Key Takeaways

• •Agents work with a limited context window, and information retrieval breaks in three ways: inefficient searching, too much context at once, and irrelevant or stale context.
• •The Deep Agents harness keeps searches small out of the box. The built-in filesystem tools search before they read: glob matches file names, grep finds matching lines, and read_file reads a page at a time. Oversized tool results are offloaded: the full result is saved to files, but the agent gets just the path and a preview.
• •For custom search tools (like RAG), use "retrieve, offload, delegate": save results to files, return the paths, and let subagents read them. The main agent only sees what's relevant.

Imagine you’re taking an open book exam: you technically have all the answers, but somehow that makes it even harder to find the right one.

• •Where do you even start searching?
• •When do you know that your sources are correct?
• •How do you optimize your time so that you don’t blow all your energy on redundant searches?