Short course · intermediate

Building Production Agents with LangGraph

Take a working agent and make it hold up in production: clean state, explicit graphs, reliable tools, recoverable failures, and the observability and hygiene that keep it maintainable.

0 of 9 read · 9 parts · ~60 min

1. 1 What is an AI agent? An agent is a language model that works in a loop, with tools and a goal, deciding each next step from what it sees. 4 min
2. 2 Tools and graphs A tool is a function the agent can call to do something. A graph is the structure that decides when each step runs. 5 min
3. 3 State fundamentals State is what the agent knows. Logic is what it does. Keeping them apart makes every step testable, every run replayable, and every bug easy to find. 5 min
4. 4 Agent architecture Five structure decisions, made before you write logic, that keep every step testable, every failure easy to trace, and every run replayable. 7 min
5. 5 Memory and state management How agents remember things across steps and sessions, and the five quiet ways memory breaks without the right structure. 7 min
6. 6 Self-improvement patterns How an agent learns from its own failures: score runs with a separate judge, store what broke, gate changes behind a human, and check a change helps before keeping it. 7 min
7. 7 Reliability and safety How to make tool failures recoverable, loops bounded, model output safe to act on, and risky actions reviewed before they run. 7 min
8. 8 Observability and tracing How to make every run searchable, every slow step visible, and every regression caught before it ships. 7 min
9. 9 Code hygiene The daily habits that keep an agent codebase readable months from now: type annotations, versioned prompts, caching, one file per role, and smoke tests. 6 min
10. 10 Course quiz Ten questions covering agent architecture, state management, self-improvement, reliability, observability, and code hygiene. 5 min