Agents need both views because they are reusable assets and governed actors at the same time. A catalog helps teams find a proven agent. An inventory records who owns it, what it can access, and whether it remains within approved risk boundaries.
Why This Matters for Security Teams
AI agents blur a line that traditional security programs rely on: whether something is a reusable software asset or a governed actor with active access. A catalog supports discovery and reuse, but an inventory is what makes ownership, entitlement review, and risk acceptance possible. Without both, teams either lose visibility into what exists or lose control over what can act.
This distinction matters because agentic systems are not static applications. They are goal-driven, can chain tools, and may change behaviour based on context. That makes “approved once” an unsafe security model. Current guidance from the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework both point toward continuous governance, not one-time registration. NHIMG research in AI Agents: The New Attack Surface report found that 80% of organisations report agents already performing actions beyond intended scope, which is exactly the kind of drift that a catalog alone will not catch.
In practice, many security teams encounter excessive agent privilege only after an incident or audit finding, rather than through intentional lifecycle governance.
How It Works in Practice
A catalog and an inventory serve different operational questions. The catalog answers “what agent patterns are approved and reusable?” The inventory answers “what instances, owners, secrets, tool permissions, and risk decisions are currently in play?” For AI agents, both views should be linked because the same agent design can be instantiated multiple times with different scopes, environments, or data boundaries.
Practically, the catalog should hold the reusable definition: purpose, model dependencies, tool integrations, guardrails, and approved use cases. The inventory should hold the live control state: accountable owner, business sponsor, runtime environment, workload identity, credential source, current TTL, last review date, and any exceptions. This is where workload identity matters. Instead of treating the agent as just another app account, teams increasingly use cryptographic identity primitives such as SPIFFE/SPIRE or OIDC-backed workload tokens so the system can prove what the agent is, not just what password or key it has.
That operational split also supports just-in-time access. An agent can be catalogued as an approved pattern, while each deployment receives ephemeral credentials and real-time policy checks based on the task at hand. This is aligned with the governance direction in the CSA MAESTRO agentic AI threat modeling framework and with NHIMG’s OWASP NHI Top 10, which both emphasize that access must be reviewed in context, not assumed safe because the agent is known.
- Use the catalog for reuse, standardisation, and approved patterns.
- Use the inventory for ownership, entitlements, secrets, and live risk status.
- Bind every agent instance to a workload identity and short-lived credentials.
- Re-evaluate access at runtime when the task, context, or toolchain changes.
These controls tend to break down in fast-moving build pipelines where agents are spawned automatically and never reconciled back into governance records.
Common Variations and Edge Cases
Tighter catalog and inventory controls often increase process overhead, so organisations have to balance speed of agent deployment against the cost of continuous governance. That tradeoff is real, especially when teams want to pilot new copilots, internal assistants, or autonomous workflows quickly.
One common edge case is a shared agent template deployed across multiple business units. Best practice is evolving, but current guidance suggests the template belongs in the catalog while each deployment instance belongs in the inventory with its own owner, scope, and approval record. Another edge case is a transient agent used for a single workflow. If it can access secrets, call tools, or act on production data, it still belongs in inventory even if it is short-lived.
A second nuance is that some teams try to inventory only “production” agents. That misses staging agents, developer sandboxes, and orchestration layers that can still reach sensitive systems. NHIMG research on the LLMjacking threat pattern shows why secret exposure and rapid attacker action can turn a low-friction test deployment into a live compromise path. The practical rule is simple: if an agent can authenticate, invoke tools, or move data, it needs an inventory record.
Where organisations fail most often is not in creating one of the two views, but in letting them drift apart so the approved design no longer matches the live access state.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A1 | Catalog and inventory gaps enable agent misuse and hidden privilege. |
| CSA MAESTRO | GOV-1 | MAESTRO stresses governance for agent lifecycle, ownership, and controls. |
| NIST AI RMF | GOVERN | AI RMF requires accountable oversight for dynamic AI systems. |
Track every agent instance and continuously verify it matches approved intent, tools, and runtime access.
