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?

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.

How do you evaluate an agent when the "correct path" might involve 5 different tool calls in any order?

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?