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

How would you design an orchestration layer for a system with multiple specialized AI agents (e.g., planner, retriever, executor) where partial failures are common?

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

Instead of the agent deciding one step at a time, why might you ask it to generate a full "Task List" first?