back_office_ops · saas · workflow
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.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User query submission
A user submits a query that initiates the multi-agent research process.
Tools used
Claude Opus 4Claude Sonnet 4
Outcome
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 failed first
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.
Results
Time savedup to days of work
Volume90.2%
Grounding & classification
Source type: technical build writeup
27 fields verified against source quotes.
agentic workflowenterprise searchmulti agent workflowknowledge basefailure mode describedhuman review describedmetric backednamed customerproduction runtime claimedsource backedtools describedvendor confirmedworkflow describedsoftwareaccuracy improvementthroughput increasetime savedtechnical build writeupback office opsagentic task execution