AI model catalog vs inventory: where agents fit in governance

At a glance

What this is: This is a governance comparison of AI model catalog and inventory models, with the central finding that agents belong in both because discovery and accountability solve different problems.

Why it matters: IAM, IGA and AI governance teams need separate discovery and accountability views for agents and other AI assets, or they will either miss reuse opportunities or lose audit-grade ownership and risk visibility.

Context

An AI model catalog and an AI model inventory are not the same control, even when they draw from the same underlying records. The catalog is built for findability and reuse, while the inventory is built for accountability, auditability and risk ownership, which matters directly to AI governance and identity governance.

The governance gap appears when teams treat discovery as control. If an AI asset can be found but not attributed, assessed or tracked through its lifecycle, oversight breaks down even though reuse looks efficient. That is why agents need to be represented in both views, not just one.

The article’s framing is typical of organisations that are starting to operationalise AI governance: the terminology sounds close, but the governance purposes are materially different.

Key questions

Q: Why do organisations need both an AI model catalog and an AI model inventory?

A: They solve different problems. A catalog helps builders find and reuse approved AI assets, while an inventory gives risk, compliance and AI leadership the accountability needed for ownership, audit and trust. If you only have a catalog, reuse can outpace governance. If you only have an inventory, assets may be controlled but hard to discover and reuse safely.

Q: What breaks when AI asset discovery and inventory are missing from governance?

A: Without AI asset discovery, security teams lose visibility into models, notebooks, data lineage, agents, and toolsets. That means hidden dependencies, untracked exposures, and compliance gaps can persist even when teams believe controls are in place. In practice, missing inventory makes risk assessment incomplete, response slower, and audit evidence harder to assemble across the AI lifecycle.

Q: How should teams inventory AI agents that can take actions?

A: Teams should record agents as operational actors, not just as models with metadata. That means capturing ownership, data access, tool scope, lifecycle state and assessment status. Agents matter more than passive models because they can trigger downstream actions, so the inventory has to answer what they can do, not only what they are called.

Q: What is the difference between an AI model registry and a catalog or inventory?

A: A registry is the underlying record of AI assets. A catalog and an inventory are different views over that record, with the catalog optimised for discovery and reuse and the inventory optimised for accountability, audit and risk management. The registry is the shared source of truth, while the other two are purpose-built views.

Technical breakdown

Why a catalog cannot replace an inventory for AI governance

A catalog is a discovery layer. It stores metadata that helps builders search for approved AI assets, understand intended use and reuse them without recreating existing work. An inventory is a governance system of record. It ties each asset to an owner, risk tier, data access footprint, lifecycle status and assessment state. The two can share the same registry, but they answer different control questions. Confusing them creates a blind spot where something is discoverable but not governable, or governable on paper but invisible to builders.

Practical implication: Treat catalog metadata and inventory metadata as distinct control objectives, even when they sit on the same underlying record.

Where agents change the governance model

Agents are not just another AI asset category. They can take actions, call tools and spawn other agents, which means one registered asset may produce several execution paths. That creates a governance requirement that is closer to identity and privilege management than to static model cataloguing. A catalog helps teams reuse a trusted agent. An inventory is what keeps that agent attributable when it behaves across multiple actions or dependencies. The key governance error is listing an agent as if it were only a model with outputs, when it also functions as an actor with operational effects.

Practical implication: Inventory agents with ownership, access scope and assessment state, not just descriptive metadata.

Why shared records are the real control point

The strongest pattern is a single registry that feeds both discovery and governance views. That avoids duplicate records, stale oversight and a gap between what builders can find and what risk teams can defend. In practice, unified records make it possible to publish an AI asset once, then surface it to builders as reusable and to governance teams as accountable. The operational benefit is consistency, but the security benefit is much more important: an asset that is not registered once cannot be both reused safely and governed credibly.

Practical implication: Build one authoritative registry and project it into discovery and governance views rather than maintaining separate inventories and catalogs.

NHI Mgmt Group analysis

Catalogs and inventories are complementary governance layers, not competing tools. The catalog solves search and reuse, while the inventory solves ownership, risk, lifecycle and auditability. When organisations collapse those functions into one view, they either overstate control or understate usability, and both outcomes create governance drift. The practitioner conclusion is simple: discovery and accountability need separate questions even if they share the same records.

Agents belong in the highest-governance tier of AI registries because they can act. A model predicts, but an agent can call tools, chain actions and trigger downstream behaviour. That changes the governance requirement from passive cataloguing to operational accountability. The field should stop treating agents as a catalog edge case and start treating them as records that require explicit ownership, scope and review.

Unified registries are the named concept that matters here: one record, two views. This is the practical answer to duplicated effort and stale oversight. One authoritative record can support builder discovery and risk management at the same time, but only if the governance metadata is complete enough to stand up under audit. Practitioners should design for shared source data, not parallel tools with different truth sets.

AI governance will increasingly converge with identity governance as autonomous capabilities spread. Once agents can call tools and delegate work, the old separation between model management and access governance becomes harder to defend. The question is no longer simply whether an AI asset exists, but who owns it, what it can touch and how its lifecycle is controlled. Teams should expect catalog design to be judged by accountability outcomes, not search convenience.

The inventory is the control plane, and the catalog is the experience layer. That distinction helps teams sequence work correctly. If risk, regulation or audit readiness is the priority, inventory logic has to come first; if adoption and reuse are the problem, catalog usability matters, but never at the expense of governance traceability. The practitioner implication is to align the record structure first and the user views second.

From our research library:

• Only 5.7% of organisations have full visibility into their service accounts, according to the Ultimate Guide to NHIs.
• Read next: NHI Lifecycle Management Guide

What this signals

One record, two views: AI governance programmes work best when the same source data feeds both discovery for builders and accountability for risk owners. Separate tools with separate truth sets create drift, while a single registry keeps reuse and oversight aligned across the lifecycle.

The inventory should be treated as the control plane because it holds the owner, risk tier, data access and assessment status that audits depend on. The catalog remains valuable, but it is only a usable front end if those governance fields stay current underneath.

For practitioners

• Separate discovery from accountability Define the catalog as the builder-facing discovery layer and the inventory as the authoritative governance record. Keep the questions different so teams do not assume findability equals approval.
• Register agents as acting assets Capture each agent’s owner, tool access, data touchpoints and lifecycle status in the inventory, because an agent can behave like more than one operational system.
• Unify records before creating tools Use one underlying registry that feeds both views so discovery metadata and governance metadata stay aligned as assets change over time.
• Use governance data for audit readiness Make assessment status, risk tier and accountability fields mandatory for every production AI asset so reviewers can answer who owns it and whether it is still valid.

Key takeaways

• AI model catalogs help teams find and reuse assets, but inventories are the control surface that determines ownership, risk and auditability.
• Agents intensify the problem because they do not just represent models, they can initiate actions, call tools and create downstream dependencies.
• The governance answer is one authoritative registry projected into two views, so discovery and accountability stay synchronised.

Key terms

• AI Model Catalog: A searchable directory of AI assets built so teams can find, compare, and reuse them. It usually stores descriptive metadata, intended use, and access information. A catalog improves discovery, but by itself it does not prove that an asset is approved, current, or accountable for production use.
• AI Model Inventory: The authoritative record of AI assets in production, including ownership, risk tier, lifecycle stage, data access, and assessment status. It exists to support accountability, auditability, and governance. Unlike a catalog, it is designed to answer who is responsible and whether the asset remains within approved bounds.
• Agentic Registry: An agentic registry is a centralized inventory of autonomous agents, MCP servers, tools, and related identities in use across an environment. It gives security teams a live source of truth for what exists, what is connected, and how each component maps to policy, risk, and ownership.
• AI Governance: AI governance is the set of controls used to discover, classify, approve, restrict, monitor, and revoke AI-enabled access. It connects identity, data, and policy so organisations can manage what AI can reach, what it can share, and when it should be stopped.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM programme, it is worth exploring.