How does the model actually "call" a tool? Explain the back-and-forth between the Assistant message and the Tool/Function message in an API loop.

What does a production-grade observability stack for AI agents look like? What metrics, logs, and traces are essential? How would you debug a scenario where an agent produces correct outputs 95% of the time but fails unpredictably?

How do you implement "Long-Term Memory" for an agent so it remembers a user's preference from a conversation that happened three weeks ago?

How does the "Extraction, Transformation, and Loading" process differ when preparing data for a Vector Database versus a traditional SQL database?