Topic starter
27/06/2026 1:28 pm
TL;DR: A current AI model and agent inventory is the baseline for governing models, use cases and autonomous agents because it ties each asset to ownership, risk and data access, according to Collibra. Without that record, shadow deployments, orphaned agents and stale spreadsheets turn AI oversight into guesswork.
Editorial analysis by NHI Mgmt Group, based on content published by Collibra: “AI Model and Agent Inventory: How to Catalog Every AI System in Your Enterprise”.
Key questions
Q: What breaks when AI systems are deployed without a complete inventory?
A: Governance breaks first, then auditability.
Q: When should organisations prioritise inventory over broader AI governance controls?
A: Inventory should come first because every downstream control depends on knowing what exists.
Q: What are the signs that AI inventory governance is failing?
A: Common signs include duplicated tools, unknown model owners, incomplete lineage, inconsistent risk ratings, and AI deployments that appear in production before they are formally registered.
Practitioner guidance
- Implement deployment-time registration Register every model, use case and agent at the point it enters production, with a unique ID, owner, type and risk tier attached immediately.
- Separate agents from models in the inventory Require distinct records for systems that act, including the tools they can call and the data they can reach, rather than folding them into a model-only list.
- Automate inventory capture from code Pull asset metadata from CI/CD or orchestration pipelines so the inventory is updated from the deployed configuration instead of manual intake forms.
Bottom line: A live AI inventory is the control point that connects ownership, risk and data access to every model, use case and agent in production.
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27/06/2026 2:49 pm
AI inventory is now the first governance test because visibility precedes every other control. If an organisation cannot state what AI systems exist, it cannot credibly assign ownership, classify risk or evidence oversight. That is true for models, but it becomes sharper for agents because runtime action expands the governance burden beyond a simple catalogue. The practitioner conclusion is straightforward: inventory completeness is the prerequisite for any defensible AI control model.
A few things that frame the scale:
- 76% of organisations cite shadow AI as a definite or probable problem, up from 61% in 2025, according to HiddenLayer's 2026 AI Threat Landscape Report.
A question worth separating out:
Q: How should teams govern AI agents without treating the model as the only control point?
A: Governance should follow the full runtime, not just the model checkpoint. Teams need approval gates for tool use, version control for prompts and procedures, isolation for sub-agents, and traceability for every governed run. If the harness changes, policy changes, even when the model does not.
👉 Read our full editorial: AI model and agent inventory is now the first governance test