AI Catalogs: The Next Governance Artifact

AI catalogs are quickly becoming one of the most important tools in enterprise AI. They serve as the registry and single source of truth for AI assets—agents, MCP tools, models, and the metadata that governs them. Early client implementations started as simple registries to list active agents and use cases, then evolved to add access control and lifecycle management.

In practice, a useful catalog record captures far more than a name and a link. It codifies ownership, purpose, risk class, allowed data, required controls, environments, deployment locations, and operational status. It also points to evaluations, guardrails, test evidence, logs, and incident history. The result is a living system of record that reduces discovery time, makes policy enforceable, and creates shared context across engineering, security, and also risk.

How catalogs fit in the enterprise stack

AI catalogs make sense when viewed alongside three adjacent components:

• AI gateways: Operational proxies that mediate input and output between LLMs, agents, and internal applications. They regulate and route usage, expose capabilities to third parties when appropriate, and support multi-inference deployments across countries, offices, and branches—including at the edge.
  • Typical gateway policies referenced from the catalog: model routing and fallbacks, rate limits, redaction rules, prompt/response filters, and egress controls.
  • Observability flows back into the catalog as runtime evidence: usage stats, error rates, latency, and drift or jailbreak detection signals.
• AI governance: The catalog is the operational instrument for governance platforms, enabling risk and compliance controls, approvals, auditability, and policy enforcement across AI assets.
  • The catalog anchors workflows: risk assessments, DPIAs, approvals, documented constraints, and periodic reviews.
  • Policy as data: instead of documents on shared drives, policies become fields the gateway can enforce.
• AI security: Access control, guardrails for inputs and outputs, and execution requirements that keep AI interactions safe. The catalog provides the authoritative mapping of who can do what, where, and under which constraints.
  • Secrets, identity, and environment isolation are discoverable via the catalog; the gateway and runtime enforce them.
  • Threat models and mitigations are stored per asset and kept current as the asset evolves.

In the coming months, catalogs and registries will be a core pillar of enterprise AI governance for most enterprises.

What belongs in an AI catalog

Start minimal but intentional. A pragmatic record typically includes:

• Identity and classification: name, description, owner, business sponsor, domain, criticality, risk tier, data sensitivity.
• Lifecycle and readiness: status (proposed → experimental → approved → production → deprecated), last review date, evaluation results, test evidence links.
• Policy and risk: allowed inputs/outputs, prohibited content, PII handling, data residency constraints, jailbreak/abuse safeguards, human-in-the-loop requirements.
• Technical integration: endpoints, model backends, gateways used, routing rules, dependencies (MCP tools, services, datasets), environments (dev/test/prod/edge).
• Observability: logs, metrics, traces, prompt/response captures (sampled), incident history, change history.
• Compliance and audit: approvals, reviewers, linked controls, audit artifacts, exceptions with expiry.
• Relationships: agents → tools (one-to-many), tools → capabilities, agent → model variants, asset → consuming applications.

Lessons from data: the product hub analogy

On the data side, the rise of the data product hub reframed datasets as governed products. A data product combines a data asset with metadata that defines usage agreements, licenses, access controls, dependencies, lineage, and more. We are at the verge of a similar wave for AI: governance and management artifacts that help enterprises manage the full lifecycle of AI assets.

Parallels that resonate with practitioners:

• Data products publish SLAs; AI assets publish expected behavior, safety constraints, and guardrails.
• Data products manage schemas and versions; AI assets manage prompts, tool contracts, and model versions.
• Data lineages reveal upstream/downstream; AI lineages reveal tool dependencies, model routing, and policy inheritance.

MCP tools add a new dimension

Modern enterprise agent development increasingly relies on MCP tools. Expect rich ecosystems that blend proprietary enterprise assets, open source, and partner technologies. Like agents, MCP tools need catalog coverage—versioning, ownership, policies, testing status—with a one-to-many relationship between agents and tools.

Good practices I see working:

• Treat tools like products: semantic versioning, changelogs, breaking-change notices, and deprecation timelines.
• Maintain a compatibility matrix: which agent versions are validated with which tool versions and model backends.
• Wire CI to the catalog: publish test results, evals, and security scans as part of the release so consumers can trust upgrades.

Open models, edge deployments, and gateways

With the growing adoption of open-source models such as GPT-OSS and Llama, many organizations will deploy models on-premises and at the edge. Maintaining consistent management across corporate and edge locations introduces new governance requirements. AI gateways—used in conjunction with AI catalogs—will become standard in distributed enterprises.

Considerations for distributed deployments:

• Data sovereignty and residency: catalog fields make these constraints explicit and gateway rules enforce them.
• Latency and reliability: gateway routing and local fallbacks reduce dependency on centralized inference.
• Federation: regional catalogs synchronize a subset of records with a central catalog; ownership and policies remain local where needed.

Common pitfalls and how to avoid them

• Treating the catalog as documentation: If it’s not integrated with the gateway and CI/CD, it will go stale. Make registration and updates part of deployment.
• Over-modeling too early: Start with a minimal schema and add fields when a policy or decision relies on them.
• Manual-only processes: Provide an API/CLI and bootstrap scripts so teams can register assets programmatically.
• No clear ownership: Every asset needs a named owner and a time-bound review schedule.
• Ignoring runtime evidence: Surface metrics, incidents, and evaluations in the catalog record to close the loop.

What to do next: an actionable checklist

• Define scope and ownership: Name an accountable owner and identify the first asset classes (agents, MCP tools, models) to register; in week 1 select 3–5 production-like assets and one gateway to integrate.
• Establish a minimal data model: Asset type, owner, purpose, risk class, data sensitivity, permitted inputs/outputs, dependencies, environments, lifecycle states; keep it to fields you will actively enforce or review in the next 90 days.
• Stand up a catalog MVP: Start pragmatic (existing platform, spreadsheet + API, or a dedicated catalog) and make it easy to register and query assets; provide a simple API/CLI and a form for manual entry, and automate population from CI where possible.
• Integrate with your gateway: Register routes, model policies, rate limits, logging, and observability that link back to catalog records; drive gateway configs from catalog fields to prevent drift and shadow policies.
• Connect governance workflows: Tie risk assessment, approvals, and audit logging to catalog entries and enforce policies at runtime via the gateway; add review SLAs and automatic reminders tied to lifecycle states.
• Implement security guardrails: Secrets management, RBAC, input/output filtering, and environment isolation defined and discoverable via the catalog; record threat models and required mitigations per asset and verify in CI/CD.
• Register MCP tools and agents together: Capture versions, tests, and compatibility, and model one-to-many relationships between agents and tools; maintain a compatibility matrix and publish it alongside releases.
• Operationalize lifecycle management: Onboarding, change control, deprecation, break-glass procedures, and incident response—all grounded in the catalog; require a rollback plan and an owner-acknowledged runbook before promotion.
• Measure and iterate: Track adoption, policy violations prevented, time-to-approve, and operational MTTR, and refine the model as you learn; publish a monthly scorecard to keep stakeholders aligned.

Done well, the catalog becomes the connective tissue for enterprise AI—linking what you build, how it runs, and how it is governed—so you can scale safely and with confidence.