Engineering and Technology
| Open Access |
DOI: Agent-to-Agent Collaboration Models for Complex Business Workflows Coordination Strategies, Task Decomposition, and Conflict Resolution
Sandeep Nutakki , Independent Researcher, Seattle, Washington, USAAbstract
As autonomous AI agents become more capable, complex enterprise tasks increasingly require coordination among multiple specialized agents rather than reliance on a single generalist. This paper presents a comprehensive framework for multi-agent collaboration in business workflows, addressing three fundamental challenges: coordination architecture design, task decomposition strategies, and conflict resolution mechanisms. We introduce and evaluate four collaboration patterns—hierarchical delegation, peer-to-peer negotiation, blackboard-based coordination, and market-based allocation—across diverse enterprise scenarios including document analysis, research synthesis, and process automation. Our experiments demonstrate that multi-agent collaboration achieves 34% higher task completion rates compared to single-agent baselines on complex tasks, while introducing a coordination overhead of 12-18% of total execution time. We identify optimal collaboration patterns for different task characteristics and provide guidelines for practitioners designing multi-agent enterprise systems.
Keywords
Multi-Agent Systems, Agent Collaboration, Task Decomposition, Conflict Resolution, Large Language Models, Enterprise AI, Coordination Mechanisms
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