You are building a multi-agent research system using the Claude Agent SDK.

Option C parallelizes independent work while retaining centralized orchestration. Each precedent can be analyzed without waiting for the previous precedent, so the coordinator can divide the 12 cases into balanced subsets and invoke several document-analysis subagents concurrently. It then receives their final outputs, records which precedents completed or failed, and aggregates the results before synthesis. Anthropic’s multi-agent research architecture uses an orchestrator-worker pattern in which the lead agent creates specialized subagents that operate in parallel and return findings for consolidation. Keeping spawning decisions at the coordinator also produces a clearer execution trace for monitoring and debugging. A generic asynchronous queue, option A, adds infrastructure but does not define how results remain associated with the correct research task. Options B and D create nested or recursive delegation, making execution paths, permissions, failures, and token consumption harder to observe. Anthropic also cautions that multi-agent systems consume substantially more tokens than ordinary interactions, so unbounded recursive decomposition is inefficient. Coordinator-controlled parallel fan-out followed by deterministic aggregation provides the latency improvement without sacrificing operational visibility.

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