Build production AI agents in 2026. Learn ReAct agents, multi-agent systems with AutoGen, stateful agents with LangGraph, tool calling, memory, and real-world agent patterns that work at scale.
Deep dive into core agent patterns: ReAct loops, Plan-Execute-Observe, reflection mechanisms, and preventing infinite loops with real TypeScript implementations.
Master error detection, reflection prompting, alternative tool selection, human-in-the-loop escalation, and graceful degradation for production agents.
Build memory systems for AI agents with in-context history, vector stores for semantic search, episodic memories of past interactions, and fact-based semantic knowledge.
Secure AI agents against prompt injection, indirect attacks via tool results, unauthorized tool use, and data exfiltration with sandboxing and audit logs.
Master tool schema design, description engineering, error handling, idempotency, and tool versioning to build AI agent tools that agents actually want to use.