Building LLM Agents – Challenges and Learnings
summary
Shows an event‑planning agent that augments an LLM with concrete tools (weather API, RAG memory, tool‑calling that emits agentic JSON calls), and a data‑science agent that spins up code, installs pandas automatically and trains a regression model on a Windows box with no sandbox Windows run. Demonstrations include a measured benchmark: agent solves the task in five minutes vs a human in 27 minutes, plus frank failures (hallucinated tool calls), debugging and evaluation lessons for production.
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chapters (16)
- 0:00LLM agents introduction
- 3:20What defines an LLM agent
- 4:24Tool calling in agents
- 10:53Agent architectures with tools
- 13:01Memory planning and reflection
- 15:50Multi-agent interaction
- 18:42Real-world agent apps
- 25:20Coding agents limitations
- 26:23Agentic search engines
- 27:27Personal agents in practice
- 28:10Deep research agents
- 29:12Three hard lessons learned
- 32:39Demo to production
- 35:07Q&A: Correct JSON output
- 36:34Q&A: Handling classified data
- 38:22Q&A: Agents replace developers
