解决企业级AI多智能体持续调度难题，提升高并发场景下的响应效率
Autonomous Event-Driven Multi-Agent Orchestration for Enterprise AI at Scale
- 设计任务管理器实现优先级推理与事件合并，支持持续运行
- 在200个智能体规模下，高优先级任务延迟降低14%-75%
- 复杂任务受规模影响较小，简单任务性能下降更明显
企业AI正朝着跨专业智能体的持续事件监控、检测与响应演进，但现有多智能体系统大多基于离散请求-响应流程，在企业级规模下仍缺乏探索。本文在涵盖208个源自生产环境的企业场景（包括个人级<10智能体、部门级20-80、企业级200）中评估了DAG Plan and Execute与ReAct两种架构。引入任务管理器以实现优先级推断、相关事件合并和抢占机制，支持持续运行。结果表明：规模是影响编排性能的主因，而非任务复杂度；小规模下两者表现良好，但在企业级规模下，由于智能体发现噪声成为主要瓶颈，性能显著下降，其中简单任务退化更严重。DAG Plan and Execute在小规模具更高精度和结构化并行能力，但开销大，放大至企业级后恶化；ReAct通过增量式容错更具鲁棒性。任务管理器使高优先级队列延迟降低14%-75%，相关事件正确率提升超20个百分点。
原文摘要 · Abstract (English)
Enterprise AI aims to move toward continuous event monitoring, detection, and action across specialist agents, yet existing multi-agent systems largely assume discrete request-response workflows and remain underexplored at enterprise scale. We evaluate DAG Plan and Execute and ReAct across 208 production-derived enterprise scenarios spanning Persona (<10 agents), Department (20-80), and Enterprise (200) scales, and introduce a Task Manager for continuous operation via priority inference, related-event merging, and preemption. Results show that scale, not task complexity, dominates orchestration performance: both architectures perform well at small scale but degrade at enterprise scale as agent discovery noise becomes the primary bottleneck, with simple tasks degrading more sharply than complex ones. DAG Plan and Execute offers higher precision and structured parallelization at smaller scales, but its higher overhead worsens at enterprise scale; ReAct is more robust by handling failures incrementally. The Task Manager reduces high-priority queue latency by 14-75% and improves related-event correctness by over 20 percentage points at enterprise scale.
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