As Artificial Intelligence (AI) agents enter their next stage of ubiquitous deployment throughout the Internet, their discoverability will become a central challenge. The information AI agents need to discover one another, how they will locate it, how to facilitate authentication, integrity, and authorization, how to connect across different platforms, and how to scale across organizational boundaries form a set of unanswered challenges that will be central to deployment success. These are challenges for which one of the Internet's most venerable, solid, and ubiquitous infrastructures is ideally suited: The Domain Name System (DNS). Such a rich, already ubiquitous, and programmatically flexible foundation is an ideal option for discovery of AI agents. In this work, we propose an illustration and rationale for the basic semantics that discovery for AI agents will require. We argue that three key evaluation criteria will become paramount: navigational completeness (the extent to which the necessary metadata, with elements like trust, is included in a discovery solution), lookup complexity, and transaction performance (e.g., latency, speed, or recency). Using 119,757 real-world service endpoints, 62,739 indexed MCP server entries, and multiple agent tooling ecosystems, we empirically evaluate the first of these considerations to illustrate the appropriateness of using DNS for AI agent discovery. We find that the necessary and sufficient discovery metadata can be encoded into a single DNS resource record that fits within one DNS UDP response, enabling single-RTT discovery. Our evaluations illustrate a promising path toward enabling AI agent discoverability at the Internet's scale, and thereby accelerating secure, stable, and resilient deployments.

翻译：暂无翻译