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Production Agent Patterns
Multi-agent systems (supervisor / hierarchical patterns); memory architectures (short-term, long-term, semantic); streaming and observability with LangSmith; evaluation that catches real agent bugs.
Module Content
Multi-Agent Systems and Supervisor Patterns
When one agent isn't enough. Coordinator patterns for systems with specialized workers. FIND_VIDEO: search 'multi-agent system supervisor LangGraph tutorial' — recommended channel: LangChain / CrewAI / Sam Witteveen. Aim for 11 min or under.
Recap — When to Split Into Multiple Agents
Multi-agent isn't free. Know when it helps, when it hurts, and what patterns work in production.
Memory — Short-term, Long-term, Semantic
What memory means for an agent and how to give it the right kind. FIND_VIDEO: search 'agent memory short term long term LLM' — recommended channel: LangChain / Mem0 / James Briggs. Aim for 10 min or under.
Recap — Designing Agent Memory
Three memory types, three implementation patterns. Pick the right combination for your agent.
Streaming, Observability, and LangSmith
The production observability layer. Without it, debugging multi-step agents is brutal. FIND_VIDEO: search 'LangSmith LangGraph observability tutorial' — recommended channel: LangChain. Aim for 10 min or under.
Recap — Debugging and Monitoring Agents in Production
LangSmith + streaming + structured logging. The three pieces that make agents debuggable at scale.
Evaluation and Testing for Agents
Evals for agents are harder than for simple LLM calls. Here's what actually works. FIND_VIDEO: search 'LLM agent evaluation testing tutorial' — recommended channel: LangChain / DeepLearning.AI. Aim for 10 min or under.
Recap — Agent Eval Beyond Simple Metrics
Three levels of agent eval: trajectory, final-answer, and component. Use all three for production-grade testing.