Agent-to-Agent Collaboration Models for Complex Business Workflows Coordination Strategies, Task Decomposition, and Conflict Resolution

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.