How I built an AI agent that generates production-ready blockchain infrastructure The Problem Setting up blockchain monitoring infrastructure typically requires: Writing RPC clients for Ethereum Building REST APIs Creating dashboards Managing configuration Writing tests I built an agentic system that uses Groq’s ultra-fast LLM to generate a complete EVM blockchain processor in Go, complete with a React dashboard. CONFIG → PLAN → GENERATE → TEST → VALIDATE → EXECUTE → FIX Each stage performs a specific task, and the agent iterates until everything works. Press enter or click to view image in full size The agent executes 7 stages in sequence: ` Stage |What it does ` The agent produces a production-ready Go binary that: Connects to Ethereum via RPC (Alchemy/Infura) Current block height Gas price (in Gwei) Pending transaction count Calculates TPS (blocks/second) Exposes REST API at :8080/api/metrics Serves CORS-enabled endpoints for the dashboard. The Dashboard Press enter or click to view image in full size The React dashboard displays real-time metrics: Auto-refreshes every 5 seconds Responsive design with Tailwind CSS CORS enabled for local development The Ethereum Virtual Machine (EVM) is the runtime environment for smart contracts on Ethereum and 50+ compatible chains. DeFi: Uniswap, Aave, Compound NFTs: OpenSea, Blur DAOs: Decentralized governance Compatibility: Runs on Polygon, BSC, Avalanche, etc. This system monitors the EVM (read-only) — it observes without executing transactions. Press enter or click to view image in full size agentic-coding-evm-processor/ Python 3.9+ Go 1.21+ Node.js 18+ Setup & Run Clone the repository Install dependencies Configure API keys Edit .env with your GROQ_API_KEY and ETH_RPC_URL Run the agent (generates Go code) python run_agent.py Start the Go backend Start the dashboard (new terminal) Testing the API Response: json { Operating Modes Press enter or click to view image in full size Why Groq? Groq's LPU (Language Processing Unit) provides: 10-100x faster inference than traditional LLMs Ultra-low latency perfect for agent loops Cost-effective for code generation Without Groq, the agent still works in mock mode (using templates). ` Port 8080 in use: lsof -i :8080 && kill -9 ` Advanced: Production Deployment For production, consider: eRPC proxy — Aggregates multiple RPC endpoints for failover Docker — Containerize Go backend and dashboard PostgreSQL — Store historical metrics Prometheus — Monitor the processor itself Building this agent taught me: The complete code is available on GitHub: https://github.com/mmmattos/agentic-coding-evm-processor Free Groq API key (takes 2 minutes) Free Alchemy API key (takes 2 minutes) 10 minutes to run through the setup