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.
What you'll learn
design tool servers for AI agents
implement the universal agent loop
apply Response-as-Instruction
utilize Failing Forward strategies
manage Intelligence Budget for token optimization
Course objectives
build production-ready AI agents
connect agents to real-world systems
foster agentic patterns for reliability and autonomy