What is the fundamental difference between a "Chain" (hardcoded steps) and an "Agent" (model-decided steps)? When is a Chain actually better than an Agent?

Explain the pattern of searching for small, granular chunks but feeding a larger "parent" context to the LLM. Why is this more accurate?

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?

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?