# How we built our multi-agent research system (Anthropic Engineering)

## Metadata

| Field | Value |
|---|---|
| Institute | Molodetz Institute — Research & Retrieval Division |
| Subject | cap-protocol |
| Document type | Source Dossier |
| Tier | 1 |
| Source title | How we built our multi-agent research system (Anthropic Engineering) |
| Source URL | https://www.anthropic.com/engineering/multi-agent-research-system |
| Source publisher / author | Anthropic |
| Source type | blog post |
| Source date (published) | Unknown — not stated |
| Date researched | 2026-10-25 |
| Researched by | tai (agent), model: Claude Sonnet 5, on behalf of retoor |
| Research method | direct page fetch |
| Confidence | High |
| Status | Active |

## Summary

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.

## Key findings

- Lead Claude Opus 4 with Sonnet 4 subagents beat single-agent Opus 4 by 90.2% on the internal research eval.
- On BrowseComp three factors explained 95% of performance variance; token usage alone explained 80%, with tool calls and model choice the others.
- Agents use about 4x the tokens of chat; multi-agent systems about 15x. Parallelization cut research time by up to 90% on complex queries.
- 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.
- 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.
- 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.
- 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.
- Upgrading to Sonnet 4 gave a larger gain than doubling the token budget on Sonnet 3.7.

## Pros / strengths

Primary source with concrete numbers, and an honest list of where delegation fails, including coding.

## Cons / limitations / caveats

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.

## Relevance to tai

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).

## Changelog

| Date | Change |
|---|---|
| 2026-10-25 | Initial dossier created. |