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The reliable way to find every AI agent is to maintain one authoritative registry, feed it from identity, cloud, code, runtime, network and data systems, and require registration before production use. Give every agent a unique identity and accountable owner; record its purpose, model, tools, data access, delegated permissions, risk tier, controls, cost and lifecycle; then continuously reconcile the registry with observed activity. This catches vendor-provided, low-code, desktop, MCP-connected and shadow agents instead of limiting the inventory to projects your central IT team already knows about.
What an AI-agent inventory is—and why it matters
Microsoft defines an agent as software that can access data, make decisions and take actions across business systems. That definition includes more than a chat window. A customer-service workflow that decides whether to issue a refund, a coding helper that can open pull requests, a retrieval application that can call business APIs, and a vendor’s embedded copilot can all be agents when they make model-driven decisions and perform actions.
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NIST’s AI Risk Management Framework Playbook calls an AI-system inventory an “organized database of artifacts relating to an AI system or model.” An agent inventory applies that idea to the complete operational system: the model, prompts and policies, tools, identities, data connections, deployment and evidence of what the agent actually did. It is not merely a list of model names or software licenses.
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There is no defensible universal statistic for how many agents a typical organization has or what percentage of fleets are inventoried. Treat completeness as a control objective that you measure in your own environment, not as an industry benchmark.
Define the inventory boundary before you scan
Write a short scope statement and publish it to every business unit. A practical boundary includes:
- Agents built in enterprise AI platforms, cloud services, SaaS applications and vendor products.
- Custom services, scripts, serverless jobs and workflow automations that use a model to choose or execute actions.
- Copilots, low-code assistants, local or desktop helpers and browser-based tools used for company work.
- Agents connected to model-context-protocol (MCP) servers, plugins, retrieval systems, vector stores or business APIs.
- Agents that call, delegate to or spawn other agents, including subagents created dynamically at runtime.
Include development, test and production environments, but mark the lifecycle state so a prototype is not confused with an approved production service. Include agents operated by contractors or vendors when they can access organizational data or systems. Exclude a model endpoint that is never used to make an autonomous or delegated decision only if your policy records that exclusion and explains how it was verified.
Name a central governance owner for the registry and an accountable owner in each business unit. The central owner defines the schema, evidence requirements, risk rules and review cadence; the business owner accepts responsibility for the agent’s purpose, outcomes and retirement. A security, privacy, data-protection or compliance contact should be attached where the agent handles regulated or sensitive information.
Choose a registry that can stay authoritative
The registry is authoritative only when it has a stable identifier, an owner, a change history and a way to compare declared information with technical evidence. Choose the least complex system that can meet those requirements now and migrate without losing IDs or history as the fleet grows.
| Approach | Best fit | Safeguards required |
|---|---|---|
| Existing enterprise registry or AI control plane | Organizations with agents on several platforms that need centralized policy and telemetry. | Connectors for each platform, immutable change history, owner and identity fields, lifecycle states, risk and approval records, and APIs for automated discovery. |
| CMDB or service-catalog extension | Organizations that already enforce ownership, change management and service reviews there. | Add agent-specific fields for model, prompts, tools, data flows, autonomy, delegated permissions, evaluations and kill-switch information rather than treating an agent as an ordinary server. |
| Controlled spreadsheet or database | A small early fleet while a stronger platform is being selected. | Unique IDs, restricted editing, required fields, approval workflow, versioned change history, review dates, evidence links and a migration plan. An uncontrolled shared sheet is not an inventory control. |
| Federated catalog | Large organizations in which business units retain local platforms or catalogs. | A central index that reconciles identities, aliases, owners, risk and lifecycle across local systems, with a common schema and escalation path for conflicts. |
Microsoft’s Agent 365 implementation account describes useful registry metadata as the agent’s name, lifecycle status, type, ID, owner, where it was created and where it is used, with additional distinctions such as system-created versus user-created agents. Those fields are a starting point, not a complete risk record.
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Use a minimum viable record for every agent
Create the record when an agent is discovered, even if several values are initially marked unverified. Do not silently drop an agent because its owner or platform is unknown; an ownerless record is the signal that remediation is needed. Keep the first-seen source, discovery timestamp and confidence state (confirmed, probable or unverified) so investigators can distinguish an observed deployment from an owner assertion.
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| Record area | Fields to capture | Evidence to retain |
|---|---|---|
| Identity | Stable agent ID, display name, aliases, creator, tenant or account, environment, runtime identity and deployment location. | Platform object ID, service-principal or workload-identity record, repository and deployment identifier. |
| Purpose and accountability | Business purpose, supported process, owner of record, accountable team, escalation contact and user population. | Approved intake, service record, architecture decision or business-owner attestation. |
| Implementation | Platform, application or repository, framework, model provider and version, prompt or policy package, configuration version and deployment method. | Build artifact, infrastructure-as-code commit, model-deployment record and release history. |
| Capabilities and dependencies | Tools, plugins, MCP servers, APIs, subagents, external services, knowledge bases, vector stores, data sources and destinations. | Tool manifests, connector configuration, network paths, API specifications and an agent bill of materials. |
| Authority | Delegated human identity, roles, scopes, secret reference, read/write/delete capability and agent-to-agent communication permissions. | Identity-provider grants, effective policy evaluation, secret-manager reference and observed calls. |
| 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. | Risk assessment, test results, approval tickets, policy versions and emergency procedure. |
| Operations | Lifecycle state, release version, deployment date, last change, last review or recertification, logs and traces location, usage, token/API/compute cost, cost center, incidents and retirement date. | Telemetry, billing export, incident records, review attestations and decommissioning evidence. |
OWASP’s Agent Observability Standard calls this component inventory an Agent Bill of Materials (AgBOM). An AgBOM should show what tools, models and capabilities are embedded, who authored components, the deployed version and configuration, and external services and data sources. Store the AgBOM with the registry record and generate a new version whenever a model, prompt package, tool, connector or permission changes.
Discover agents from every authoritative technical plane
No single scan finds every agent. Reconcile independent inventories, deduplicate by stable identity plus deployment location, and preserve aliases so a renamed agent does not become a second asset.
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Query the identity provider
Export service principals, workload identities, OAuth application consents, API keys, managed identities and delegated permissions. Look for identities that call model endpoints, tool APIs, data stores or orchestration services. Record both the agent identity and the human identity when the agent acts on a user’s behalf. A permission grant alone does not prove an agent exists, so correlate it with deployment and activity evidence.
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Enumerate cloud and AI platforms
Inventory model deployments, agent runtimes, orchestration resources, copilot or AI-foundry environments, serverless jobs, managed connectors and scheduled workflows. Include disabled, test and failed deployments until their lifecycle state is confirmed. Capture the platform’s native object ID and deployment region or tenant.
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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 clients, prompt packages and references to model endpoints. Link a production record to the exact commit, image digest or release artifact that created it.
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Use runtime and network telemetry
Analyze API-gateway and model-endpoint logs, tool calls, MCP traffic, DNS and proxy records, serverless execution logs and SIEM events. A process that repeatedly calls a model and then invokes business APIs is a high-value lead even when its code or platform record is missing. Retain first-seen and last-seen timestamps, caller identity, destination, action and volume.
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Search data and integration catalogs
Review vector stores, retrieval indexes, document connectors, SaaS integrations and databases for clients that use model-driven retrieval or actions. Map each connection to the agent that can reach it and record the direction of data flow, not just the database name.
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Ask the humans who approve work
Use intake forms, architecture reviews, procurement records, expense and contract data, help-desk tickets and interviews with business owners. This step finds low-code automations, desktop helpers and vendor features that leave little central telemetry. Require the respondent to provide an owner, purpose, identity and evidence; mark the record probable until technical data confirms it.
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Classify each finding as confirmed, probable or unverified. Merge duplicates by stable identity and deployment location, keep aliases and source references, and send an exception to the accountable business unit when no owner can be established.
Make registration a production gate
Discovery is a safety net, not permission to operate. Microsoft recommends requiring every AI agent to be recorded in a single organizational inventory and tracking ownership, purpose, platform and access scope. Implement that requirement in the delivery path:
- Intake: create a draft record with the proposed purpose, owner, platform, model, tools, data classes, identity and environment.
- Design review: validate data flows, autonomy, prohibited actions, human approvals, failure handling, evaluation plan and cost center.
- Identity and access: issue a distinct runtime identity, grant only the required scopes, reference secrets rather than copying them into prompts or code, and document delegated-user behavior.
- Testing: run functional, security, privacy, prompt-injection, tool-abuse and reliability evaluations appropriate to the risk tier. Record results and unresolved limitations.
- Approval and deployment: attach approvals, AgBOM, version and kill-switch owner; block deployment when mandatory fields or approvals are absent.
- Change control: require a new review for model, prompt-policy package, tool, connector, identity, permission, data class or autonomy changes.
For a small environment, a controlled manual register can be an interim solution only when it enforces required fields, access control, change history and review dates. Plan the migration to an automated catalog before manual updates become the only source of truth.
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Find and contain shadow or unregistered agents
Shadow agents usually appear as a mismatch between declarations and evidence. Build detections for:
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- Tool or MCP traffic from a process, host, browser extension or SaaS tenant absent from the catalog.
- Repositories or container images containing agent frameworks, tool schemas or prompt packages that have no approved deployment.
- Vector indexes, document connectors or business APIs receiving traffic from an unknown runtime identity.
- Agents that use tools, data sources or write permissions not declared in their record.
- New scheduled jobs, serverless functions or desktop automations that make model calls and then alter business records.
- Unexpected token, API or compute spend, recursive spawning, unusual agent-to-agent calls or activity outside the declared environment.
When a signal fires, preserve logs and configuration, identify the owner, and classify the deployment before disabling it. If it handles sensitive data or has destructive authority, place it in a quarantine policy that blocks tools or network destinations while an owner and scope are established. Do not delete evidence before determining whether the agent is part of an incident, a vendor feature or a legitimate deployment that was never registered.
Bind every agent to a distinct identity and owner
Use one runtime identity per agent wherever the platform supports it. Shared credentials make it impossible to attribute an action, rotate access safely or revoke one agent without interrupting unrelated services. Record the human identity for on-behalf-of actions, but do not let a user’s broad permissions silently become the agent’s standing authority.
Compare effective permissions with declared permissions. A role assignment, group membership or inherited policy can grant more access than the agent’s manifest shows. Review read, write and delete capability separately; a read-only data connector and a connector that can alter records should not receive the same risk treatment. Scope secrets to the smallest resource and operation, set expiration or rotation, and store only a reference in the agent configuration.
Assign a named owner of record, an accountable team, an escalation contact and a kill-switch owner. Ownership must survive staff changes: use a team mailbox or service group as the durable accountability point and require reassignment when an individual leaves or changes role. An agent without a current owner should move to a restricted state until ownership is accepted.
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Risk is not determined by the model name alone. Evaluate data sensitivity, external exposure, tool breadth, write or delete authority, autonomy, business criticality, user population and reversibility. The following four-tier model is a practical internal starting point; adapt thresholds to your regulatory and operational context.
| Tier | Typical characteristics | Minimum controls |
|---|---|---|
| 1 — Assistive | Low-sensitivity data, narrow read-only tools, human review of every external action and low business impact. | Registered owner, unique identity, basic evaluation, logging, cost allocation and periodic review. |
| 2 — Controlled workflow | Internal or moderately sensitive data, several tools or limited write actions, bounded user population and reversible outcomes. | Documented data flows, least-privilege scopes, approval checkpoints for writes, tool allow-list, behavioral monitoring, incident runbook and scheduled recertification. |
| 3 — High impact | Sensitive or regulated data, external exposure, broad write authority, autonomous multi-step decisions or material customer, financial or operational consequences. | Formal risk approval, adversarial and abuse testing, strong human approval, segregation of duties, detailed traces, anomaly alerts, tested kill switch and frequent recertification. |
| 4 — Critical | Irreversible or safety-critical actions, privileged administration, large-scale personal-data processing or essential business operations. | Executive or designated risk-owner approval, isolation, dual control for dangerous actions, continuous monitoring, rehearsed incident response, strict change windows and automatic shutdown on control failure. |
Record the reasons for the tier and the controls that lower or raise it. Recalculate after a new tool, data source, model, user population, autonomy setting or business process changes.
Monitor behavior, recertify access and retire safely
Ingest activity and tool-use telemetry continuously enough to detect material changes before the next scheduled review. At minimum, link model calls, tool invocations, data destinations, identity, approvals, errors, latency, usage and cost to the stable agent ID. Keep prompts or sensitive payloads according to your privacy and retention policy; traces are useful only when access to them is controlled.
Alert on new tools, privilege changes, access outside declared data scope, unusual spend, recursive spawning, unexpected agent-to-agent behavior, disabled logging, a changed model or prompt package, and activity from an unapproved environment. Compare observed tool calls with the declared AgBOM and effective permissions. Treat a mismatch as a review event, not as harmless drift.
Set a review cadence based on risk and require an additional attestation after a material change or incident. The owner should confirm purpose, users, data classes, identity, tools, permissions, controls, cost center and retirement date. Capture the attestation, reviewer and evidence in the registry.
Retirement is a controlled change: announce the end date, stop new requests, revoke identities and secrets, disable connectors, preserve required logs and records, remove scheduled jobs and containers, and verify that no downstream process still depends on the agent. Automatically flag or quarantine agents that are stale, unsupported, ownerless or unused beyond your policy window; provide an exception path with a new owner and expiry date.
Compare inventory approaches with these questions
When selecting a platform or designing a federated process, score each option against the same questions:
- Coverage: Can it discover cloud, SaaS, code, desktop, vendor and third-party agents?
- Identity quality: Does every record have a stable, auditable identity and traceability to a delegated user?
- Metadata depth: Can it store tools, data sources, versions, permissions, risk, owner and lifecycle?
- Freshness: Is discovery continuous, event-driven or manual, and how quickly do changes appear?
- Enforcement: Can it block, quarantine or require remediation for unregistered or over-permissioned agents?
- Evidence: Are logs, approvals, evaluations, incidents and recertifications linked to the same record?
- Scale and interoperability: Can it reconcile multiple platforms, protocols and business units without duplicate identities?
- Cost visibility: Can token, API and compute usage be allocated to an owner or cost center?
Reject a catalog that is easy to populate but cannot prove freshness, effective permissions or observed behavior. A smaller registry with reliable evidence is safer than a large directory of names that nobody can validate.
A practical implementation sequence
- Set policy and accountability: publish the scope, required fields, risk axes, prohibited actions, review cadence and exception process; name the central owner and business-unit owners.
- Build the schema and ID strategy: create immutable IDs, lifecycle states, aliases, confidence states and relationships for subagents, tools, data stores, identities and deployments.
- Seed the registry: ingest identity, cloud, code, runtime, network and data-catalog findings; add human and procurement leads; deduplicate and assign provisional owners.
- Remediate the highest-risk gaps: address unknown owners, shared credentials, broad write or delete access, missing logs, sensitive data flows and absent kill switches first.
- Wire the production gate: connect intake and CI/CD or platform deployment controls so an unregistered agent cannot reach production without an approved record.
- Automate reconciliation: schedule or stream discovery, permission comparison, AgBOM updates, cost allocation and alerts for drift.
- Operate the lifecycle: run recertification, incident response, exception expiry and retirement reviews; report coverage, ownerlessness, permission drift, stale records and spend by business unit.
Common failure modes and fixes
- Only counting centrally built agents: add SaaS, low-code, desktop, vendor and contractor discovery, plus human interviews and procurement data.
- Using a model list as the inventory: add identities, tools, data flows, prompts, permissions, deployment and evidence; the model is one component.
- Relying on self-registration: reconcile forms with identity, runtime and network telemetry, and quarantine unregistered activity.
- Recording declared permissions only: calculate effective roles and inherited grants, then compare them with observed calls.
- Sharing one service account: issue a distinct identity per agent and preserve delegated-user context.
- Making ownership a one-time field: require recurring attestation, owner reassignment after staff changes and an escalation path for ownerless records.
- Ignoring subagents and external services: version an AgBOM and model agent-to-agent, MCP, plugin, API and vector-store relationships.
- Logging too little or too much: capture enough identity, action, destination, approval, version and cost data to investigate while applying privacy-based retention and access controls to payloads.
- Never testing retirement: rehearse revocation and shutdown, verify dependencies, and preserve evidence before deletion.
Inventory readiness checklist
- Every agent has an immutable ID, owner, accountable team, lifecycle state and environment.
- Purpose, users, model and version, prompt or policy package, tools, subagents, data sources and destinations are recorded.
- Runtime identity, delegated user, effective permissions and secret reference are known.
- Risk tier, data classification, autonomy, human approvals, prohibited actions and kill-switch owner are documented.
- AgBOM, deployment artifact, evaluations, approvals, logs, incidents, usage and cost are linked.
- Discovery covers identity, cloud, code, delivery, runtime, network, data catalogs and human channels.
- Unregistered, over-permissioned, stale and ownerless agents trigger a defined response.
- Recertification, material-change review and retirement leave an auditable record.
The objective is not a one-time census. It is a living control loop: discover, identify, register, constrain, observe, recertify and retire. When every action-capable agent has a durable identity, accountable owner and evidence-backed record, security and business teams can decide what may run, what must change and what should be stopped.
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