What: The FastContext paper (Microsoft) trains a dedicated explorer subagent — a 4B-30B model the main coding agent calls to find code — that issues read-only searches and returns compact file-line citations instead of dumping files into the main context.
Why: Reading and searching a repository is the biggest single drain on a coding agent: in GPT-5.4 traces it ate 56.2% of tool-use turns and 46.5% of the main agent's tokens, so moving that work off the main agent is where the token budget is won.
vs prior: A normal coding agent greps and reads files itself, so every raw file lands in its own context window and crowds out the actual coding. FastContext offloads exploration to a separate subagent that returns only citations — the evidence, not the haystack.
Think of it as
A reference librarian you send into the stacks.
