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To find every AI agent in a company, combine automated discovery with mandatory registration in one authoritative registry. Reconcile that registry continuously with identity systems, cloud and AI platforms, source code, deployment pipelines, runtime telemetry, data catalogs and business-owner attestations. Give every agent a stable identity, named owner, declared purpose, tool and data inventory, effective permissions, risk tier, monitoring record and retirement date.
This approach finds approved systems and shadow deployments alike. It also lets security teams block unregistered agents, reduce excessive privileges, trace actions to a human or service identity and prove that high-impact agents are reviewed before they operate.
Why an AI-agent inventory is a control, not a spreadsheet
An AI agent is software that can access data, make decisions and take actions across business systems. A chatbot that only answers questions has a different exposure from an agent that can send messages, modify records, call payment APIs or create additional agents. Without a complete inventory, security and compliance teams cannot know which systems have those abilities, who is accountable for them or whether their permissions still match their purpose.
Microsoft’s guidance, updated March 19, 2026, says a governance baseline should apply to every agent before it operates and recommends a centralized, enforceable baseline. It specifically warns that untracked “shadow” deployments create security and cost risks because an organization cannot govern agents it does not know exist.
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The NIST AI RMF Playbook defines an AI system inventory as “an organized database of artifacts relating to an AI system or model” and calls for mechanisms that support high-level queries across models. For agents, the record must go beyond a model name: it needs the agent’s identity, tools, data paths, authority, behavior and lifecycle evidence.
Use the inventory to answer practical questions quickly:
- Which agents can read confidential or regulated data?
- Which agents can write, delete, approve, purchase or communicate externally?
- Who owns each agent, and which human identity caused a specific action?
- What changed since the last review?
- Which agents are idle, unsupported, over-permissioned or unregistered?
Define what counts as an agent
Write a scope statement before collecting records. Use a capability-based definition rather than limiting the term to one vendor’s product label. Include any software that uses a model-driven decision or plan to retrieve information, invoke tools or take action with limited human direction.
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- Copilots embedded in productivity, service-management, development or business applications.
- Workflow automations that use a model to choose steps, recipients, records or actions.
- Low-code, no-code, desktop and local assistants.
- Services connected to model-context protocols (MCP), plugins, APIs, databases or vector stores.
- Agents that call, delegate to or spawn other agents.
- Development, test, staging and production deployments, including temporary pilots.
Record ordinary model APIs separately when they are merely called by an application with fixed logic; link those models as dependencies of the application. Treat a system as an agent when it can select tools, sequence actions, change state or delegate work based on model output.
Choose an authoritative registry
The registry is the control plane for identity, ownership, lifecycle and evidence. It can be a dedicated enterprise control plane, a configuration-management database (CMDB) extension, a controlled database or a federated catalog. The technology matters less than enforcement: every production agent must have a unique ID, an owner, a change history and a review date.
| Approach | Best fit | Controls required |
|---|---|---|
| Enterprise registry or governance control plane | Multiple clouds, platforms and business units | Connectors for discovery, identity, activity, risk and lifecycle; deduplication across platforms |
| CMDB or service-catalog extension | Organizations with mature asset ownership and change management | Agent-specific fields for models, tools, data, permissions, evaluations and telemetry |
| Controlled database or spreadsheet | Small early fleet or a short transition period | Restricted access, unique IDs, approvals, version history, review dates and a migration plan |
| Federated catalog | Large enterprises where business units keep local systems | A central index that reconciles identities, owners, risk and lifecycle across local catalogs |
A registry should expose the fields needed for an operational query, not just an inventory count. Microsoft Digital Inside Track described a useful record as containing the agent’s name, lifecycle status, type, ID, owner, where it was created and where it is used, with additional metadata for system-created and user-created agents and newer agent types.
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Minimum record for every agent
Create one record per logical agent and deployment. If the same design runs in development and production, link the records but give each deployment its own environment, identity and telemetry references. Preserve aliases so a renamed agent remains traceable.
| Record area | Fields to capture | Why it matters |
|---|---|---|
| Identity | Stable agent ID, display name, creator, tenant or account, runtime identity, environment and aliases | Supports deduplication, attribution and reliable joins to logs and permissions |
| Purpose and accountability | Business purpose, supported process, owner of record, accountable team and escalation contact | Provides a decision-maker for approvals, incidents and retirement |
| Implementation | Platform, repository or application, framework, model and provider, model version, prompt or policy package and deployment location | Makes versions, changes and dependencies auditable |
| Capabilities and dependencies | Tools, plugins, MCP servers, APIs, subagents, external services, knowledge bases, vector stores and data sources or destinations | Shows the agent’s actual attack and data-exposure surface |
| Authority | Delegated human identity, roles, scopes, secret reference, read/write/delete capability and agent-to-agent permissions | Shows what the agent can do, not merely what its documentation claims |
| Risk and controls | Data classification, autonomy level, business criticality, risk tier, human approval points, prohibited actions, evaluation or red-team status and kill-switch owner | Determines the level of testing, monitoring and approval required |
| Operations | Lifecycle state, version, deployment date, last change, last review or recertification, logs and traces location, usage, token/API/compute cost, cost center, incidents and retirement date | Supports reliability, budgeting, incident response and decommissioning |
OWASP’s Agent Observability Standard calls this component inventory an Agent Bill of Materials (AgBOM). It says AgBOM metadata should show what tools, models and capabilities are embedded in an agent, who authored components, the deployed version and configuration, and the external services and data sources involved. Store that information as linked objects where possible, so a single tool or model update can reveal every affected agent.
Discover agents from every technical plane
No single scanner sees the entire fleet. Run the following sources in parallel, normalize their results and reconcile them into the registry.
1. Start with scope and accountable owners
- Publish the capability-based definition and examples.
- Assign a central governance owner for the registry and require every business unit to name an accountable owner.
- Set the production rule: no agent may operate without a registry ID, owner, approved purpose and declared access scope.
- Give employees an intake path for pilots and shadow deployments; make disclosure safer than concealment.
2. Query identity systems
Export service principals, workload identities, OAuth application consents, API keys, delegated permissions and group assignments from the identity provider. Look for identities created by automation platforms, model runtimes and integration tools. Record both the identity attached to the agent and the human identity when the agent acts on a user’s behalf.
3. Enumerate cloud and AI platforms
Collect model deployments, agent runtimes, orchestration resources, managed connectors, serverless jobs, copilot or AI-workbench environments and project-level deployments. Include nonproduction subscriptions and accounts. Capture region, tenant, resource owner and deployment status so an abandoned experiment does not disappear from view.
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4. Inspect source and delivery systems
Search repositories, infrastructure-as-code, CI/CD pipelines, container registries, package manifests and deployment records for model SDKs, agent frameworks, tool definitions, MCP configuration and prompt or policy packages. Link a deployed artifact to its commit, build and release record. A repository hit without a deployment is a candidate; a runtime hit without source ownership is an investigation item.
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5. Analyze runtime and network telemetry
Use API-gateway logs, model-endpoint calls, tool invocations, MCP traffic, DNS and proxy records, and SIEM events. Detect model calls from unexpected workloads, new outbound destinations, tool calls outside declared scope and identities that suddenly exhibit agent-like behavior. Keep raw evidence long enough to support an ownership decision and incident investigation.
6. Join data and integration catalogs
Map vector stores, retrieval indexes, document connectors, SaaS integrations and databases accessed by each runtime identity. Record both source and destination, including write targets. This step often reveals an agent that was registered as a simple assistant but can reach sensitive repositories through a connector.
7. Ask the people who operate the business process
Use intake forms, architecture reviews, procurement records and interviews with process owners to find low-code, desktop and third-party deployments that leave little infrastructure evidence. Require the owner to validate purpose, users, data classes, tools and shutdown procedure.
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8. Reconcile and classify findings
Deduplicate by stable identity plus deployment location, while preserving aliases and historical IDs. Mark each finding as confirmed when an owner validates it, probable when technical evidence is strong but ownership is pending, or unverified when it needs investigation. Do not delete an unverified record until its evidence has been reviewed.
Make registration a production gate
Automated discovery finds what exists; registration captures intent and creates accountability. Microsoft recommends requiring every AI agent to be recorded in a single organizational inventory with ownership, purpose, platform and access scope.
- Submit: The creator enters the purpose, process, owner, deployment environments, model, tools, data sources, permissions and proposed risk tier.
- Validate: Automated checks compare declared resources with cloud, code, identity and telemetry data. Missing owners, shared credentials or undeclared tools fail validation.
- Approve: The accountable owner and security or privacy reviewer approve according to risk. High-impact agents need explicit human approval points and a documented kill switch.
- Deploy: The pipeline receives the registry ID and refuses production deployment if the record is absent, expired or over-permissioned.
- Change: Model, prompt, tool, data source, identity, autonomy or destination changes create a new version and trigger the appropriate review.
A controlled manual register can be an interim measure for a small fleet, but it must have an owner, access controls, change history and review dates. Treat it as a temporary control, not as permission to operate without telemetry.
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Bind each agent to a unique identity and least privilege
Give every agent a distinct runtime identity. Shared accounts prevent reliable attribution, make revocation risky and hide which agent made a change. Scope secrets to the smallest required resource and store only a reference to the secret in the registry, never the secret value.
- Record effective permissions, not just the roles declared in configuration.
- Separate read, create, update, delete, approve and send capabilities.
- Record the delegated human identity for user-authorized actions.
- Use short-lived credentials where the platform supports them.
- Compare observed tool calls with the declared scope and open an exception when they differ.
- Restrict agent-to-agent calls to named recipients and prevent unrestricted recursive spawning.
When an agent needs broad authority for a business process, add approval checkpoints and narrow the high-risk operation rather than granting blanket administrator access.
Tier risk by capability and consequence
Use a repeatable scoring method based on data sensitivity, external exposure, tool breadth, write or delete authority, autonomy, business criticality, user population and reversibility. The exact labels can vary; the controls must be explicit.
| Example tier | Typical characteristics | Minimum controls |
|---|---|---|
| Tier 1: Low impact | Internal, low-sensitivity data; read-only tools; human verifies every output | Named owner, registry record, basic logging, annual recertification and documented retirement |
| Tier 2: Managed | Internal business data; limited updates; moderate user population or external connector | Scoped identity, change approval, tool and data allowlists, behavioral monitoring and at least semiannual review |
| Tier 3: High impact | Confidential or regulated data, external exposure, broad tools, autonomous updates or material business decisions | Security and privacy review, evaluation or red-team evidence, human approval for sensitive actions, detailed traces, incident playbook and more frequent recertification |
| Tier 4: Critical | Payment, deletion, safety, legal, access-control or business-continuity authority; difficult-to-reverse actions | Explicit executive or risk-owner approval, strong separation of duties, dual control where practical, real-time alerting, tested kill switch and continuous review |
Put prohibited actions in the record, such as changing access policies, exporting regulated data or contacting customers without approval. A risk tier is useful only when it changes deployment and monitoring requirements.
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Ingest activity and tool-use telemetry into the registry or link each record to its logs and traces. Establish a behavioral baseline for normal tools, destinations, volume, latency, spend and delegation patterns.
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Set review triggers for scheduled recertification and material changes. At each review, the owner confirms purpose, users, data, permissions, model and cost center; the technical owner confirms deployment and telemetry; and security or privacy confirms the risk controls. Automatically quarantine or retire agents that are ownerless, stale, unsupported or no longer used. Record the retirement date and revoke identities, secrets, connectors and scheduled jobs rather than merely hiding the registry row.
Detect and handle shadow agents
Shadow agents are often discovered through an identity, network destination or expense record before anyone reports them. Treat the first finding as an intake and safety event, not as proof of malicious intent.
- Restrict dangerous actions or isolate the runtime while preserving logs.
- Identify the creator, business owner, delegated users, data sources and current permissions.
- Register the agent as probable or confirmed and compare observed behavior with its stated purpose.
- Remediate shared credentials, undeclared tools, excessive access and missing approval points.
- Approve it through the normal gate, migrate it to a supported platform or retire it with evidence.
Do not allow an unregistered agent to remain indefinitely in an exception queue. Give every exception an owner, compensating controls and an expiration date.
Compare inventory approaches before selecting tools
Evaluate a registry or governance platform against the questions below. A product that stores names but cannot reconcile identities and telemetry will create a second, unreliable list.
| Question | What a capable approach provides |
|---|---|
| Coverage | Discovery across cloud, SaaS, source code, desktop, local and third-party platforms |
| Identity quality | Stable IDs, deployment-level identities and traceability to delegated users |
| Metadata depth | Tools, data sources, versions, permissions, risk, owner, lifecycle and cost |
| Freshness | Continuous or event-driven discovery rather than an annual manual export |
| Enforcement | Ability to block, quarantine or require remediation for unregistered or over-permissioned agents |
| Evidence | Linked logs, approvals, evaluations, incidents and recertifications |
| Scale and interoperability | Multiple platforms, protocols and business units without duplicate identities |
| Cost visibility | Usage, tokens, API calls and compute allocated to an owner or cost center |
A practical rollout plan
First 30 days: establish control
- Approve the scope definition, ownership model and production registration rule.
- Create the registry schema, stable-ID convention, risk tiers and exception process.
- Connect the identity provider and at least one cloud or AI platform.
- Require owners for all known production agents and quarantine records with no accountable owner.
Days 31–60: expand discovery and enforcement
- Add repository, CI/CD, container, network, model-endpoint and data-catalog feeds.
- Compare declared tools and permissions with observed calls.
- Put registry-ID validation into deployment pipelines.
- Run interviews with business units and review procurement and low-code records.
Days 61–90: operate continuously
- Set review intervals by risk tier and automate reminders and expirations.
- Define alerts for new tools, privilege changes, undeclared data access, spend anomalies and recursive delegation.
- Test the kill switch and retirement workflow on a noncritical agent.
- Publish coverage, ownerlessness, stale-agent, permission-drift and cost-allocation metrics to governance leadership.
Common failure modes and fixes
- Counting only chatbots: Expand the scope to workflow automations, copilots, local helpers, MCP-connected services and subagents.
- Relying on a single platform export: Reconcile identity, cloud, code, runtime, data and human sources.
- Recording declared permissions only: Compare configuration with effective grants and observed tool calls.
- Using names as identifiers: Generate stable IDs and retain aliases across renames and migrations.
- Leaving ownership to the platform team: Require a business owner who is accountable for purpose, impact and retirement.
- Treating inventory as a one-time project: Use event-driven discovery, scheduled recertification and automatic stale-agent handling.
- Storing secrets in the record: Keep secret references and rotate credentials through the approved secrets system.
- Allowing permanent exceptions: Set an expiry, compensating controls and a named approver for every exception.
Operating checklist
- Every agent has a stable ID, environment and unique runtime identity.
- Every record names a business owner, technical owner and escalation contact.
- Purpose, model version, prompt or policy package, tools, data paths and external services are current.
- Effective permissions and delegated human identities are documented.
- Risk tier, prohibited actions, approval points, evaluation evidence and kill-switch owner are recorded.
- Logs, traces, costs, incidents, deployment changes and recertifications are linked.
- Discovery from identity, cloud, code, runtime, data and human channels is reconciled continuously.
- Unregistered, ownerless, over-permissioned and stale agents are blocked, quarantined or retired.
An authoritative inventory turns agent governance from a search exercise into an operating discipline: discover what exists, bind it to an accountable identity, limit what it can do, observe what it actually does and remove it when its owner or purpose disappears.
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