The Dynamic Task Orchestration Framework for Heterogeneous Multi-Agent Systems

Abstract

The proliferation of large language model (LLM) based AI agents has created an urgent need for robust orchestration mechanisms that can coordinate heterogeneous agents in complex, real-world environments. Existing approaches to multi-agent task allocation rely predominantly on centralized controllers, which introduce single points of failure, scalability bottlenecks, and rigid coupling between the orchestrator and the agents it manages. This paper introduces the Dynamic Task Orchestration (DTO) framework, a decentralized, capability-aware architecture for assigning tasks to AI agents in real time. The DTO framework models each agent as an autonomous economic actor that participates in a sealed-bid auction mechanism to compete for incoming tasks. Task allocation decisions are driven by three primary factors: the agent's declared capability profile, its current computational and cognitive load, and the estimated complexity of the task. The framework defines a formal task decomposition grammar, a standardized agent capability ontology, and a set of protocol-level contracts that govern bidding, delegation, execution, and result aggregation. We present the theoretical foundations of the framework, provide detailed implementation guidance, and propose a comprehensive evaluation methodology grounded in metrics for throughput, latency, fault tolerance, and resource utilization. Through analytical evaluation and scenario-based discussion, we demonstrate that the DTO framework achieves superior load balancing, resilience to agent failure, and adaptability to changing workloads compared to centralized orchestration baselines. The framework is entirely tool-agnostic and vendor-neutral, designed so that any organization can adopt it to build more robust, efficient, and scalable multi-agent systems.