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

arXiv: https://arxiv.org/abs/2606.20058 · HTML: https://mlsanity.com/paper/2606.20058/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 54 references that are in the map.
- First posted on arXiv 18 Jun 2026.

## Builds on (most cited first, 10 of 10)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers](https://mlsanity.com/paper/2508.20453/) (2025)
- [Multi-Agent Collaboration via Evolving Orchestration](https://mlsanity.com/paper/2505.19591/) (2025)
- [The Tool Decathlon: Benchmarking Language Agents for Diverse, Realistic, and Long-Horizon Task Execution](https://mlsanity.com/paper/2510.25726/) (2025)
- [MCPMark: A Benchmark for Stress-Testing Realistic and Comprehensive MCP Use](https://mlsanity.com/paper/2509.24002/) (2025)
- [The Orchestration of Multi-Agent Systems: Architectures, Protocols, and Enterprise Adoption](https://mlsanity.com/paper/2601.13671/) (2026)
- [From Agent-Only Social Networks to Autonomous Scientific Research: Lessons from OpenClaw and Moltbook, and the Architecture of ClawdLab and Beach.Science](https://mlsanity.com/paper/2602.19810/) (2026)
- [Defensible Design for OpenClaw: Securing Autonomous Tool-Invoking Agents](https://mlsanity.com/paper/2603.13151/) (2026)
- [Autonomous Deep Agent](https://mlsanity.com/paper/2502.07056/) (2025)

## Abstract (arXiv)

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.