Code Intelligence

AI-powered code analysis using Google ADK's multi-agent system on Cloud Run - detect security issues, optimize performance, improve code quality instantly.

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Inspiration

Code reviews are time-consuming and often miss critical issues. We wanted to build an AI system that thinks like a team of expert reviewers - one focused on security, one on performance, one on code quality - all working together to provide comprehensive analysis in seconds.

What it does

Code Intelligence Platform uses 4 specialized AI agents powered by Google ADK and Gemini 2.0 Flash to analyze code:

  • Security Agent detects SQL injection, XSS, hardcoded secrets
  • Performance Agent identifies O(n²) complexity, bottlenecks, optimization opportunities
  • Parser Agent analyzes code structure, metrics, and patterns
  • Refactoring Agent suggests best practices and design patterns

All agents run sequentially on Cloud Run, sharing context to provide comprehensive analysis.

How we built it

Frontend: React + TypeScript + Ant Design, deployed on Cloud Run Backend: FastAPI with multi-agent orchestration using Google ADK patterns, Gemini 2.0 Flash for AI reasoning, deployed on Cloud Run (8 vCPU, 8GB RAM) Multi-Agent System: 4 specialized agents coordinated sequentially, each building on previous results Google Cloud: Cloud Run (frontend + backend), Firestore (storage), Secret Manager (API keys), Cloud Build (CI/CD)

Challenges we ran into

  1. Agent Coordination - Ensuring agents execute in correct sequence and share context effectively
  2. Cloud Run Timeout - Gemini API calls can be slow; implemented timeout handling and progress indicators
  3. Real-time Updates - Showing agent progress while maintaining REST API compatibility
  4. Performance Optimization - Balancing response time with analysis depth

Accomplishments that we're proud of

  • True multi-agent system with 4 specialized agents working together
  • Production-ready deployment on Cloud Run with auto-scaling
  • Comprehensive analysis covering security, performance, and code quality
  • Enterprise-grade UI with real-time agent workflow visualization
  • Clean architecture

What we learned

  • How to design multi-agent systems using Google ADK patterns
  • Cloud Run best practices for serverless AI applications
  • Effective prompt engineering for specialized agent tasks
  • Agent coordination strategies (sequential vs parallel execution)
  • Serverless scalability and auto-scaling on Cloud Run

What's next for Code Intelligence

  • Support for more languages (JavaScript, Java, Go)
  • Parallel agent execution for faster analysis
  • VS Code extension for IDE integration
  • CI/CD integration for automated code review
  • Agent marketplace for custom specialized agents

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