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

If an agent task takes 2 minutes to complete, how do you architect the API so the user's browser connection doesn't time out?

You can't use a standard debugger on an LLM's "thought process." How do you build observability into an agentic loop to find where a logic error occurred?

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?