This Specialization teaches learners to build production-ready AI agents using TypeScript and the Model Context Protocol (MCP), focusing on the agentic patterns that make agents reliable, efficient, and autonomous. Learners master designing tool servers that connect agents to real-world systems, implementing the universal agent loop, and applying critical patterns like Response-as-Instruction (treating LLM outputs as executable directives), Failing Forward (using errors as learning signals rather than stop conditions), and Intelligence Budget (optimizing token spend across reasoning steps). Graduates can ship AI agents that discover context dynamically, recover from errors automatically, and operate effectively in production environments.
Applied Learning Project
Learners build complete MCP tool servers and AI agents from scratch, implementing real-world patterns like context-aware workspace managers, error recovery systems, and intelligence-optimized architectures. Each project tackles authentic challenges—teaching agents to discover context on-demand, recover from failures automatically, and make efficient use of limited cognitive resources—the same problems that cause production AI agents to fail.
