Agents

Learn how the ReAct loop works, what tool use looks like under the hood, and why compound failure math is the central challenge every production agent team faces.

Learn how LangGraph models agent state as a typed graph, how conditional edges enable complex branching workflows, and how persistent checkpointing lets agents survive crashes and support human approval gates.

Learn how to structure multi-agent architectures with orchestrators and specialists, when parallel subagents save time, and how to handle failures when individual agents go wrong.

Learn why production agents fail when demos succeed, how to reduce blast radius through sandboxing and cost limits, and what reliability patterns make AI agents safe to deploy.

Learn when AI agents need human approval gates, how to implement pause-and-resume in LangGraph, and how to calibrate the approval threshold to balance safety with autonomy.

Compare LangGraph, n8n, and Temporal for building agentic workflows, understand when each tool shines, and learn the deterministic-plan pattern that makes production agents reliable.

Learn how to design agent memory across four tiers, when to persist what, how to manage context window pressure, and how to build cross-session continuity that makes agents actually useful.

Learn why standard LLM evals fail for agents, how to score agent trajectories step-by-step, and how to build an eval harness that catches regressions before they reach production.