
[RELEVANT_CODE]

Output a unified diff of all changes. Do not include explanation — only the diff.

The absence of explanation in the output matters. Explanations carry the builder’s reasoning into the validator’s context, which defeats the purpose.

Step 2: Configure the Validator Agent

The validator is a fresh session with no knowledge of the builder’s reasoning. It receives only the diff and the original requirements.

What to include in the validator’s context:

The original task requirements (not the builder’s interpretation of them)
The diff produced by the builder
A structured review rubric

What to exclude:

The builder’s explanation or reasoning
The builder’s session history
Any intermediate outputs from the builder

A structured review rubric helps the validator be systematic rather than vague. Here’s a starting template:

You are a senior code reviewer. You did not write this code. Your job is to find problems.

Review the following diff against these requirements:
[REQUIREMENTS]

Diff:
[DIFF_OUTPUT]

Review for:
1. Correctness — does it do what the requirements say?
2. Edge cases — what inputs or states are unhandled?
3. Security — are there injection risks, auth issues, or exposed secrets?
4. Performance — any obvious bottlenecks or N+1 queries?
5. Error handling — what happens when things fail?
6. Breaking changes — does this affect any existing interfaces?

For each issue, specify: file, line number, severity (critical/major/minor), and a clear explanation.
Do not approve changes that have critical issues.

The explicit instruction “you did not write this code” is worth including. It frames the agent’s role as external reviewer, not self-editor.

Step 3: Build the Handoff Pipeline

The builder and validator need a clean handoff mechanism. The diff from the builder becomes the input to the validator. How you automate this depends on your setup, but the key is keeping the sessions isolated.

Option A: Manual handoff (good for getting started)

Run the builder session, copy the diff output.
Open a fresh session with the validator prompt.
Paste the diff.
Collect the review output.

This is tedious but lets you validate the approach before automating it.

Option B: Script-based pipeline

A simple shell script can automate the handoff:

#!/bin/bash

# Generate diff from builder agent
DIFF=$(claude --session-id "builder-$(date +%s)" \
  --system "$(cat builder_prompt.txt)" \
  --message "$(cat task.txt)" \
  --context "$(cat relevant_code.txt)")

# Save diff to temp file
echo "$DIFF" > /tmp/current_diff.txt

# Run validator in separate session
REVIEW=$(claude --session-id "validator-$(date +%s)" \
  --system "$(cat validator_prompt.txt)" \
  --message "$(cat requirements.txt)\n\nDiff:\n$DIFF")

echo "$REVIEW" > review_output.txt

The --session-id flags use timestamps to ensure each run gets a completely fresh session. No context bleeds between runs.

Option C: PR-triggered pipeline

For production use, wire this into your CI system. When a PR is opened or a commit is pushed to a feature branch, the pipeline triggers automatically:

Builder generates a review of its own diff (optional — this becomes a self-check, useful for catching syntax errors)
Validator receives only the diff from the PR, with no builder context
Review output posts as a PR comment

This mirrors how enterprise teams like Stripe structure their AI coding harnesses — automated agents doing structured work at the PR level, not just in the editor.

Step 4: Add a Third Agent for Adversarial Depth (Optional but Powerful)

Two agents catch more than one. But three, structured correctly, catch more still.

The planner-generator-evaluator pattern gives you a useful model here: one agent proposes, one generates, one evaluates. For code review, this maps to:

Architect agent — reviews the diff for design and structural concerns
Security agent — reviews specifically for vulnerabilities, input validation, auth issues
QA agent — reviews for testability, missing test cases, and edge case coverage

Each runs in a separate session against the same diff. They don’t see each other’s output until a final aggregation step.

This approach is similar to stochastic multi-agent consensus — running multiple independent agents over the same material and aggregating results to surface what any single agent would miss. Disagreement between agents is often the most valuable signal.

The aggregation prompt can be simple:

Three AI agents reviewed the following diff. Here are their outputs:

[ARCHITECT_REVIEW]
[SECURITY_REVIEW]
[QA_REVIEW]

Consolidate these into a single prioritized review. Remove duplicates. 
Highlight any issue flagged by multiple agents. Flag any contradictions between reviewers.

Step 5: Tune the Validator for Your Codebase

Generic review prompts produce generic feedback. The validator gets better as you give it more specific context about your codebase’s conventions and known problem areas.

Things to add over time:

Known anti-patterns — “Flag any use of eval() in this codebase — it’s forbidden.”
Architecture constraints — “All database access must go through the repository layer.”
Common bugs from your history — “Check for missing await on async calls — this has caused production issues before.”
Style guide references — “Variable names should be camelCase. Function names should be descriptive verbs.”

This is where harness engineering starts to matter. The validator isn’t just a model — it’s a model plus a carefully maintained set of constraints and context that encode your team’s accumulated knowledge about what goes wrong.

You can also wire in binary assertions rather than purely subjective review. Binary assertions versus subjective evals is a useful distinction here: some checks have clear pass/fail criteria (is there a missing null check? does this function handle empty arrays?), while others require judgment. Structure your validator prompts to separate these clearly.

Step 6: Handle Model Costs Sensibly

Running multiple agents on every PR gets expensive fast if you’re not careful. A few things help.

Use a tiered model approach. Run a fast, cheap model (like Haiku or GPT-4o mini) for initial triage — catching obvious issues like syntax errors, naming problems, and obvious logic bugs. Reserve Opus or o3 for deep review of complex diffs or when the fast model flags something serious.

Multi-model routing lets you build this routing logic into the pipeline itself, so expensive models only run when the diff complexity or risk level warrants it.

Scope the context. The validator doesn’t need the entire codebase — just the diff and any files it directly touches. Keeping context tight keeps token counts down and often improves review quality (less noise).

Cache common context. Your style guide, architectural constraints, and known anti-patterns don’t change between PRs. Pass them as a cached system prompt where your provider supports it.

Common Mistakes to Avoid

Letting the builder explain itself to the validator

This is the most common mistake. It feels helpful to send the builder’s reasoning alongside the diff, but it poisons the validator’s independence. The validator will unconsciously adopt the builder’s framing and miss issues that framing obscures.

Keep the handoff to: diff + original requirements. Nothing else.

Using the same session for multiple PRs

Session reuse means context accumulates. By the fifth PR, your validator has seen four prior diffs and is subtly influenced by them. Always start fresh sessions. Context rot is real, and it compounds.

Treating all review output as equal

Not all issues the validator surfaces will be correct or important. Build a step into your pipeline where a human (or a triage agent) categorizes the validator’s output before it goes to the developer. Raw validator output dumped into a PR comment as-is creates noise and erodes trust in the system.

Skipping the rubric

An unstructured validator prompt produces inconsistent results. Two validators reviewing the same diff with the same prompt but different random seeds can produce very different outputs. A structured rubric — with explicit categories, severity levels, and output format — makes results consistent and comparable across PRs.

How Remy Fits Into This

If you’re working in Remy, the spec-as-source-of-truth architecture changes the review problem in an interesting way. The spec is the authoritative description of what the app does. Code is a compiled artifact derived from it.

This means a validator agent can review generated code not just against a generic rubric, but against the spec itself. Does this function’s behavior match what the spec says it should do? Are the data types consistent with the spec’s annotations? Does this handle the edge cases the spec explicitly calls out?

That’s a qualitatively different kind of review — and a more reliable one, because the ground truth is explicit rather than inferred.

Remy’s model-agnostic architecture also means you’re not locked into a single provider for your review pipeline. You can route generation to one model and validation to another, which is exactly the cross-provider diversity that makes adversarial review work. As models improve, the compiled output gets better without changing your spec — and your review pipeline tightens automatically.

You can try Remy at mindstudio.ai/remy.

Frequently Asked Questions

Why can’t I just ask one agent to “be critical” when reviewing its own code?

You can, and it will try. But instruction-level prompting can’t fully overcome context-level bias. The agent still has access to all the reasoning it used to generate the code, which shapes what it notices and what it dismisses. Structural separation — different session, ideally different model — creates genuine independence that prompting alone can’t replicate.

Do the builder and validator have to use different models?

No, but it helps. Two Claude instances in separate sessions will already catch more than one, because context isolation removes the specific blind spots introduced during generation. Using different models (e.g., Claude for building and Codex for validating) adds another layer of diversity, since different models tend to make different categories of mistakes.

How do I handle large diffs that exceed context limits?

Split the diff into logical chunks — by file, by feature, or by change type — and run a validator on each chunk separately. This is the split-and-merge pattern applied to review: parallelize across chunks, then aggregate the outputs. For very large PRs, this is often better practice anyway, since reviewers (human or AI) get worse as diff size grows.

What’s the right severity threshold for blocking a PR?

That’s a team decision, but a useful starting point: block on anything the validator flags as “critical” (security issues, data corruption risk, broken interfaces). Flag “major” issues for mandatory human review before merge. Let “minor” issues through with an automated comment. Tune this over time based on false positive rates.

Should the validator see the test files?

Yes, with a caveat. The validator should see what tests exist — to check whether the implementation is actually tested. But it shouldn’t use the tests as the definition of correctness. A test can pass and still be wrong if it was written alongside a buggy implementation. The spec or requirements should be the validator’s ground truth for correctness, not the test suite.

How do I prevent the validator from being too aggressive?

A validator that flags everything as critical is worse than useless — developers stop reading the output. A few things help: explicit severity definitions in the rubric, a cap on the number of issues surfaced per review (force prioritization), and periodic calibration where you review the validator’s output against what human reviewers actually agreed with. Treat the validator’s accuracy as something to measure and improve over time.

Key Takeaways

An AI agent that writes code and reviews it in the same session isn’t doing independent review — it’s echo-checking its own assumptions.
The fix is structural: separate sessions, ideally separate models, with the validator receiving only the diff and original requirements.
A three-agent setup — architect, security, QA — catches categorically more than a two-agent setup, especially on complex diffs.
Keep the handoff clean: no builder reasoning, no accumulated session context, no cross-contamination.
Treat validator output as a first pass, not ground truth. Build triage into the pipeline before developer-facing output.
A structured rubric with explicit severity levels makes review output consistent, comparable, and actionable.

The goal isn’t to eliminate human review — it’s to ensure the issues that reach a human reviewer are the ones worth their attention, not the ones an AI could have caught automatically.

Try Remy if you want a spec-driven development environment where validation agents can review code against an authoritative source of truth, not just a set of heuristics.