NANDA Adaptive Resolver: Architecture for Dynamic Resolution of AI Agent Names

2025-08-05

AI Summary

In distributed heterogeneous AI environments, static endpoint resolution impairs agent communication flexibility and contextual adaptability. Method: This paper proposes a context-aware dynamic endpoint resolution mechanism, featuring Agent Fact Card registration and a federated agent directory index; a context-driven resolution algorithm integrating trust negotiation, QoS evaluation, and resource constraints; and a lightweight security negotiation protocol. Contribution/Results: The approach departs from traditional DNS-style static mapping by enabling runtime selection of optimal communication endpoints based on dynamic contextual factors—including physical/logical location, system load, trustworthiness, and policy compliance. Experimental evaluation demonstrates significant improvements in elasticity, security, and interoperability for cross-platform AI agent interactions, enabling scalable, adaptive coordination across large-scale heterogeneous AI systems.

Technology Category

• Multiagent Systems: Agent Communication
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

Abstract

AdaptiveResolver is a dynamic microservice architecture designed to address the limitations of static endpoint resolution for AI agent communication in distributed, heterogeneous environments. Unlike traditional DNS or static URLs, AdaptiveResolver enables context-aware, real-time selection of communication endpoints based on factors such as geographic location, system load, agent capabilities, and security threats. Agents advertise their Agent Name and context requirements through Agent Fact cards in an Agent Registry/Index. A requesting Agent discovers a Target Agent using the registry. The Requester Agent can then resolve the Target Agent Name to obtain a tailored communication channel to the agent based on actual environmental context between the agents. The architecture supports negotiation of trust, quality of service, and resource constraints, facilitating flexible, secure, and scalable agent-to-agent interactions that go beyond the classic client-server model. AdaptiveResolver provides a foundation for robust, future-proof agent communication that can evolve with increasing ecosystem complexity.

Problem

• Dynamic resolution of AI agent names in distributed environments
• Context-aware endpoint selection for agent communication
• Support for trust negotiation and QoS in agent interactions

Innovation

• Dynamic microservice for AI agent communication
• Context-aware real-time endpoint selection
• Supports trust negotiation and QoS