DAN: A Dynamic, Distributed Agent Network for Scalable and Adaptive Orchestration

This work introduces DAN (Dynamic Agent Network), a distributed architecture for orchestrating adaptive multi-agent AI systems at scale. DAN separates global coordination from local learning by combining a lightweight stateless global router with specialized local planners that maintain their own episodic case banks and adapt using case-based Q-learning. Each planner retrieves relevant prior experiences from non-parametric memory and adapts solutions to new tasks through learned retrieval utilities. This design enables specialization across nodes while preserving system scalability and fault isolation. The architecture supports horizontal scaling by allowing independent planners to operate across distributed server pods while remaining coordinated through a shared routing layer. The system is designed for long-horizon, tool-driven tasks such as those found in complex reasoning benchmarks and enterprise automation environments. Tasks pass through a pipeline consisting of global routing, local planning, case retrieval, and tool execution, allowing planners to incrementally learn from interaction histories while maintaining lightweight memory stores. The paper introduces a formal model for distributed planners with learned retrieval policies and describes a Boltzmann-based case selection mechanism together with reinforcement learning updates for retrieval utilities. A bandit-based routing strategy enables the global orchestrator to dynamically select planners based on empirical utility metrics such as success rate, latency, and cost. Beyond the core orchestration model, DAN also proposes a protocol bridging mechanism that enables interoperability between Agent-to-Agent (A2A) communication and Model Context Protocol (MCP) systems through a stateless protocol adapter layer. This allows heterogeneous AI systems and tools to interoperate without requiring changes to existing agents or tool APIs. We analyze the scalability properties of the architecture and outline evaluation plans using benchmarks such as GAIA, DeepResearcher, and SimpleQA. The design is optimized for environments where specialized planners running small language models at the edge collaborate within a distributed reasoning network. DAN demonstrates how distributed agent architectures can enable specialization, resilience, and efficient resource utilization compared with centralized orchestration approaches. The framework provides an architectural foundation for large-scale collaborative AI ecosystems in enterprise, research, and hybrid edge-cloud environments.