Task Orchestration in Hybrid Workflows: A Multi-Agent Approach for Seamless Human-AI Role Transition

Abstract

This paper addresses the challenge of dynamic task allocation in human-AI collaborative workflows, where rigid role assignments often lead to systemic inefficiencies. We present a multi-agent orchestration framework that enables real-time role transitions between human and AI agents based on continuous competency assessments. The system employs distributed Q-learning to evaluate agent capabilities across three dimensions: task proficiency (accuracy and speed), contextual awareness (environmental adaptability), and collaborative readiness (communication latency). Experimental evaluations across healthcare triage and manufacturing quality control scenarios demonstrate that the framework reduces workflow bottlenecks by 32% compared to static allocation baselines, while maintaining 94%+ task completion accuracy. The architecture particularly improves performance in edge cases—situations requiring exception handling saw a 28% reduction in resolution time through dynamic reassignment. These results suggest that adaptive role transition mechanisms may enhance the flexibility of hybrid human-AI teams, though the benefits appear contingent on accurate real-time competency monitoring. The study contributes: (1) a formally verified task allocation protocol with bounded decision latency, (2) an open-source implementation of the orchestration middleware, and (3) empirical evidence from domain experts highlighting the importance of interpretable role transition logic. The framework’s performance characteristics suggest it may be particularly suitable for applications requiring rapid adaptation to evolving task demands, such as emergency response or logistics coordination.