AI Agents for Code Review
This reference shows how an AI agent performs code review work, from goal formulation through tool use to final decision, with a concrete worked example.
By Suhas Bhairav · Updated
Direct Answer
AI agents for code review are goal driven software that plan steps, call static analyzers and linters, fetch diffs, and check results against style, security, and correctness rules. They retain the final decision, the rationale, and a traceable tool log for audit and handoffs.
Best for: Engineering teams, code review managers, and platform developers.
What AI Agents Can Do in Code Review
Define goal
Understand the code change request, identify constraints, select relevant quality gates, and set success criteria before initiating any tool calls.
Plan steps
Create a plan that sequences static checks, dynamic tests, and human approvals, assigns owners, and estimates required data and artifacts before execution.
Run checks
Execute analyzers, run compilation, check dependencies, and apply security and style rules, collecting results and pinpointing high-impact issues for review.
Aggregate results
Summarize findings, rank issues by risk, correlate with project policies, and generate actionable remediation actions for developers and reviewers.
Decide to stop
If all critical gates pass and no new blockers emerge, finalize the review; otherwise loop back to refine goals or escalate for human input.
Document and handoff
Produce a review report, attach artifacts, log decisions, and provide a remediation plan for the next code iteration.
How the Agent Works
Receive goal
The agent receives a code change request, project policies, and target quality gates, then confirms scope, affected modules, and required artifacts before planning.
Plan
The agent constructs a plan using available tooling, priorities issues by risk, assigns owners, and schedules checks, ensuring data paths and dependencies are defined.
Call tools
The agent runs static analysis, compiles targets, tests, and security checks, collecting structured results and file-level annotations for later synthesis.
Check results
The agent evaluates results against gates, filters false positives, and decides whether to proceed, retry, or escalate to a human reviewer.
Finish or hand off
If gates pass, the agent outputs a formal review package; otherwise it formulates remediation guidance and routes to the right owner for action.
Worked Example
Suggesting remediation for a risky code change
Illustrative scenario with sample data.
Scenario
A midsize product team integrates a new payment handler, triggering lint errors and a potential security risk in error handling. An AI agent is tasked with validating the change against coding standards and policy before approval.
Input
Goal: Approve code change with high confidence. Repo: payment-service. Branch: feature/payment-handler. Files: src/payments/handler.go, tests/handler_test.go. Policies: security, formatting, dependency updates, test coverage. Data: diffs, build logs, lints, and test results.
What the agent does
- Call tools: Run static analyzers and linters on changed files; result: three critical lint warnings and one security warning appear; agent decides to request targeted fixes.
- Call tools: Compile and run unit tests; result: two failing tests and one flaky test identified; agent marks tests with failure hotspots and suggests fixes.
- Call tools: Re-run checks after proposed fixes; result: one remaining lint warning and failing test resolved; agent prepares a summary and approves non-blocking changes.
- Call tools: Generate review artifacts; result: summary report, recommended remediation steps, and a deterministic rationale for why changes are safe; agent signals handoff to human reviewer.
Output
Review package produced: status: ready for final approval; issues: 2 warrants fix, 0 blockers; remediation: adjust error handling in src/payments/handler.go; tests: fix in handler_test.go; rationale: security and formatting gates met; artifacts: diff, build logs, lint results, test report, and remediation plan.
Human decision
A human reviewer checks that remediation steps align with security policy and ensures no regression in payment processing logic before final merge approval.
What You Need
- Tooling access to static analysis, linters, and test suites for the codebase.
- Access to the repository diffs, build logs, and test results.
- Clear project policies and gate definitions for security, formatting, and coverage.
- Structured logging and audit trail for tool outputs and decisions.
Where People Stay in Charge
- The agent may not approve changes involving payment authorization logic without a senior engineer review and sign-off.
- Critical security findings must be escalated to the security lead for manual verification prior to merge.
- Privacy or compliance sensitive changes require a qualified reviewer to validate data handling and logging practices.
FAQ
What is an AI agent for code review used for?
AI agents for code review are used to automate the verification of code changes against project policies, identify high-risk issues, and prepare actionable remediation plans. They plan steps, call tooling, and either finish with a ready-to-merge report or escalate for human review.
What tools do these agents call during review?
These agents call static analyzers, linters, type checkers, build systems, and test runners, plus artifact fetchers for diffs and logs. They also generate a structured review report and attach remediation guidance to support developers.
When should a human review be required?
A human review is required when a critical security finding remains, when policy gates cannot be deterministically satisfied, or when business risk exceeds the predefined threshold and escalation to a senior engineer is needed.
What makes a good code review agent design?
A good design includes clear goals, explicit gate definitions, deterministic tool results, traceable decisions, and a repeatable handoff process. It should log tool outputs, preserve rationale, and require human validation for safety-critical changes.