Anthropic: How we built our multi-agent research system
AI Summary
Simon Willison's link-blog commentary on Anthropic's multi-agent research system post, in which he says the piece cured his prior skepticism of multi-agent LLM systems. He pulls out the key technical points — the orchestrator/subagent architecture, the 90.2% eval improvement over single-agent Opus 4, the 15x token cost of multi-agent systems, the Memory mechanism for surviving context truncation, and the OODA research loop used by subagents — and calls it the most practical writeup he has seen on multi-agent system design.
Original excerpt
Anthropic: How we built our multi-agent research system. OK, I'm sold on multi-agent LLM systems now.
I've been pretty skeptical of these until recently: why make your life more complicated by running multiple different prompts in parallel when you can usually get something useful done with a single, carefully-crafted prompt against a frontier model? This detailed description from Anthropic about how they engineered their "Claude Research" tool has cured me of that skepticism.
Reverse engineering Claude Code had already shown me a mechanism where certain coding research tasks were passed off to a "sub-agent" using a tool call. This new article describes a more sophisticated approach.
They start…
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Simon Willison's link-blog commentary on Anthropic's multi-agent research system post, in which he says the piece cured his prior skepticism of multi-agent LLM systems. He pulls out the key technical points — the orchestrator/subagent architecture, the 90.2% eval improvement over single-agent Opus 4…
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