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What agent monitoring is—and what it is not
An AI agent is a model-driven workflow that can choose steps, call tools, retrieve data, update state, and sometimes act in external systems. Monitoring therefore has two subjects: the agent’s internal execution and the effects of its actions. A transcript of prompts and answers is useful evidence, but it cannot show whether an account was changed, a file was written, a payment API was called, or an approval was bypassed.
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Use monitoring to answer five questions for every important operation:
- Who or what initiated the task, and under whose authority?
- What did the agent decide to do, and which policy or model version produced that decision?
- Which tool or downstream service was called with what effective identity and scope?
- What authorization, approval, and policy checks occurred before execution?
- What changed, when did it change, and can the action be reversed?
Monitoring is a detection, investigation, and damage-limitation control. OWASP’s LLM06:2025 Excessive Agency guidance says logging and rate limiting can help identify or limit undesirable downstream actions, but do not prevent excessive agency by themselves. OWASP describes the underlying risk as “the vulnerability that enables damaging actions to be performed in response to unexpected, ambiguous or manipulated outputs from an LLM.” Treat that statement as a reason to place controls at the action boundary, not as a reason to trust anomaly alerts alone.
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Build a monitoring architecture around the action path
Instrument the full path from request to effect. A practical design has four correlated layers:
1. Request and identity layer
Record the user, service, tenant, application, network or device context, authentication method, and the task or workflow requested. Preserve a stable task_id and session_id across model calls, retrieval, tool execution, retries, and asynchronous jobs. If an agent delegates to another agent, add a parent_task_id or delegation edge so investigators can reconstruct the chain.
2. Decision and model layer
Log the model or agent version, system-policy version, relevant configuration, decision type, confidence or risk score when your implementation produces one, and the reason or structured rationale that is safe to retain. Store the exact instruction or policy identifier used for a high-risk decision. Do not assume free-form chain-of-thought is required; a structured decision record such as “requested refund, amount 480, policy rule R-17 matched” is usually more useful and less sensitive.
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3. Tool and authorization layer
For each tool invocation, capture the tool name and version, operation, normalized arguments, data sources, effective principal, requested scopes, authorization decision, approval identifier, policy decision, start and end times, retries, and result status. Record both the agent’s requested action and the action actually executed after validation. This distinction exposes scope expansion, argument rewriting, and confused-deputy failures.
4. Effect and state layer
Correlate the invocation with downstream audit logs: file writes, database mutations, messages sent, API calls, account changes, transactions, knowledge-base updates, memory writes, and external notifications. Include before-and-after identifiers or hashes where feasible, not a copy of sensitive payloads by default. Errors, exceptions, timeouts, circuit-breaker trips, and compensating actions belong in the same event stream.
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Define an event schema investigators can use
A consistent schema lets a security analyst pivot from an alert to the complete sequence. The following fields are a minimum for security-relevant events; mark fields as required or optional in your implementation.
| Field group | Examples | Why it matters |
|---|---|---|
| Correlation | event_id, task_id, session_id, parent_task_id, trace/span ID |
Reconstructs a workflow instead of isolated messages. |
| Time | UTC timestamp, duration, queue delay, retry number | Establishes order, detects bursts, and separates a slow dependency from an agent loop. |
| Actor and context | User, tenant, application, service account, IP or device context, workflow name | Tests whether the actor and environment are authorized for the task. |
| Model and policy | Model ID and version, agent build, system-policy version, tool registry version | Links behavior changes to deployments or policy changes. |
| Decision | Action class, risk score, selected plan step, refusal or escalation reason | Shows what the agent intended before execution. |
| Tool call | Tool, operation, normalized arguments, requested scopes, effective principal | Reveals privilege use, argument manipulation, and unusual tool sequences. |
| Controls | Authorization result, policy result, approval ID, approver, approval expiry, decision timestamp | Proves whether a high-risk action was independently checked. |
| Outcome | Success or failure, downstream request ID, changed-object ID, error class, rollback result | Connects intent to the real-world effect and recovery status. |
| Data handling | Payload classification, redaction status, content hash, retention class | Supports privacy controls and evidence integrity without indiscriminate content collection. |
For high-risk actions, retain the action classification, applicable risk score, authorization result, approval identifier, execution result, and policy version. The OWASP AI Agent Security Cheat Sheet recommends this level of auditability.
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Instrument the workflow step by step
- List every capability. Create an inventory of model endpoints, retrieval stores, memory stores, plugins, APIs, command runners, queues, and human-approval paths. For each capability, document the data it can read, the state it can change, and whether the effect is reversible.
- Classify actions by impact. Separate read-only retrieval, internal updates, external communication, financial or administrative changes, destructive operations, and actions that expose regulated or secret data. The classification should be stable enough to drive policy and alert thresholds.
- Add correlation at ingress. Generate a task and session identifier when work enters the system. Propagate it through every model call, tool wrapper, queue message, and downstream request. Reject or quarantine events that arrive without required correlation fields.
- Wrap every tool. Use a common middleware layer that records the requested operation, validates arguments, checks authorization independently of the model, emits a pre-execution event, executes only the approved operation, and emits a post-execution event.
- Record state transitions. Emit events for memory writes, knowledge-base updates, plan changes, approval changes, retries, and cancellations. A malicious or faulty run can hide in state changes even when its final response looks harmless.
- Join downstream evidence. Pass a trace or request identifier to databases, cloud services, payment systems, ticketing systems, and messaging systems that support it. Where propagation is impossible, join on a short-lived operation ID and timestamp window.
- Protect the event pipeline. Send security events to an append-oriented store with access controls, time synchronization, integrity checks, and a retention policy. Keep alerting and investigation access separate from the agent’s own credentials.
- Test the records. Execute normal, denied, approved, timed-out, retried, and partially completed tasks. Verify that an analyst can determine who requested the task, what was attempted, what was authorized, what happened, and what remains to be remediated.
Establish behavioral baselines before writing anomaly rules
An anomaly is a departure from an expected workflow, not automatically fraud or compromise. Baselines should be segmented by agent, workflow, tenant, user role, time window, and deployment version. A customer-support agent may legitimately call a ticket API hundreds of times but should not suddenly write to a payroll system.
| Signal | What to measure | Investigation questions |
|---|---|---|
| Tool-call volume | Calls per task, per minute, and per tool; retries and parallel branches | Is there a loop, denial storm, scraping pattern, or legitimate batch? |
| High-risk action rate | Count and proportion of financial, destructive, administrative, or external actions | Did a deployment, user, or prompt cause a sudden increase? |
| Approval behavior | Approval requests, denials, expiry, repeated resubmission, and bypass attempts | Was the exact action approved, or did arguments change afterward? |
| Privilege use | Effective roles, scopes, elevation events, and cross-tenant access | Does the principal normally have this scope and target? |
| Runtime effects | Unexpected file writes, API calls, data exports, state changes, or outbound messages | Was this effect in the tested workflow and change record? |
| Sequence shape | Tool order, branching depth, duration, and unplanned tools | Did the agent skip a required check or take a new path? |
| Input and output risk | Prompt-injection indicators, secret requests, sensitive-data movement, policy-violating output | Did untrusted content alter the plan or redirect data? |
OWASP guidance names approval drift or bypass attempts, elevated-privilege use, abnormal tool-call frequency, and sudden increases in high-risk actions as useful indicators. The NIST IR 8596 initial preliminary draft, dated December 2025, gives unexpected file writes and API calls as examples of anomalous runtime behavior. That NIST document is a preliminary draft, not a final requirement.
Monitor misuse and possible fraud as behavior patterns
“Agent fraud” is not a single standardized monitoring category in the cited guidance. Treat fraud as a possible downstream impact of misuse, manipulation, account compromise, or excessive agency. Detection should focus on observable combinations of identity, intent, authorization, and effect.
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Approval manipulation
Alert when the agent repeatedly resubmits a denied action, changes amount or recipient after approval, routes around an approval service, or requests approval for a low-risk description while executing a higher-risk operation. Bind approval to a canonical action digest containing the target, amount, scope, and expiry; reject execution when any material field differs.
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Investigate a service account using an elevated role outside its normal workflow, a user’s task causing cross-tenant access, or a tool call made with a broader scope than the request. Compare the effective principal and scope in the tool event with the identity and authorization decision at ingress.
Transaction and account anomalies
For agents that create orders, refunds, transfers, credits, or account changes, correlate agent events with business controls such as amount limits, velocity limits, recipient history, and segregation of duties. A spike may be a legitimate campaign or a compromised workflow; the alert is a prompt for validation, not proof.
Prompt-injection-driven activity
Track when retrieved or user-supplied content causes a new tool, data source, or privilege to appear in the plan. Preserve the source document or content identifier, the resulting decision, and the policy check. Do not treat every suspicious phrase as an incident; prioritize cases where untrusted content precedes a policy-relevant action.
Data exfiltration signals
Look for unusual retrieval breadth, large result volumes, secret or personal-data access, unexpected outbound destinations, and transformations that combine data from unrelated tenants. Redact or hash content in the security stream where full payloads are not necessary, while retaining enough metadata to establish what class of data moved.
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Put independent controls at the action boundary
The model should propose; a separate enforcement layer should decide whether an operation may execute. Minimize each tool’s functionality and permissions, and expose narrowly scoped operations instead of a general-purpose shell or unrestricted database connection.
- Independent authorization: Re-evaluate the user, tenant, resource, operation, and current context immediately before execution. Do not rely on a permission claim emitted by the model.
- Least privilege: Give each agent and tool only the scopes required for its workflow. Use short-lived credentials and separate read and write identities where practical.
- Explicit approval: Require a human or approved control for destructive, financial, administrative, externally visible, or difficult-to-reverse actions.
- Exact-action binding: Bind approval to the canonical target, arguments, amount, scope, and expiry. A changed argument requires a new decision.
- Rate and budget limits: Cap calls, spend, records changed, recursion depth, and execution time per task, user, and tenant. Rate limiting creates time to detect activity but is not a complete prevention mechanism.
- Fail closed: Deny execution when policy lookup, approval validation, authorization, or required audit logging fails. Do not silently continue because the model is waiting.
- Separation of decision and execution: Keep planning and tool execution in separate components or trust domains so a prompt cannot directly grant itself authority.
Design alerts that lead to decisions
Every alert should state the signal, affected identity and resources, confidence or rule basis, and the action an operator can take. Avoid a single “anomalous agent” score with no evidence.
| Severity | Typical condition | Immediate response |
|---|---|---|
| Critical | Unauthorized destructive or financial effect, secret exposure, cross-tenant access, or approval mismatch on an executed action | Pause or revoke the agent’s credentials, preserve evidence, stop downstream processing, and start incident response. |
| High | Repeated approval bypass, privilege escalation, unexpected external API or file-write sequence, or large sensitive-data export attempt | Quarantine the task, require human review, and inspect adjacent sessions and deployments. |
| Medium | Abnormal tool volume, new sequence, elevated retry rate, or policy refusal followed by a similar request | Throttle or challenge the task and compare it with the workflow baseline. |
| Low | Small deviation with no sensitive effect and a plausible operational explanation | Record for tuning and trend analysis; do not page by default. |
Use compound rules where possible. For example, “new tool plus elevated privilege plus outbound transfer within one task” is more actionable than any one condition alone. Include an allowlist for approved deployments and scheduled batch jobs, but expire exceptions and review them when the model, policy, or tool version changes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Connect alerts to an incident-response runbook
Monitoring is valuable only when an organization can act on the evidence. OWASP AI Exchange describes monitoring as supporting real-time interception and retrospective analysis, with predefined response actions. Define those actions before the first alert.
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- Preserve evidence. Snapshot correlated agent, identity, tool, authorization, approval, and downstream logs. Record hashes or immutable references where your logging system supports them.
- Contain. Pause the task, revoke or narrow credentials, disable the affected tool, or place the workflow in approval-only mode. Choose the least disruptive containment that stops further impact.
- Scope. Search by user, session, task, model version, policy version, tool, target resource, and time window. Include retries, delegated tasks, and parallel branches.
- Validate authorization. Determine whether the action was requested, correctly authorized, explicitly approved, and executed exactly as approved. A valid user request does not excuse a policy or scope failure.
- Assess effects. Identify changed records, sent messages, exposed data, financial consequences, and downstream automations. Mark which effects are reversible.
- Recover. Roll back or compensate where safe, rotate exposed credentials, restore affected state, and notify owners under applicable incident and privacy procedures.
- Learn. Add a regression test for the observed sequence, tighten the tool or policy boundary, tune the alert, and document whether the root cause was model behavior, prompt injection, identity misuse, code, configuration, or an external dependency.
Protect the monitoring data
Agent logs can contain prompts, retrieved documents, personal information, credentials, proprietary code, and customer records. The Singapore CSA Agentic AI Addendum advises considering applicable privacy regulations and guidelines when logging inputs.
- Collect the minimum content needed for security, debugging, compliance, or an identified investigation purpose.
- Prefer structured fields, identifiers, hashes, redaction, and sampled payloads over unrestricted prompt and output retention.
- Classify logs by sensitivity and restrict access through separate investigator roles, strong authentication, and audited queries.
- Encrypt data in transit and at rest, protect keys separately, and prevent the agent from deleting or rewriting its own evidence.
- Set retention by purpose and jurisdiction; apply legal holds when an incident requires preservation.
- Document when sensitive payload capture is enabled, who can enable it, and how it is disabled after the investigation.
Additional checks for agents using MCP components
Not every agent uses the Model Context Protocol (MCP). If yours does, include MCP-specific controls in the same monitoring design. The OWASP MCP Top 10 identifies risks including token mismanagement and secret exposure, privilege escalation through scope creep, and supply-chain or dependency tampering. Log server identity and version, tool and resource declarations, granted scopes, token issuance and use, unexpected scope changes, dependency updates, and the provenance of a server or connector. Alert when a server requests a broader scope than its registered contract or when a connector changes without an approved deployment.
Choose implementation tools by capability, not brand
The Singapore CSA addendum lists MLflow, Databricks, OpenLLMetry/Traceloop, Helicone, Langfuse, LangSmith/LangChain, Azure Agent Monitoring, and AWS Bedrock Trace Events as examples of monitoring or tracing tools. This is not a comparative ranking. Verify current features, integration requirements, privacy handling, retention, and cost before adopting any option.
| Evaluation axis | Questions to answer |
|---|---|
| Agent and tool coverage | Does it capture model calls, retrieval, tool arguments, state changes, approvals, errors, and downstream outcomes? |
| Correlation | Can analysts pivot by user, tenant, session, task, workflow, model, policy, and deployment? |
| Detection | Can you define rate, sequence, privilege, approval, and high-risk-action rules, and send alerts to existing response systems? |
| Identity integration | Does it preserve effective principals and connect to your authorization, secrets, and audit systems? |
| Evidence controls | Are export, immutability, retention, redaction, access review, and legal hold supported? |
| Privacy | Where are prompts and outputs stored, who can view them, and can sensitive fields be excluded or masked? |
| Operations | What instrumentation changes, sampling limits, latency, maintenance, and failure behavior are required? |
| Cost and lock-in | How are events, payload volume, seats, retention, and exports priced, and can data be moved if the tool changes? |
A tracing product can improve visibility, but it does not automatically enforce authorization or make an alert actionable. Keep the independent policy and execution boundary even when an observability platform supplies dashboards.
A practical rollout plan
First phase: make high-impact actions visible
Inventory tools and identities, classify actions, add task and session correlation, and instrument pre- and post-execution events for financial, destructive, administrative, sensitive-data, and external-communication operations. Start with immutable authorization and approval records.
Second phase: make dangerous behavior interruptible
Add independent authorization, exact-action approval binding, rate and budget limits, credential revocation, and a kill switch or approval-only mode. Define critical and high-severity response playbooks and test them with synthetic tasks.
Third phase: tune detection
Collect a representative baseline across normal users, tenants, time periods, and deployments. Add rules for tool volume, sequence changes, privilege, approval drift, unexpected effects, and sensitive-data movement. Review false positives with workflow owners rather than simply raising thresholds.
Fourth phase: test continuously
Run regression scenarios for prompt injection, denied actions, changed approval arguments, expired approvals, policy-service outages, tool compromise, retries, delegation, and partial failure. Verify that the system fails closed where required and that the response team can recover affected state.
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- Only logging text: Add tool, authorization, approval, state-change, and downstream audit events.
- No shared identifiers: Propagate task, session, and trace IDs through queues and delegated agents.
- Alerting on volume alone: Combine volume with identity, privilege, target, sequence, and effect.
- Trusting model-supplied permissions: Re-authorize at the execution boundary with a separate policy service.
- Approving a plan instead of an action: Bind approval to canonical arguments and reject changes.
- Capturing every payload forever: Minimize, redact, classify, restrict, and retain according to purpose and applicable privacy rules.
- No response owner: Assign an on-call team, containment authority, evidence procedure, and recovery path for each alert severity.
- Ignoring version changes: Include model, agent, policy, tool, connector, and dependency versions in events and revisit baselines after deployment.
What success looks like
An effective program lets an investigator move from an alert to a defensible timeline: the requesting identity and task, the model and policy versions, the plan and tool calls, the independent authorization and approval results, the exact downstream effects, and the containment or rollback status. It also limits what the agent can do while the investigation is underway. That combination—observable behavior, bounded permissions, independent checks, and practiced response—is what makes monitoring useful against misuse, possible fraud, and unexpected activity.
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