Agent Discovery

Agent discovery refers to the process of finding and inventorying AI agents that are running inside an organization, including ones that employees or teams deployed without central IT approval. Autonomous agents can call tools, access data, and act on behalf of users, so an organization cannot govern what it does not know exists. For compliance and risk teams, agent discovery is the first step toward assigning owners, reviewing data access, and applying the same oversight used for other software.

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ResearchGlobal2026-11-12

HCLTech-MIT Research: Agent Discovery Infrastructure Exposes Systemic Governance Gap

HCLTech and MIT Media Lab published joint research examining NandaIndex, an open agent-discovery protocol. It is designed to help AI agents locate and interact with one another across organizational and platform boundaries. The research argues that AI safety controls focused on individual agents are insufficient once agents coordinate at ecosystem scale. It calls for systemic monitoring, incident-learning mechanisms, and cross-ecosystem accountability infrastructure.

HCLTech multi-agent systems agentic AI systemic risk agent discovery cross-organizational governance MIT Media Lab