How to Build AI Agents and Keep Your Sanity
summary
Claims a practical framework that raises agent reliability while containing cost: reflection validation (Pydantic or OpenAI structured outputs and LM-as-judge), adaptive information retrieval (run multiple retrieval strategies and tabularize extracted facts), an execution engine that compiles plans into runnables, and bandits to optimize action selection under budget. Reports 50% accuracy on a finance comparison baseline, 95% of pilots fail, and uses AI21’s Maestro and techniques like text-to-SQL, RL bandits and GPT4O in experiments.
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chapters (8)
- 0:00Introduction to AI agents
- 3:28Why AI agents are hard
- 7:46Four components for sanity
- 12:47Reflection mechanism
- 13:10Adaptive information retrieval
- 18:33Execution engine
- 22:04Bandits for optimization
- 26:06Demo of Maestro system
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