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You cannot govern an AI agent your company does not know exists. The practical answer is a maintained, organization-wide registry that records each agent’s owner, purpose, identity, permissions, data, tools, dependencies, version, approval and risk, combined with runtime monitoring of prompts, tool calls, decisions, actions and privilege changes. Build the inventory from several discovery routes—cloud and platform records, identity systems, deployment artifacts and employee declarations—because no single provider sees every managed, self-hosted, SaaS or locally run agent. Then apply monitoring and review requirements in proportion to autonomy, access, data sensitivity and potential impact.
What is an AI-agent inventory, and why does it matter?
An AI-agent inventory is a maintained database of the agents your organization builds, buys, deploys or permits employees to use. NIST defines an AI-system inventory as “an organized database of artifacts relating to an AI system or model” and recommends that it support both individual-system lookups and organization-wide views. See the NIST AI RMF Playbook.
The inventory is a control, not a spreadsheet project. It connects an agent to a responsible person, a business purpose, an identity, a set of permissions, the data and tools it can reach, the software versions it uses, and the approvals and operating evidence needed to decide whether it should continue running.
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- Operations: teams can find owners, dependencies, health signals and change history when an agent fails.
- Governance: reviewers can apply stricter controls to agents with greater autonomy, access or business impact.
- Cost and rationalization: duplicate, unused or abandoned agents become visible before they create unnecessary exposure.
A record without an owner, scope or review date quickly becomes stale. Assign a maintainer for the registry itself and require an accountable owner for every agent record.
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How should you define what belongs in scope?
Start with a written definition that teams can apply consistently. Include software that can interpret a goal and take one or more actions through tools, APIs, workflows or other systems, whether it is hosted by your company, a cloud provider, a SaaS vendor or an employee’s development environment.
Set the inventory boundary
- Cover production, test, development and personal or departmental environments that can reach company data or services.
- Decide whether prototypes, notebooks, embedded assistants and externally hosted agents are included; document exceptions rather than leaving them implicit.
- Define which business units, subsidiaries, cloud accounts and regions are covered.
- Specify the minimum record fields, review frequency, approval states and retirement evidence.
Security, IT, risk, privacy and business leaders should agree on the boundary and the process for registering an agent that automatic discovery misses. Microsoft’s guidance recommends aligning this work with existing cloud governance, security and compliance responsibilities instead of creating an unrelated oversight structure.
Assign accountability
Name two roles when practical: an accountable owner who accepts the agent’s business and security risk, and an operational maintainer who keeps its technical details and monitoring current. Record a backup contact, the escalation route and what happens if the owner leaves. A team or cost center alone is not enough; someone must be able to approve changes and stop the agent.
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How do you discover every agent?
Use multiple discovery routes and reconcile their results. Provider inventories have implementation-specific boundaries, while team declarations alone miss unapproved or forgotten deployments.
Combine technical and organizational discovery
- Cloud and platform inventories: export agent, model, endpoint, workflow and workspace records from each approved cloud and developer platform.
- Identity and access records: look for service principals, managed identities, API keys, OAuth applications and unusual grants associated with automation or model access.
- Deployment artifacts: inspect infrastructure-as-code, CI/CD manifests, container registries, software bills of materials, source repositories and configuration-management records.
- Application and data catalogs: search API gateways, plugin catalogs, data-loss-prevention records, integration platforms and logs for agent-like callers.
- Team declarations: require product, engineering, operations and business teams to report agents they built, adopted or connected to company data.
- Reconciliation: merge duplicates, resolve conflicting owners, flag records with no recent activity and investigate active identities that have no corresponding inventory entry.
NIST recommends policies that specify the systems included, the attributes captured and the person or function responsible for maintaining the inventory. Treat employee reporting as a required control, not an optional survey.
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Understand provider-specific limits
AWS announced Security Hub AI inventory on July 14, 2026. AWS says it discovers managed resources through AWS Config, including Amazon Bedrock, Bedrock AgentCore and SageMaker, and identifies supported self-hosted inference endpoints, models and agents through software-bill-of-materials analysis from Amazon Inspector. Those mechanisms are AWS-specific and cover the supported services and assets they can identify; they are not proof that every agent in a company will appear automatically. Reconcile provider findings with organization-wide records, identity data and owner declarations.
| Discovery route | What it can reveal | Typical blind spots |
|---|---|---|
| Cloud-native inventory | Managed agents, models, endpoints, configurations and account or region metadata | Other clouds, SaaS agents, local tools, unsupported resource types and misconfigured accounts |
| Identity and access systems | Principals, grants, keys, service accounts and data scope | Agents using shared credentials, unregistered personal accounts or indirect workflow identities |
| Code, deployment and SBOM records | Self-hosted components, versions, dependencies and deployment locations | Click-to-configure SaaS agents and systems outside managed repositories |
| Team and owner declarations | Business purpose, shadow use, exceptions and context unavailable in technical telemetry | Unreported or forgotten agents and inaccurate self-described permissions |
| Runtime logs and network data | Actual callers, tools, destinations, frequency and behavior | Agents that are dormant, encrypted from inspection or operating outside monitored paths |
What should each AI-agent record contain?
Capture enough information for a security analyst, operator and business approver to understand the agent without opening its source code. AWS registry guidance includes capabilities, permissions, purpose, ownership, version history, dependencies, metrics, usage, approval status and governance classification. NIST also identifies documentation, incident-response plans, data dictionaries, implementation software or source-code links and contact information as useful attributes.
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|---|---|
| Identity and purpose | Stable ID, name, description, intended outcome, business process, creator and accountable owner |
| Location and lifecycle | Platform, cloud account or tenant, region, environment, deployment location, lifecycle state, creation date and planned retirement date |
| Technical identity | Human or service principal, managed identity, credentials, authentication method and associated groups |
| Permissions and reach | Roles, scopes, write or destructive capability, network destinations, data classifications and approved operating boundaries |
| Models and components | Model provider and version, framework, prompts or policies, tools, plugins, APIs, workflows, libraries and other dependencies |
| Change and approval | Version history, change tickets, testing evidence, approver, approval state, risk tier, last review and next review |
| Operations | Usage, latency or performance measures, health checks, monitoring source, alert thresholds, incident route and support owner |
| Retirement evidence | Disablement date, revoked identities and keys, removed integrations, retained records and confirmation that dependent processes were migrated |
Store links to architecture diagrams, data-flow records, threat models, incident plans and source repositories rather than copying every artifact into the registry. Keep an audit trail for field changes so a reviewer can see who changed an owner, permission, model or approval.
How should you classify agent risk?
Do not assign a risk label from the model name alone. Compare the agent’s autonomy, permissions, data sensitivity, potential impact and blast radius. The cited guidance supports risk-based controls and differentiated treatment by criticality, but it does not prescribe one universal numerical formula.
| Dimension | Questions to ask | Higher-risk indicators |
|---|---|---|
| Autonomy | Can it only suggest, or can it execute without approval? | Multi-step planning, unattended execution, self-modification or no human checkpoint |
| Permissions | What can its identity read, change or delete? | Production writes, financial transactions, privileged administration or broad cross-system access |
| Data sensitivity | Which information can it retrieve, remember or transmit? | Personal, health, financial, confidential, regulated or credential data |
| Potential impact | What happens if it is wrong, manipulated or unavailable? | Safety, legal, customer, financial, employment or critical-service consequences |
| Blast radius | How many systems, users or records can one failure affect? | Shared credentials, broad tenant scope, unrestricted tool catalogs or chained agents |
A practical policy can define tiers such as advisory, controlled-action and high-impact autonomous. For each tier, specify required testing, approval authority, human intervention, logging detail, alert response and review frequency. Record the reasons for the classification so a later reviewer can reassess it when permissions or use change.
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What should you monitor in production?
Monitoring only prompts and final answers misses the actions that create risk. The Canadian Centre for Cyber Security recommends observing internal operations as well as inputs and outputs, including prompts, tool calls, memory interactions, decisions, actions, identity and privilege changes, sources and tools used, and anomalous behavior.
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- Request and response identifiers, timestamps, initiating user or service principal and the agent version.
- Prompt and retrieved-context metadata, subject to redaction and access controls.
- Tool or API calls, parameters, destination, result, authorization decision and whether a human approved the action.
- Memory reads and writes, data sources consulted, files created or changed and downstream workflow steps.
- Model, policy, tool and dependency versions active at the time of the event.
- Errors, retries, latency, resource consumption, refusal or policy-violation signals and outcome status.
Watch identity, privilege and supply-chain changes
Alert when an agent gains a role, receives a new tool, changes its data source, switches model or begins using a previously unseen identity or destination. Microsoft guidance emphasizes least privilege, auditable agent identities and inventorying models, tools, plugins and data sources as supply-chain dependencies. A version change should create a review event when it affects behavior, permissions or data handling.
Define response actions before an alert arrives
For each agent, document thresholds that trigger notification, human review, temporary pause, credential revocation, rollback or an incident process. Examples include an unusual volume of records read, a new external destination, repeated failed authorization, prompt-injection indicators, a privilege escalation or an action outside the declared business process. Monitoring is useful only when someone owns the queue and can intervene.
Protect the monitoring data
Agent telemetry may contain prompts, retrieved documents, identifiers, customer information or secrets. Limit access by role, redact or tokenize sensitive fields where possible, encrypt logs, and define retention according to applicable legal, contractual and internal requirements. The available guidance supports monitoring these operations but does not establish one retention period suitable for every organization.
How do you keep the inventory current?
Make registration and review part of the delivery lifecycle, then add event-driven checks for changes that can alter risk.
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- Register before access: create the record before production credentials, sensitive data or write-capable tools are granted.
- Approve the intended design: record the owner, purpose, data flows, permissions, risk tier, tests and approver.
- Monitor continuously: connect runtime telemetry, identity events, deployment changes and incidents to the stable agent ID.
- Review on triggers: require reassessment after a major model or framework update, new tool or data source, permission change, owner departure, abnormal behavior or material business change.
- Conduct periodic attestations: ask the owner to confirm purpose, usage, dependencies, access and contacts; mark unconfirmed records for investigation.
- Retire deliberately: disable deployments, revoke identities and keys, remove integrations, update dependent processes and retain the approval and decommissioning evidence.
Microsoft lifecycle guidance calls for registration, approval, expiration and decommissioning, with deliberate change control and clear ownership. An agent that has not run recently may still retain credentials or appear in downstream workflows, so inactivity alone is not proof that it is safe to delete.
How can you find and handle shadow AI agents?
Shadow agents usually surface when technical evidence and owner reporting are compared rather than when one catalog is searched. Create a non-punitive disclosure path, explain what information teams must provide and make registration faster than bypassing it.
- Search for unapproved model and agent API calls in gateway, DNS, proxy and identity logs.
- Review new OAuth applications, service accounts, keys, plugins and automation workflows.
- Scan repositories, infrastructure code, containers and SBOMs for agent frameworks and tool definitions.
- Compare departmental software, procurement and expense records with the approved registry.
- Contact the apparent owner, restrict risky access while investigating and preserve evidence.
- Register legitimate uses, migrate them to an approved identity and retire duplicates or unsafe deployments.
Do not assume that an unregistered agent is malicious. The immediate objective is to establish ownership, purpose, permissions and data exposure, then apply the same approval and monitoring requirements as any other agent.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you evaluate inventory and monitoring tools?
Compare capabilities against your operating model rather than choosing a catalog because it has the longest feature list. Ask vendors and internal platform teams to demonstrate the following with your own representative agents.
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|---|---|
| Discovery coverage | Does it find managed cloud services, self-hosted workloads, developer platforms, SaaS agents and locally run agents? How are unsupported assets reported? |
| Record quality | Can it store owner, purpose, permissions, dependencies, deployment, versions, approvals, risk and retirement evidence? |
| Identity and access | Can it connect an agent to principals, grants, credentials, data scope and privilege changes? |
| Runtime monitoring | Does it capture tool calls, actions, policy violations, anomalies, human approvals and pause or rollback paths? |
| Governance integration | Can it trigger approvals, change management, incident response, audit exports and decommissioning workflows? |
| Interoperability and effort | Are APIs and exports available? Can it integrate with existing security and IT-service processes, and who maintains connectors and data quality? |
Microsoft describes a centralized registry and control plane for identity, ownership, access, lifecycle, observability and intervention. AWS describes registry fields covering ownership, purpose, permissions, dependencies, versions, approval, usage and performance. These are provider descriptions, not a neutral comparative product test; verify current availability, licensing, regional support and the exact assets each service can discover.
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What are the most common implementation failures?
- One catalog is treated as complete: combine provider, identity, deployment, runtime and owner-based discovery.
- No accountable owner: require a named person, backup and escalation route before approval.
- Inventory fields are descriptive but not actionable: include identities, permissions, dependencies, monitoring sources and stop procedures.
- Monitoring stops at the answer: record tool calls, memory access, decisions, actions and privilege changes.
- Logs create a second data leak: redact sensitive content and restrict telemetry access.
- Risk labels never change: make permissions, tools, model versions, owners and business impact review triggers.
- Retirement is omitted: revoke credentials and integrations and preserve decommissioning evidence.
The durable operating model is simple to state: discover through several routes, register a useful record, assign accountability, monitor behavior and changes, and revisit the record whenever the agent or its environment changes.
Frequently Asked Questions
How often should an AI-agent inventory be reviewed?
Use a combination of periodic owner attestations and event-driven reviews. A new permission, tool, data source, model or owner should trigger review immediately, while the cadence for routine attestations should follow the agent’s risk tier and applicable policy.
Should development and test agents be included?
Include them when they can access company data, credentials or production-connected services. Recording lower-risk prototypes separately from production agents preserves visibility without applying identical controls to every experiment.
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What is the difference between an agent inventory and an AI model inventory?
A model inventory describes models and their versions, while an agent inventory also records identities, tools, permissions, workflows, memory, dependencies and actions. One agent may call several models, and one model may serve many agents, so the records should be linked rather than merged.
How can a company stop an agent quickly during an incident?
Document an intervention path for each production agent before deployment. It should identify the person authorized to pause the agent, the credential or deployment to disable, the downstream integrations to isolate and the rollback or recovery procedure.
Is a spreadsheet sufficient for a small company?
A controlled spreadsheet can be an initial registry if it has stable IDs, access control, change history, required fields, an owner and a review workflow. As the number of agents and integrations grows, connect the record to identity, deployment and monitoring systems so updates are not dependent on manual copying.
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