What is the difference between an AI agent registry and a model registry?

Why This Matters for Security Teams

An ai agent registry is not just a catalogue. It is the control plane for autonomous software that can decide, call tools, and change state across systems. A model registry, by contrast, is usually an inventory of static artifacts. Confusing the two leads to blind spots in ownership, approval, and revocation. That gap matters because agent behavior is dynamic, while model metadata is mostly historical and versioned.

Security teams often discover the difference only after an agent has already been granted access to sensitive workflows. NHIMG’s research on LLMjacking: How Attackers Hijack AI Using Compromised NHIs shows how quickly exposed credentials can be abused, which is exactly why registry design has to account for live identity, not just build-time lineage. The right reference point is the operational risk of active agents, not the storage of model files. Current guidance from the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework treats runtime governance as a distinct concern, which is the core reason the registries should not be merged conceptually.

In practice, many security teams encounter agent misuse only after a tool chain has already been chained together and a sensitive action has been completed.

How It Works in Practice

A model registry typically stores model name, version, training lineage, evaluation results, and deployment status. It answers questions such as what was trained, when it was approved, and where it is running. An AI agent registry needs a different schema because the object being governed is alive. It should track the agent’s current identity, owner, declared purpose, connected tools, policy scope, heartbeat, recent actions, and kill-switch status.

That distinction becomes operational when an agent is allowed to browse, call APIs, trigger workflows, or delegate to other agents. In a mature design, the registry supports discovery and runtime control, not just documentation. It should also align with workload identity practices, because the registry is most useful when it binds an agent to cryptographic proof of what it is, not merely what it was built from. The CSA MAESTRO agentic AI threat modeling framework and the MITRE ATLAS adversarial AI threat matrix both support the idea that runtime context and adversarial behavior matter more than static inventory alone.

• Use a model registry for training artifacts, approvals, and reproducibility.
• Use an agent registry for live autonomy, delegated permissions, and tool access.
• Map each agent to an owner, policy boundary, and revocation path.
• Record heartbeat and last-seen activity so dormant agents are not mistaken for active ones.
• Tie registry events to secrets rotation and runtime policy checks when the agent changes state.

NHIMG’s OWASP NHI Top 10 highlights why this matters: agents create a governance problem that is closer to a privileged workload than a passive model artifact. These controls tend to break down when autonomous agents are allowed to self-orchestrate across loosely governed SaaS and internal APIs because the registry cannot keep pace with tool chaining and state changes.

Common Variations and Edge Cases

Tighter registry controls often increase operational overhead, requiring organisations to balance visibility against velocity. That tradeoff is real when teams want to move quickly with copilots, task agents, or multi-agent systems. Best practice is evolving, and there is no universal standard for whether the registry should live in a platform engineering stack, an MLOps stack, or a dedicated agent governance plane.

Some organisations extend the model registry with custom fields for agent metadata. That can work for small deployments, but it usually breaks down once agents are allowed to persist, re-enter workflows, or inherit delegated authority. A model registry rarely provides heartbeat monitoring, runtime kill-switches, or policy-aware discovery. For those cases, a separate agent registry is cleaner and safer. The difference becomes sharper in environments that use NIST AI Risk Management Framework controls to evidence accountability, because the question is not only what model was approved, but which autonomous entity is acting right now.

NHIMG’s coverage of incidents such as CoPhish OAuth Token Theft via Copilot Studio and Replit AI Tool Database Deletion underscores the edge case that matters most: once an agent can act on live systems, registry design becomes a security control, not just an inventory concern.

Related resources from NHI Mgmt Group

• What is the difference between human identity governance and AI agent governance?
• What is the difference between governing human access and governing AI agent access?
• What is the difference between managed identities and hardcoded secrets for AI agents?
• What is the difference between workload identity and API keys for AI agents?