Agentic Coding: Code Reviews - THE LGTM

Agentic Coding: Code Reviews

AI-powered code review is moving from novelty to necessity. The best teams are blending automated review with human judgment — here's how to build that workflow.

Last Updated: April 5, 2026

The Shift: AI-Native Review

Traditional code review: human writes code → human reviews → human approves.

AI-native review:

• Human reviews specifications and architecture
• AI reviews implementation details
• Human validates outcomes and business logic

The long-term workflow shifts toward AI-native review where humans focus on what they do best: intent, constraints, and judgment.

What AI Code Review Catches

✅ Strengths

• Security vulnerabilities (OWASP patterns)
• Performance anti-patterns (N+1 queries, memory leaks)
• Style inconsistencies
• Dead code and unused imports
• Logic errors in obvious cases
• Missing error handling
• Test coverage gaps

❌ Limitations

• Business logic correctness
• Architectural alignment
• User experience implications
• Contextual tradeoff decisions
• Novel edge cases

Human-AI Workflow Patterns

Pattern 1: AI First Pass

Developer: opens PR
AI Agent: reviews automatically, posts comments
Human: addresses AI feedback, requests human review
Human: final approval

Benefit: Catches obvious issues before human spends time.

Pattern 2: Parallel Review

Developer: opens PR
AI Agent: reviews simultaneously with human
Human: sees AI comments alongside their own review
Both: discuss, iterate

Benefit: Human can validate or challenge AI findings.

Pattern 3: Validation Chain

Agent 1: writes code
Agent 2: reviews code, posts critique
Agent 3: addresses review comments
Human: reviews final result

Benefit: Multi-agent quality gates before human sees anything.

Tools for AI Code Review

Integrated (Built into IDEs/Agents)

• GitHub Copilot: PR review comments, issue-to-PR automation
• Augment Code: Purpose-built AI code review agent
• Cursor: Background agents review PRs
• Claude Code: Can review diffs with /review command

Standalone Review Tools

• CodeRabbit: AI-powered PR review bot
• PR-Agent: Open source AI review automation
• Amazon CodeGuru: Security and performance review
• DeepCode (Snyk): Static analysis + AI

HubSpot Sidekick Case Study

HubSpot built "Sidekick" — an AI code review agent deployed to production:

• 90% faster initial review (AI vs human)
• 80% approval rate on AI suggestions
• False positive rate: 15% (acceptable tradeoff)
• Catch rate: 70% of issues human reviewers found

Key insight: Not replacing humans, but handling the "trivial many" so humans focus on the "vital few."

Review Validation Chains

Advanced teams implement multi-stage validation:

Stage 1: Automated checks
  ├── Type checker passes
  ├── Linter clean
  ├── Tests pass
  └── Security scan clean

Stage 2: AI review
  ├── Logic review
  ├── Performance check
  ├── Security patterns
  └── Style consistency

Stage 3: Human review
  ├── Business logic
  ├── Architecture alignment
  └── UX implications

Stage 4: Integration
  ├── CI/CD pipeline
  └── Staging validation

Each stage gates the next. No human time wasted on code failing basic checks.

Review Prompts That Work

General Review

Review this PR for:
- Security vulnerabilities (OWASP Top 10)
- Performance anti-patterns
- Error handling gaps
- Logic errors
- Test coverage

Be specific: quote problematic code and suggest fixes.

Security-Focused

Security review: check for:
- SQL injection vectors
- XSS vulnerabilities
- Authentication/authorization gaps
- Secrets in code
- Input validation issues

Rate each finding: CRITICAL, HIGH, MEDIUM, LOW.

Architecture Review

Architecture review against our principles:
- Does this follow hexagonal layering?
- Are dependencies pointing in correct direction?
- Is domain logic separated from infrastructure?
- Any circular dependencies introduced?

Quality Metrics

Track AI review effectiveness:

Common Pitfalls

🚩 Alert Fatigue

Too many AI comments → developers ignore them all.
Fix: Threshold tuning, severity filtering, only blocking issues.

🚩 False Confidence

"AI approved it" → human skips review.
Fix: Clear scope, require human approval for merges.

🚩 Style Wars

AI enforces style inconsistently.
Fix: Deterministic formatters (prettier, black), not AI style opinions.

🚩 Security Theater

AI finds low-severity issues, misses critical ones.
Fix: Dedicated security scanning tools, not general AI review.

Building Trust

Adoption requires trust. Build it gradually:

1. Week 1-2: AI comments only, no blocking
2. Week 3-4: Block on critical security issues
3. Month 2: Block on test failures + security
4. Month 3+: Full integration, humans focus on design

Key: Let teams see value before adding friction.

The Future

Where AI code review is heading:

• Pre-PR review: AI reviews code as it's written (inline)
• Historical context: AI knows your codebase patterns
• Cross-PR analysis: Detects patterns across multiple changes
• Learning: AI adapts to your team's preferences
• Natural language: "Why did we do it this way?" → AI explains

Getting Started

Low-friction first steps:

1. Enable GitHub Copilot PR review (if available)
2. Add PR-Agent to one repository
3. Configure to comment only, not block
4. Review AI suggestions for 2 weeks
5. Tune thresholds based on feedback
6. Gradually increase automation

Don't aim for full automation on day one. Aim for helpful augmentation.

Further Reading

• Agentic Coding: Best Practices
• Agentic Coding: QA & Testing