Option A parallelizes independent precedent analysis while preserving centralized control. The coordinator can partition the 12 precedents into balanced groups, provide each worker with identical extraction and citation requirements, monitor completion or failure, and aggregate the structured results before invoking synthesis.

Anthropic’s multi-agent research architecture uses an orchestrator-worker pattern in which a lead agent coordinates specialized subagents operating in parallel. Parallel execution is valuable when tasks are substantially independent, as each precedent can be analyzed without waiting for the preceding case. Maintaining the fan-out at the coordinator also produces a clear execution trace showing each assignment, status, and returned result.

Option B introduces nested delegation and makes tool usage, permissions, failures, and costs harder for the coordinator to observe. Option C compounds those problems through recursive spawning and risks excessive agent and token consumption. Option D may be appropriate for a large distributed processing platform, but it adds infrastructure without inherently improving the coordinator’s reasoning-level observability or defining how results are associated with the case. Coordinator-managed parallel workers provide the required latency reduction with the simplest debuggable architecture.

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