54 lines
2.9 KiB
Markdown
54 lines
2.9 KiB
Markdown
# How we built our multi-agent research system (Anthropic Engineering)
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## Metadata
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| Field | Value |
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| Institute | Molodetz Institute — Research & Retrieval Division |
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| Subject | cap-protocol |
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| Document type | Source Dossier |
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| Tier | 1 |
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| Source title | How we built our multi-agent research system (Anthropic Engineering) |
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| Source URL | https://www.anthropic.com/engineering/multi-agent-research-system |
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| Source publisher / author | Anthropic |
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| Source type | blog post |
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| Source date (published) | Unknown — not stated |
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| Date researched | 2026-10-25 |
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| Researched by | tai (agent), model: Claude Sonnet 5, on behalf of retoor |
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| Research method | direct page fetch |
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| Confidence | High |
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| Status | Active |
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## Summary
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Anthropic's account of an orchestrator-worker research system: a lead agent plans and spawns parallel subagents with their own context windows. Includes numbers on performance and token cost, prompting principles for delegation, and when the pattern does not fit.
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## Key findings
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- Lead Claude Opus 4 with Sonnet 4 subagents beat single-agent Opus 4 by 90.2% on the internal research eval.
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- On BrowseComp three factors explained 95% of performance variance; token usage alone explained 80%, with tool calls and model choice the others.
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- Agents use about 4x the tokens of chat; multi-agent systems about 15x. Parallelization cut research time by up to 90% on complex queries.
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- Delegation must be taught: each subagent needs an objective, an output format, tool and source guidance, and clear task boundaries; vague instructions caused duplicated work.
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- Scale effort to complexity: 1 agent with 3-10 tool calls for simple facts, 2-4 subagents with 10-15 calls for comparisons, 10+ subagents for complex research.
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- Does not fit: tasks where all agents share the same context or have many dependencies, most coding tasks (fewer parallelizable parts), weak real-time coordination, and low-value tasks that cannot pay the token cost.
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- Production: agents are stateful so errors compound (resume from checkpoints, tell agents when a tool fails); leads wait synchronously for each subagent batch; start evals with about 20 real queries; judge end state for stateful agents.
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- Upgrading to Sonnet 4 gave a larger gain than doubling the token budget on Sonnet 3.7.
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## Pros / strengths
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Primary source with concrete numbers, and an honest list of where delegation fails, including coding.
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## Cons / limitations / caveats
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A research workload, not an interactive coding agent; numbers come from Anthropic's internal eval and BrowseComp, not independently reproduced. 15x is a ballpark for their system.
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## Relevance to tai
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Direct evidence for two rules: delegation to another model costs a multiple of the tokens (so cap it and count it), and a delegation brief needs objective, format, tools and boundaries (so a delegation tool needs a structured input, not a free string).
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## Changelog
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| Date | Change |
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| 2026-10-25 | Initial dossier created. |
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