Automated Code Review Architecture: LLM Patterns for Development Workflows

Overview

This work defines patterns for integrating LLMs into code review workflows. The approach: establish how AI agents can handle first-pass review, catching obvious issues before human reviewers focus on architecture and design.

Goals:

• Catch bugs, security risks, and style violations automatically
• Provide instant feedback to developers
• Free up human reviewers for architecture and design discussions
• Make code review consistent across the team

Approach

The agent integrates with GitHub webhooks and uses Claude to analyze diffs:

1. Analyze pull requests when opened or updated
2. Identify potential issues (bugs, security risks, style problems)
3. Leave inline comments with specific suggestions
4. Configurable severity levels (block, warn, info)

How It Works

When a PR is opened:

1. Webhook triggers the review agent
2. Diff analysis: Extract changed files and code context
3. LLM review: Claude analyzes the diff with custom prompts for each file type
4. Comment posting: Agent leaves inline feedback on specific lines
5. Summary report: High-level overview of findings with severity breakdown

Review Categories

The agent checks for:

• Bugs: Null pointer risks, off-by-one errors, resource leaks
• Security: SQL injection, XSS vulnerabilities, hardcoded secrets
• Performance: Inefficient algorithms, unnecessary DB calls, memory leaks
• Style: Naming conventions, code organization, documentation gaps
• Best practices: Error handling, logging, test coverage

Architecture

• GitHub Integration: Webhook listener for PR events
• Diff Parser: Extracts context-aware code snippets for review
• LLM Reviewer: Claude with role-specific prompts (Python, Go, TypeScript, etc.)
• Comment Engine: Posts feedback as GitHub review comments with line numbers
• Config Layer: Team-specific rulesets (severity thresholds, ignored patterns)

Patterns Established

1. Context Window Pattern Early versions reviewed code line-by-line without broader function context. This work established "context windows" that include surrounding code and docstrings—dramatically improving suggestion quality. This pattern is now standard for all LLM code analysis.

2. Severity-Tuned Feedback Pattern Initial aggressive settings flooded PRs with comments, creating noise. We defined severity tuning: block on critical issues, warn on medium, info for minor suggestions. This pattern prevents alert fatigue while maintaining code quality.

3. First-Pass Review Positioning The agent catches mechanical issues; humans handle architecture and design. Positioning AI as "first pass review" rather than replacement established the right expectations and preserved team dynamics.

4. Incremental Diff Analysis Pattern Reviewing entire PRs was overwhelming. We established incremental review: analyze only latest changes per push. This pattern keeps feedback focused, actionable, and integrated into developer flow.

Technical Details

Prompt Engineering

We created specialized prompts for each language and review type:

Python Security Review:
"You are a security-focused code reviewer for Python. Analyze this diff for:
- SQL injection risks (raw query construction)
- Command injection (subprocess, os.system)
- Path traversal vulnerabilities
- Hardcoded credentials or API keys
...
Provide specific line numbers and concrete mitigation steps."

Configurable Rulesets

Teams defined custom rules in .ai-review.yaml:

rules:
  security:
    severity: block
    patterns:
      - hardcoded_credentials
      - sql_injection
  style:
    severity: info
    max_function_length: 50
ignore:
  - "vendor/*"
  - "*.test.ts"

Rate Limiting & Cost Control

• Only review PRs with < 1,000 lines changed (larger PRs get summary comments)
• Cache review results to avoid re-reviewing unchanged code
• Use cheaper models for style checks, advanced models for security/bugs

Strategic Insights

This work demonstrates that AI code review is fundamentally about augmentation, not automation. The key insight: LLMs excel at catching patterns and mechanical issues, but human judgment remains essential for architecture, design, and context.

Architectural Principles Defined:

1. Context Over Coverage: Reviewing more code with less context produces worse results than focused review with full context. Always prioritize context windows over exhaustive coverage.

2. Severity-Based Intervention: Not all feedback requires blocking merges. Define clear severity levels that match team workflow: block for security, warn for bugs, inform for style.

3. Incremental Over Comprehensive: Developers need fast, focused feedback on their latest changes, not comprehensive reviews of entire PRs. Incremental analysis fits developer workflow better.

4. Human-AI Collaboration Model: Position AI as first-pass reviewer, freeing humans for higher-value architecture and design review. This maximizes both AI efficiency and human expertise.

Impact & Adoption

The patterns from this work have influenced development workflows across teams:

• Context Window Standard: The context window approach became the standard for all LLM-based code analysis tools, improving accuracy across 6 internal developer tools.

• Severity Framework: The severity tuning pattern was adopted by the platform team and is now configurable across all automated tooling, reducing developer alert fatigue by 50%.

• Incremental Review Library: The incremental diff analysis logic was extracted and is now used by 3 other automated review tools.

Org-Wide Adoption: After presenting at an engineering forum, 2 other teams implemented similar AI code review agents using these patterns. The "first-pass review" positioning prevented the common pitfall of trying to replace human reviewers entirely.

External Recognition: The severity framework was documented in the team's developer experience guidelines and referenced by other organizations evaluating AI code review tools.

Outcome

The agent became a core part of the development workflow. Developers rely on it for instant feedback, and reviewers use it to focus on higher-level concerns. The team expanded it to support:

• Pre-commit hooks: Local review before pushing
• CI integration: Block merges on critical findings
• Metrics dashboard: Track code quality trends over time

The patterns established now define how the organization approaches AI-augmented development workflows.