Agents fail. Handle it gracefully. Implement retry with exponential backoff, circuit breakers, fallback chains, and graceful degradation — so your agent survives production chaos.

Ship changes to your agent without breaking production. Implement prompt versioning, gradual rollouts, A/B experiments, and automated evaluation pipelines with weight-based traffic splitting.

Stop paying for the same LLM call twice. Implement semantic caching, tool result caching, session-aware TTLs, and cache invalidation for AI agents — with Redis and embeddings.

Stop running agents in the dark. Implement structured observability for AI agents — trace every tool call, track token costs, detect infinite loops, and build a real-time monitoring dashboard.

Test your MCP server with automated tests, publish it to npm/GitHub, register on community directories, and explore the broader MCP ecosystem of clients, tools, and best practices.

Secure your MCP server for production. API key authentication, Express middleware, rate limiting, CORS policies, environment validation, graceful shutdown, and Docker security hardening.

Take your MCP server from localhost to remote. Full SSE transport implementation with Express.js, Docker multi-stage build, environment configuration, health checks, MCP Inspector over SSE, and connecting Claude Desktop remotely.

Add Resources and Prompts to your MCP server. Expose issue comments as readable content, create reusable prompt templates, add advanced tools with pagination and streaming, and test all three capabilities together with MCP Inspector.