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?

AI agents often rely on external tools/APIs. How would you design a system that ensures robustness when these dependencies are unreliable or slow?

When would you store a summary of a document in the vector DB but retrieve the full text for the LLM?

If your user queries are short "slang" phrases but your documents are formal legal texts, how do you ensure the embedding model can bridge that semantic gap?