How to Test LLM Agents
summary
Claims concrete wins: enterprise migration of 50+ apps to AWS, a generative DVI platform (Complhawk) that cuts manual analysis by 99.98% and saves ~$25M annually. Covers testing methods: RAG with vector database chunking, MCP servers for orchestration, the three‑step routing→retrieve→act workflow, and limits on agent count to avoid “infinity‑squared” failures. Gives metrics and tools: ROUGE and BLEU, recall/precision tradeoffs, contrastive verification, LLM‑as‑judge, few‑shot/fine‑tune and reinforcement loops, plus logging points to validate handoffs.
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chapters (9)
- 0:00Testing LLM agents
- 2:14Trust and autonomy in agents
- 10:45RAG and agent workflows
- 18:15Hallucinations overview
- 27:49Detecting hallucinations
- 35:09Preventing hallucinations
- 40:44Prompt optimization
- 43:16Agent orchestration & AI safety
- 47:30Q&A
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