· 7 min read
By the end of this guide, you will have a 4-agent code review team that examines pull requests from four distinct perspectives: security vulnerabilities, performance implications, code style and maintainability, and architectural alignment. Each agent produces a focused review, and a Coordinator merges them into a single, prioritized review document with actionable feedback.
Human code reviewers are stretched thin. They context-switch between reviewing PRs and doing their own work, which means reviews tend to focus on whatever catches the eye first -- often surface-level style issues rather than deeper security or architectural concerns. A multi-agent review team eliminates this problem by giving each concern its own dedicated reviewer that never gets tired, distracted, or rushed.
You need Claude Code installed and configured. For CI/CD integration, you will also want the Claude Agent SDK so you can trigger reviews programmatically on pull request events. Your codebase should be in a Git repository, and you should have a clear set of coding standards, even if informal, that you want the agents to enforce.
Gather the following before starting: your team's style guide or linting configuration, a list of known security patterns to watch for (SQL injection, XSS, authentication bypasses), performance budgets or SLAs if applicable, and your architectural principles or system design documents.
Mission: Receive the pull request diff, distribute it to each specialist reviewer, collect their individual reviews, resolve conflicts between reviewers, and produce a single prioritized review document.
The Coordinator is responsible for triage. If the Security Reviewer flags a line as dangerous and the Style Reviewer wants to refactor the same line for readability, the Coordinator prioritizes security. It also eliminates duplicate comments -- if two reviewers flag the same function, the Coordinator merges their feedback into one comment.
Prompt guidance: Tell the Coordinator to categorize all findings into three priority levels: "Must Fix" (security vulnerabilities, bugs, breaking changes), "Should Fix" (performance issues, maintainability concerns), and "Consider" (style preferences, minor improvements). This forces prioritization rather than presenting a flat list.
Mission: Examine the diff for security vulnerabilities, unsafe patterns, authentication and authorization issues, data exposure risks, injection vectors, and dependency concerns.
This agent reads code through a paranoid lens. It assumes every user input is malicious, every API endpoint is exposed, and every dependency is compromised until proven otherwise. It checks for hardcoded secrets, unsafe deserialization, missing input validation, improper error handling that leaks internal state, and SQL or command injection opportunities.
Prompt guidance: Provide this agent with your security checklist and any past security incidents relevant to the codebase. Instruct it to reference specific CWE (Common Weakness Enumeration) identifiers when flagging issues so developers can look up the vulnerability class. Require it to assess severity: critical, high, medium, or low.
Mission: Analyze the diff for performance regressions, inefficient algorithms, unnecessary database queries, memory leaks, blocking operations in async contexts, and missed caching opportunities.
The Performance Reviewer focuses on computational cost. It looks for N+1 query patterns, unbounded loops over user-supplied data, synchronous I/O in request handlers, missing pagination on list endpoints, large object allocations in hot paths, and regex patterns vulnerable to catastrophic backtracking.
Prompt guidance: Give this agent context about your runtime environment -- language, framework, expected request volumes, database type, and any existing performance bottlenecks. A performance concern in a batch job that runs once daily is different from the same concern in a request handler serving 10,000 RPM.
Mission: Evaluate whether the changes align with the codebase's architectural patterns, follow established conventions, maintain appropriate abstraction levels, and keep the code maintainable for future developers.
This agent thinks about the long-term health of the codebase. It checks for proper separation of concerns, consistent naming conventions, appropriate test coverage, adherence to the project's module boundaries, and whether new abstractions are justified or premature. It also flags dead code, unused imports, and inconsistent error handling patterns.
Prompt guidance: Feed this agent your project's architectural decision records (ADRs) if you have them, or a summary of your architectural principles. Include your linting configuration and any patterns you have explicitly adopted (repository pattern for data access, middleware pattern for cross-cutting concerns, etc.).
The review process follows a fan-out/fan-in pattern:
This fan-out approach is critical for speed. A sequential review where each agent waits for the previous one would take three times as long. Since the reviewers do not depend on each other's output, parallelism is safe and efficient.
The key to effective code review agents is specificity in what to look for and how to report it. Vague instructions like "review this code" produce vague output. Here is the structure for each reviewer prompt:
Scope boundaries. "You are the Security Reviewer. You focus exclusively on security concerns. Do not comment on code style, naming, performance, or architecture unless it directly creates a security vulnerability."
Detection checklist. "Check for the following: (1) SQL injection via string concatenation in queries, (2) XSS via unescaped user input in templates, (3) hardcoded API keys, tokens, or passwords, (4) missing authentication checks on endpoints, (5) overly permissive CORS configurations, (6) insecure cryptographic choices, (7) path traversal in file operations."
Output format. "For each finding, provide: file path, line number or range, severity (critical/high/medium/low), CWE identifier if applicable, description of the vulnerability, suggested fix with code example, and reasoning for the severity rating."
False positive guidance. "If a pattern looks suspicious but is safe due to context (e.g., a parameterized query that appears to use string formatting but actually uses the ORM's safe API), note it as 'Reviewed -- no issue' with a brief explanation."
For maximum value, the review team should run automatically on every pull request. Using the Claude Agent SDK, set up a webhook listener that triggers on PR events:
For teams not ready for full automation, start with on-demand reviews. A developer runs the review team manually before requesting human review. This catches the obvious issues early, letting human reviewers focus on design decisions and business logic that agents handle less well.
A completed review from this agent team looks like this:
Must Fix (2 items)
src/api/users.ts:47 -- CRITICAL (CWE-89): User-supplied sortField parameter is interpolated directly into SQL ORDER BY clause. Use a whitelist of allowed column names. Suggested fix provided.src/auth/session.ts:23 -- HIGH (CWE-798): JWT secret is hardcoded as a string literal. Move to environment variable.Should Fix (3 items)
src/services/export.ts:89 -- MEDIUM: Unbounded query fetches all records without pagination. For tables exceeding 10,000 rows, this will cause memory pressure and slow response times. Add limit/offset or cursor-based pagination.src/models/order.ts:34-56 -- MEDIUM: New Order class duplicates validation logic already present in src/validators/order.ts. Consolidate to avoid drift.src/api/reports.ts:12 -- MEDIUM: Synchronous file read in async request handler blocks the event loop. Use async file read or move to a background job.Consider (2 items)
src/utils/format.ts:15 -- LOW: Function name doFormat is generic. Consider formatCurrencyForDisplay to clarify intent.src/api/users.ts:60-72 -- LOW: This error handling block catches all exceptions uniformly. Consider distinguishing between validation errors (400) and internal errors (500).Language-specific tuning. Adjust each reviewer's checklist for your language and framework. A Python reviewer should check for eval() and pickle.loads(). A JavaScript reviewer should check for innerHTML assignments and dangerouslySetInnerHTML. Generic security checklists miss framework-specific vulnerabilities.
Incremental learning. When a human reviewer overrides an agent's finding (marking it as a false positive or upgrading its severity), log that feedback. Periodically update the agent's prompt with these corrections: "In this codebase, the sanitize() function in src/utils/security.ts is trusted -- do not flag its output as unsanitized."
Diff size limits. Large PRs produce lower-quality reviews from both humans and agents. If a PR exceeds 500 lines of changes, have the Coordinator split it into logical chunks and review each chunk separately, then merge the results. This keeps each reviewer focused and within effective context limits.
Complement, do not replace. Agent reviews catch mechanical issues -- the security patterns, performance antipatterns, and style violations that follow known rules. Human reviewers should focus on what agents cannot: whether the approach makes sense for the business problem, whether the abstraction will hold up as requirements evolve, and whether the code communicates intent clearly to the next developer who reads it.