Why research needs more than a single agent

The motivation is the read-versus-write idea from Chapter 1. Open-ended research does not follow a predictable path. Each discovery can shift the direction of inquiry, so you cannot script a fixed pipeline in advance. A single agent also runs into two hard walls: the context window fills up, and its sequential processing makes broad searches slow.

The articles also contrast this with traditional RAG. Standard RAG fetches a fixed set of documents once, based on similarity to the query, and then answers from them. Research needs more: it needs to search repeatedly, adapt based on what it finds, and follow new leads. A multi-agent system can do this dynamic, branching exploration in a way a single static retrieval cannot.

The payoff was large. In internal evaluations, a system using Claude Opus 4 as the lead agent and Claude Sonnet 4 as subagents outperformed a single-agent Claude Opus 4 by 90.2 percent on their research evaluation. The articles are candid that this works largely because multi-agent systems spend more tokens: token usage alone explained about 80 percent of the performance variance in their browsing evaluations, with the number of tool calls and the model choice as the next biggest factors.