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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.
Master the art of designing tools that LLMs can reliably use. Learn schema patterns, error handling, idempotency, and production tool registries.
Design production-grade AI agents with tool calling, agent loops, parallel execution, human-in-the-loop checkpoints, state persistence, and error recovery.