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

Abstract:Enterprise agent language models must coordinate specialist agents over continuous business event streams, yet most multi-agent evaluations assume discrete request-response workflows. We present a controlled simulation based on a sanitized enterprise research snapshot: 208 scenarios, 393 events, and 1,051 expected agent calls across Persona (<10 agents), Department (20-80), and Enterprise (200) registries. We compare DAG Plan & Execute with ReAct and evaluate a minimal Task Manager for priority inference, related-event merging, and preemption. Both systems perform well on the Persona cohort but score lower on the Enterprise cohort, especially for Simple scenarios; a larger, more confusable discovery space may contribute. The tested DAG Plan & Execute implementation generally maintains higher agent-call precision, whereas ReAct shows stronger recall and Expected Answer Completeness in several Enterprise conditions. The Task Manager reduces high-priority queue latency by 14-75% and improves related-event Expected Answer Completeness by over 20 percentage points at Enterprise scale. These controlled-simulation results characterize the tested systems but do not establish production readiness.

Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)

arXiv:2606.20058 [cs.AI]

(or arXiv:2606.20058v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.20058 Focus to learn more arXiv-issued DOI via DataCite

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