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.

Agents generate a lot of "internal thought" and tool logs. How do you keep the context window from filling up with irrelevant logs during a long multi-step task?

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?