Autonomous Event-Driven Multi-Agent Orchestration for Enterprise AI at Scale

2026-06-18

AI Summary

This work addresses the limitations of existing enterprise-scale multi-agent systems, which predominantly rely on discrete request-response paradigms and struggle to support large-scale, continuous event-driven monitoring and response. To overcome this, we propose an autonomous, event-driven multi-agent collaboration framework tailored for enterprise AI. Our approach presents the first systematic evaluation at enterprise scale of DAG-based Plan-and-Execute and ReAct architectures, augmented with a task manager that enables priority-aware reasoning, correlated event aggregation, and preemptive scheduling. Experimental results across Persona–Department–Enterprise tiered scenarios demonstrate that our method reduces latency for high-priority tasks by 14%–75% and improves accuracy in handling correlated events by over 20 percentage points. These findings indicate that system bottlenecks stem primarily from scale rather than intrinsic task complexity.

Abstract

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.

Problem

• multi-agent systems
• enterprise AI
• event-driven orchestration
• agent discovery
• scalability

Innovation

• event-driven orchestration
• multi-agent systems
• enterprise AI
• Task Manager
• priority inference