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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.

Master agent evaluation: trajectory analysis, tool accuracy, task completion rates, efficiency scoring, and LLM-as-judge evaluation frameworks.

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.