Anthropic builds Claude's multi-agent Research feature: orchestrator-worker architecture outperforms single-agent by 90.2%
Research tasks are open-ended and path-dependent, making linear pipelines inadequate; information relevant to complex queries also exceeds single context windows.
Early agents made coordination errors, including spawning excessive subagents for simple queries, searching endlessly for nonexistent sources, and consistently preferring SEO-optimized content farms over authoritative sources.
The multi-agent system with Claude Opus 4 as lead and Claude Sonnet 4 subagents outperformed single-agent Claude Opus 4 by 90.2% on the internal research evaluation, and users report saving up to days of work.
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Frequently asked questions
What did this team achieve with this AI workflow?
The multi-agent system with Claude Opus 4 as lead and Claude Sonnet 4 subagents outperformed single-agent Claude Opus 4 by 90.2% on the internal research evaluation, and users report saving up to days of work.
What tools did this team use?
Claude Opus 4, Claude Sonnet 4, Google Workspace.
What results were reported?
Multi-agent vs single-agent performance on internal research eval: 90.2%; BrowseComp performance variance explained by token usage: 80%; BrowseComp performance variance explained by three factors combined: 95%; Agent token usage relative to chat: about 4× (source-reported, not independently verified).
What failed first in this deployment?
Early agents made coordination errors, including spawning excessive subagents for simple queries, searching endlessly for nonexistent sources, and consistently preferring SEO-optimized content farms over authoritative…
How is this back office ops AI workflow structured?
User query submission → Lead agent strategy planning → Parallel subagent spawning → Subagent information gathering → Final answer compilation.