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To track resources created by AI agents, give every agent a distinct cloud identity, capture both the agent’s own action history and the provider’s control-plane audit events, then join the actor identity to the target resource ID in a central inventory. The agent trace explains what the agent attempted or decided; the cloud audit record establishes what actually happened to a resource. Keeping both records lets you investigate creation, changes, ownership and cleanup across AWS, Azure and Google Cloud.
What you must capture to identify an agent-created resource
A reliable answer to “How can I tell which resources an AI agent created?” requires two evidence streams. First, retain the agent platform’s trace: prompts or tasks, tool calls, approvals, handoffs and the action outcome when those records are available. Second, retain the cloud provider’s control-plane event for the create, update or delete operation. These streams overlap, but they are not interchangeable.
- Agent trace: what the agent was asked to do, what it decided, which tool it called and whether a workflow reported success.
- Cloud audit event: which principal invoked a provider API, which operation ran, which account, project or subscription was affected, which resource identifier was targeted, when it occurred and whether the provider accepted or rejected it.
An agent can claim that it created a database while the provider rejected the request, or it can create a resource through a tool path that is missing from the agent’s application log. Treat the provider event as the authoritative record of infrastructure state and use the agent trace to explain intent and context.
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Do not let an agent share a human administrator’s credentials or a broad service role used by unrelated workloads. Create a dedicated identity for each agent or well-defined agent class, and keep human identities separate. Stable names and metadata make later filtering practical. A useful convention includes the agent name, workload, environment, owner and purpose. AWS Well-Architected guidance gives the concrete tag pattern PrincipalType: Agent and AgentName: <name>; apply an equivalent label or tag scheme in other clouds.
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Identity separation also limits damage. A reviewer can distinguish an agent’s attempted change from a human approval, and a security team can disable one agent without revoking access for every automation job.
Preserve the join keys
Every normalized event should retain the fields needed to connect an actor with a resource:
| Field | Why it matters |
|---|---|
| Provider | Identifies AWS, Azure or Google Cloud and prevents ambiguous resource names. |
| Account, project or subscription | Scopes the event to the correct administrative boundary. |
| Actor principal | Shows the exact role, service account, managed identity or other principal that called the API. |
| Agent metadata | Maps a principal to an agent name, class, workload, owner and environment. |
| Operation or action | Distinguishes Create, Read, Update, Delete and provider-specific API operations. |
| Target resource ID | Uses an ARN, Azure resource ID or Google Cloud full resource name as the stable inventory key. |
| Event time | Supports ordering, time-window joins and investigation of subsequent human changes. |
| Result and error details | Separates successful creation from denied or failed attempts. |
| Raw event payload | Preserves provider-specific fields needed for forensic review. |
Keep the raw event even after extracting these fields. Normalized columns make dashboards fast; the original payload provides evidence when a provider adds a field or a parser makes a mistake.
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How do I track cloud resources created by AI agents?
Use the following sequence for each cloud account, project or subscription. Configure logging before granting the agent production permissions, then test with a controlled resource whose creation and deletion you can verify.
1. Inventory agent identities and permissions
- List every agent, agent class and orchestration component that can call cloud APIs.
- Map each one to a dedicated role, service account or managed identity. Record the account, project or subscription in which that identity is defined.
- Apply stable tags or labels for agent name, workload, environment, owner and purpose. On AWS, include
PrincipalType: AgentandAgentName: <name>where your tagging policy permits. - Document the permissions granted to the identity, including indirect access through assumed roles or delegated service accounts.
- Record the human approver or owning team separately. Ownership is not the same as the actor that performed the API call.
Review this inventory whenever an agent is renamed, redeployed or given a new tool. A stale mapping turns a technically complete audit trail into an attribution problem.
2. Keep the agent’s action history
Enable the agent platform’s action history, tool-call records and approval or handoff events. Capture a correlation value when the platform supplies one, and retain the task start and end times. In Microsoft’s Azure SRE Agent documentation, action history is written to Application Insights and queried from Monitor > Logs with KQL. That history is valuable for reconstructing intent, but it does not prove that Azure Resource Manager accepted every infrastructure change.
For platforms without a durable action log, emit an equivalent application event before and after each cloud tool call. Include the agent identity, requested operation, intended resource, approval state and returned provider request or operation ID when available. Never replace the provider audit stream with this application log.
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3. Capture provider control-plane operations
Enable audit coverage for the APIs that create, update and delete resources, plus the agent service itself. The exact event class matters: a default management trail may omit data events or specialized runtime calls.
- On AWS, retain CloudTrail management events for control-plane activity and explicitly configure advanced event selectors for the relevant Amazon Bedrock agent data events.
- On Azure, use Activity Log for Azure Resource Manager operations on agent-related resources, managed identities and Application Insights resources.
- On Google Cloud, use Cloud Audit Logs and query Admin Activity for configuration changes. Enable Data Access only for services and investigations that need it, because it is generally disabled by default and can increase logging charges.
Include denied calls as well as successful calls. A denied create attempt can reveal privilege escalation, a broken policy or an agent repeatedly choosing an unsafe region.
4. Centralize and normalize the records
Forward agent traces and cloud events to a query surface with a documented retention period. Normalize the fields listed above while retaining provider-specific columns and the raw payload. Preserve timestamps in UTC and record the provider’s event time separately from ingestion time; delayed delivery can otherwise make a resource appear to have been created before the request that caused it.
Join events to a current resource inventory by provider plus account, project or subscription and the canonical resource ID. Use a time window around the event to handle eventual consistency. If a resource is renamed, retain its immutable identifier and store the display-name history as a separate attribute.
5. Build operational views and alerts
Start with views that answer a concrete operational question:
- Which resources did each agent create in the last hour, day or deployment window?
- Which agent-created resources have no owner, environment or expiry metadata?
- Which create attempts were denied, and which policy caused the denial?
- Which resources were created outside approved accounts, projects, subscriptions, regions or environments?
- Which resources received a human-principal change after an agent created them?
- Which agent identities suddenly created more resources than their normal workload?
Alert on the conditions that require a person to act. A dashboard can show every event; an alert should identify an unexpected region, an unowned resource, a privileged operation or a failed cleanup attempt.
6. Protect and retain the evidence
Restrict audit-data access to the investigators and operators who need it. Route records to a protected central destination when a compromised workload could otherwise delete or alter its own evidence. Set retention from your incident-response and compliance requirements, then verify the current storage, ingestion and query charges for each service.
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AWS guidance describes tamper-evident CloudTrail validation, a separate Amazon S3 artifact bucket and analysis through AWS Glue and Amazon Athena. Treat those as design elements to verify in your own account, not as an automatic property of every trail.
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Use the create event to propose an owner and expiry date, or route the resource to a review queue. Require an explicit owner for production resources and a shorter lifetime for experiments. Before deletion, check dependencies, snapshots, data-retention obligations and any human handoff recorded after creation. “Created by an agent” is a useful review signal, not sufficient authority for automatic deletion.
How do I audit AI agent actions in AWS?
Separate Bedrock runtime data events from management events
CloudTrail logs Amazon Bedrock Agents for Amazon Bedrock Runtime API operations, including InvokeAgent and InvokeInlineAgent, as data events. Data events are not captured merely because management events are enabled. Configure advanced event selectors for the resource types used by your agents, such as AWS::Bedrock::AgentAlias or AWS::Bedrock::InlineAgent. Data-event capture can add charges, so select only the accounts, regions and resources required for your investigation and governance goals.
Management events remain important because the agent or a deployment pipeline may create or modify IAM roles, Lambda functions, networking, storage or other infrastructure through ordinary AWS APIs. Keep those records and correlate them with the Bedrock runtime event and the agent’s application trace.
Use role metadata that survives the audit path
Give each agent a dedicated service identity and tag the role or session consistently. Keep human operators on separate roles. When an agent assumes a role, retain both the originating principal and the assumed-role session details so an investigation can distinguish the orchestrator from the effective API caller.
Query a centralized CloudTrail lake
A documented AWS pattern delivers CloudTrail records to Amazon S3, catalogs them with AWS Glue and queries them with Amazon Athena. In that query surface, filter by the agent principal, operation, account, region and target ARN, then join the result to your resource inventory. Keep the original CloudTrail record for evidence and use a normalized view for dashboards.
How do I audit AI agent actions in Azure?
Use Application Insights for intent and Activity Log for resource state
Azure SRE Agent action history is recorded in Application Insights and can be queried with KQL from Monitor > Logs. Use that history to see the agent’s task, tool calls and reported result.
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Use Azure Activity Log for Azure Resource Manager operations affecting the agent’s resources, managed identities and Application Insights resources. Activity Log tells you which principal performed the operation and what Azure resource ID was changed. Join it to the Application Insights record by time, principal and any shared operation or correlation identifier.
Centralize cross-cloud records carefully
Azure Monitor’s CloudAuditEvents schema can hold cross-cloud audit data. Its documented fields include ActionType values such as Create, Read, Update and Delete; Azure, AWS and Google Cloud resource identifiers; DataSource; RawEventData; and TimeGenerated in UTC. Confirm that the relevant workspace ingestion rules and source connectors are enabled before treating this table as a complete inventory. A schema can accept an event without guaranteeing that every provider service is sending its events.
How do I audit AI agent actions in Google Cloud?
Query Admin Activity for configuration changes
Google Cloud audit entries identify the owner scope, target resource, timestamp, service and audit payload. For Agent Platform records, the service name is aiplatform.googleapis.com and the resource type is audited_resource. Admin Activity logs record user-driven API calls and other actions that change resource configuration or metadata, and Google Cloud writes them automatically.
Use Logs Explorer, gcloud or the Logging API to filter by the agent’s service account, operation and target resource. Preserve the project and full resource name because display names are not globally unique.
Enable Data Access deliberately
Data Access logs cover access to resource configuration or metadata and to user-provided data. They are generally disabled by default except for BigQuery. Enable them only for the services and questions that require that visibility, and account for increased logging charges and volume. Admin Activity coverage alone is not proof that every read or data access event is present.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to join agent traces with cloud events
The join should be deterministic whenever possible and explicitly qualified when it is not. Use the cloud provider’s canonical identifier as the primary key and the actor principal plus UTC time as supporting keys.
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- Map the agent name in the application trace to its dedicated role, service account or managed identity.
- Find provider events for that principal in a narrow window around the tool call.
- Match the operation and target ARN, Azure resource ID or Google Cloud full resource name.
- Verify the account, project or subscription and region or location.
- Record the outcome from the provider event, including denial or error details.
- Link later updates and deletes to the same resource ID, even if its display name changes.
If no provider event matches, do not label the resource as created. Mark the trace as an attempted or unverified action and investigate missing selectors, delayed delivery, an intermediate identity or an out-of-band tool.
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Comparison: native provider logs and a centralized inventory
| Approach | Strength | Limitation to plan for | Best use |
|---|---|---|---|
| Provider-native audit logs | Authoritative control-plane record with provider principal and resource identifiers | Event categories, defaults and charges differ by provider and service | Proving that a resource operation occurred |
| Agent action history | Shows intent, tool calls, approvals and handoffs | May not include every provider-side change or rejected request | Explaining why an agent attempted an operation |
| Central normalized inventory | Cross-account and cross-cloud views, ownership and expiry workflows | Depends on complete ingestion, correct parsing and current inventory data | Searching, alerting and routing cleanup work |
| Security or posture workflow | Can combine event filters with review queues and policy alerts | Coverage and retention depend on the connected sources and configuration | Operational response after a resource is identified |
Evaluate any central tool against provider and service coverage, actor attribution, stable resource correlation, capture defaults, query and alerting, retention and access controls, cost, and the ability to assign ownership without unsafe deletion.
Common gaps and how to fix them
“The agent log says it created a resource, but I cannot find it.”
Check whether the provider rejected the call, whether the agent used a second identity, and whether the resource was created in another account, project, subscription or region. Then verify that the relevant management or data-event selector was enabled at the time of the call.
“I see the resource, but the actor is a shared role.”
Separate the role for future runs and add agent metadata. For historical events, correlate session names, orchestration logs, deployment records and timestamps, but label the attribution as reconstructed rather than direct when the provider event cannot distinguish agents.
“Events arrive out of order.”
Store both event time and ingestion time, allow a bounded delay in joins and reconcile against the provider inventory after ingestion. Do not infer causality from dashboard arrival order.
“Logging costs or volume are growing quickly.”
Review data-event and Data Access selectors, high-volume read operations and retention tiers. Keep create, update, delete and denial coverage as the minimum governance set, and enable broader access logging only for a defined investigation or compliance need.
“Cleanup removed something still in use.”
Require an owner and expiry review, inspect dependencies and snapshots, and check for a human change after agent creation. Route uncertain candidates to a person instead of deleting solely because a resource is old or agent-created.
An operating rule for agent-created infrastructure
Every agent should have a distinct identity, every resource-changing API should produce a retained provider event, and every event should be joinable to a canonical resource identifier. The agent trace answers what the automation intended; the cloud control-plane record answers what the platform accepted. Build dashboards and cleanup queues from that joined evidence, protect the logs from tampering, and verify provider defaults and charges whenever a service or logging configuration changes.
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