arXiv:2606.20058v1 Announce Type: new 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 sc
The proliferation of specialized AI models and the increasing complexity of enterprise operations are driving demand for autonomous coordination at scale.
This development addresses a critical bottleneck in deploying AI across large organizations, moving from discrete tasks to continuous, adaptive intelligence.
The ability of AI systems to autonomously manage and orchestrate complex workflows in real-time within an enterprise environment is significantly enhanced.
- · Enterprise AI providers
- · Large corporations
- · AI agents developers
- · SaaS platforms
- · Legacy workflow automation
- · Human task managers
- · Companies slow to adopt advanced AI
Enterprises will see accelerated adoption and integration of multi-agent AI systems, leading to more efficient and autonomous operations.
This improved orchestration will collapse many white-collar workflows, leading to significant shifts in organizational structures and human-machine collaboration.
The enhanced scalability and continuous operation of AI will drive a deeper integration of AI into competitive strategy, increasing the gap between AI-native and legacy enterprises.
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Read at arXiv cs.AI