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Monitor an AI agent by tracing the complete path from the user’s task to model calls, retrieved content, tool requests, authorization decisions, approvals, and downstream results. Evaluate those events against explicit policies, then pause, block, revoke, or escalate actions that exceed the agent’s permitted scope. Monitoring supplies evidence and early warning; authorization and approval controls enforce the boundary.
This matters because an agent can finish a software operation successfully while still making an unauthorized change, exposing data, following a malicious instruction in retrieved content, or exercising more autonomy than intended. The OWASP GenAI Security Project’s guidance is explicit: Implement authorization in downstream systems rather than relying on an LLM to decide if an action is allowed or not.
(OWASP LLM06:2025 Excessive Agency guidance)
What an effective monitoring design must show
A useful record lets an investigator reconstruct one run without guessing. Give every run a stable trace ID and correlate nested events for the initiating request, agent identity, model invocation, retrieval, tool call, downstream request, approval, and result. If agents delegate to one another, retain both the delegating and receiving agent identities. The OWASP Agent Observability Standard describes tracing actions back to their reasoning and originating task, while NIST’s agent evaluation-probe project describes machine-readable audit trails that map decisions to supporting evidence.
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Minimum event relationships
- Task: the user or service request, tenant or workspace, timestamp, and trace ID.
- Identity: the human principal, agent name and version, delegated-agent relationship, and service account used for execution.
- Decision: model/provider and version, retrieved evidence references, policy version, authorization outcome, and any risk score.
- Action: tool or API name, operation type, target resource, parameters after secret redaction, and whether the call was simulated, queued, approved, blocked, or executed.
- Outcome: downstream status, returned result, side effects confirmed by the system of record, and operator disposition.
OpenTelemetry-compatible instrumentation can reduce custom plumbing. The OWASP observability project says its agent-specific work extends OpenTelemetry and OCSF, but the overview and trace pages identify the specifications as working drafts. Treat that status as a project qualification, not as evidence of a finalized standard or universal adoption. See the OWASP Agent Observability trace specification.
Instrument the agent and its boundaries
Start at orchestration boundaries rather than trying to infer behavior from final text. A span should begin when a task enters the agent service and continue through planning, retrieval, each model call, every tool request, authorization, approval, execution, and completion. Record failures and cancellations as first-class outcomes; a missing result can otherwise look like a successful stop.
- Create the root trace. Assign a non-sensitive trace ID, task ID, tenant or application context, and start time. Link the authenticated human or service principal to the run.
- Instrument model calls. Record the model and provider identifier, configuration or policy profile, input and output token counts when available, latency, and finish reason. Redact or hash sensitive content before it enters centralized logs.
- Instrument retrieval. Record the corpus or index, query identifier, document IDs or stable references, access decision, and the evidence supplied to the model. Do not copy an entire confidential document into an alert by default.
- Instrument tools and downstream requests. Log the tool name, operation class, target resource, requested parameters after redaction, caller identity, and the exact downstream response. Keep a link to the system-of-record transaction or change ID when one exists.
- Instrument delegation. When one agent starts another, create a child span containing the parent agent, delegated purpose, inherited permissions, and resulting child trace ID. This prevents a dangerous action from appearing to originate from an anonymous worker.
- Instrument controls. Emit an event when policy evaluation, authorization, rate limiting, sandboxing, simulation, or human approval occurs. Include the policy version and decision reason, not merely a Boolean success value.
An illustrative action record
{
"trace_id": "non-sensitive identifier",
"task_id": "request identifier",
"principal": "authenticated user or service",
"agent": "agent-name@version",
"action": {"tool": "tool-name", "operation": "operation-class", "resource": "resource-reference"},
"authorization": {"policy_version": "version", "outcome": "allow|deny|approval-required"},
"approval_id": "identifier or null",
"execution": {"state": "simulated|queued|executed|blocked", "result_ref": "downstream record"}
}The field names are an implementation example, not a required vendor schema. The important property is that an operator can connect identity, intent, permission, approval, and observed side effect in one query.
What to detect: behavior that is risky even when no error occurs
Traditional application monitoring focuses on latency, exceptions, and availability. Agent monitoring must also evaluate whether the action was appropriate. OWASP identifies prompt injection (including instructions hidden in external content) and excessive agency as major agent risks. Translate those risks into rules tied to the tools and data your agent can actually reach; there is no universal threshold that is safe for every deployment.
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Prompt-injection indicators
- Retrieved text or a web page attempts to override the system or developer policy, requests secrets, or instructs the agent to ignore its task.
- A tool argument contains instructions unrelated to the user’s request, such as sending data to a new destination or creating a second credential.
- The agent’s plan changes from a read operation to an external write immediately after consuming untrusted content.
Tag the source of each instruction (system policy, user, retrieved document, tool output, or another agent). A source tag lets a detector distinguish ordinary user intent from an instruction injected through data.
Excessive autonomy and scope drift
- The agent requests a tool, resource, privilege, or geographic destination outside its declared allowlist.
- A read-only task attempts a write, deletion, purchase, message, permission change, or code deployment.
- The agent creates additional agents, credentials, scheduled jobs, or long-running loops without an explicit policy permitting that behavior.
- The number, cost, or repetition of calls exceeds a task-specific budget or a rate limit.
These rules should be evaluated before execution whenever possible. A post-action alert can support investigation, but it cannot undo a transfer, deletion, or disclosure reliably.
Anomalous sequences and destinations
Baseline normal sequences for each workflow: for example, retrieve an account record, validate an authorization, then update that same record. Alert on unusual ordering, repeated denials, rapid retries, a new domain or account, a sudden increase in data volume, or a destination never previously approved for that tenant. Treat a baseline as a signal for review, not proof of compromise; legitimate incidents and new business processes can look anomalous.
Grounding and factual support
NIST’s evaluation-probe work describes checking an agent’s factual grounding against a human-curated reference corpus and accumulating the results in a machine-readable audit trail (NIST evaluation probes). Grounding checks can reveal unsupported claims or incorrect evidence selection, but a grounded answer is not automatically authorized to perform an action. Pair grounding with identity, permission, destination, and impact checks.
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Turn detections into enforceable response paths
Define what happens for each policy result before deployment. An alert that only lands in a dashboard is inadequate for a high-impact operation.
| Condition | Recommended control | Operator evidence |
|---|---|---|
| Low-impact, allowlisted read | Allow and record the span; apply normal rate and data-volume limits. | Trace ID, resource, policy version, result. |
| Untrusted instruction or scope mismatch | Block the tool call or pause the run; preserve the triggering source reference. | Rule ID, source type, attempted operation, blocked state. |
| High-impact write, deletion, transfer, or permission change | Require an approval gate before execution and re-authorize in the downstream service. | Approver identity, approval ID, expiry, exact action parameters. |
| Repeated denials, runaway loop, or unusual destination | Throttle or terminate the run, revoke temporary credentials, and open an incident. | Sequence of attempts, budget consumed, credential and session state. |
Approval must cover the concrete action, target, and parameters, not a vague statement that the agent may “continue.” Expire approvals and prevent the agent from changing the approved payload after approval. For especially consequential operations, use a two-person review or an out-of-band confirmation channel according to your organization’s risk policy.
Put authorization in the downstream API, database, ticketing system, or cloud control plane as well as in the agent orchestrator. The model should never be the sole authority on whether it may perform an operation. This is the central control in OWASP’s excessive-agency guidance.
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Design alerts for action
Every alert should identify the affected run, attempted action, resource, policy that fired, and current execution state. Route urgent alerts to the team that can pause or revoke the relevant account; send lower-severity policy drift to the owner for review. Include links to the trace and downstream transaction, while keeping sensitive prompt text out of broad notification channels.
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Protect the monitoring data itself
Logs become a high-value target because they can combine user identity, business records, prompts, tool parameters, and model output. Apply the same or stronger controls used for the systems the agent accesses.
- Never record API keys, passwords, session cookies, private keys, or bearer tokens. Redact before serialization, not only in the dashboard.
- Minimize prompt and response retention; store hashes, document IDs, classifications, or secure references when full content is unnecessary.
- Separate operational access from investigative access, and audit who views sensitive traces.
- Encrypt data in transit and at rest, define deletion and legal-hold procedures, and document retention by data class.
- Ensure tenants cannot query one another’s traces, including through aggregate dashboards and exported files.
These practices align with OWASP’s recommendations for structured metadata on high-risk actions and for avoiding secrets or unnecessary sensitive content in logs (OWASP Agent Security Cheat Sheet; OWASP prompt-injection guidance).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate the monitor and controls continuously
Monitoring is itself a security control that can regress. Build a test set containing normal tasks, known prompt-injection patterns, unauthorized resource requests, tool failures, approval bypass attempts, and plausible anomalous sequences. Run the set in a sandbox and verify that traces contain the fields needed to explain each decision.
- Test detection coverage. Confirm that each risky scenario produces the intended event, policy result, alert, and execution state.
- Test enforcement. Attempt to bypass the orchestrator by calling the downstream service directly, changing a tool parameter after approval, or replaying an old approval. The downstream control should still deny unauthorized work.
- Measure alert quality. Have reviewers label true and false positives and record missed cases. Adjust rules, allowlists, and workflows with a versioned change record.
- Retest after material changes. Re-run security tests when prompts, tools, memory, retrieval sources, policies, model providers, or delegation logic change. OWASP specifically calls out these changes as reasons for renewed testing (OWASP Agent Security Cheat Sheet).
- Review real or safely simulated traces. Human review helps reveal behavior that a static rule missed, especially when an agent completes the nominal task but violates data-minimization or business-process expectations.
NIST announced its AI Agent Standards Initiative on February 17, 2026, and its evaluation-probe work emphasizes structured evidence and auditability rather than a single universal safety score. Do not treat a grounding score or an absence of alerts as proof that an agent is safe (NIST announcement).
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Choosing an observability platform
A platform can simplify tracing and review, but it does not replace authorization, sandboxing, or approval gates. Compare candidates against your actual agent framework and data-handling requirements.
| Option | Documented capabilities | Questions to verify |
|---|---|---|
| LangSmith | Agent tracing, tool and trajectory monitoring, online evaluations, cost tracking, webhook or PagerDuty alerts, support for popular agent frameworks and OpenTelemetry, and SDKs for Python, TypeScript, Go, and Java. | Can it capture delegated agents and downstream authorization events in your architecture? What retention, redaction, residency, and access controls apply to your deployment? |
| Langfuse | Tracing for model and agent activity, including tool calls and retrieval steps, OpenTelemetry-based instrumentation, and cloud or self-hosted deployment options. See its SDK overview and integration list. | Does the chosen deployment fit your data-residency and retention policy? Can reviewers annotate traces and connect alerts to your incident process, and can controls pause actions before execution? |
Product features, integrations, deployment terms, and project status can change. Validate current documentation and run a representative trace through each candidate before committing. The deciding criteria are complete tool-level coverage, secure data handling, useful evaluation and review workflows, and the ability to connect a detection to a real blocking or approval mechanism.
A practical rollout sequence
- Inventory authority. List every tool, resource, write capability, credential, delegated agent, and external destination available to the agent.
- Classify impact. Mark reads, writes, deletions, transfers, financial actions, permission changes, and code execution; assign approval requirements to the high-impact classes.
- Define policy. Create allowlists, budgets, destination rules, data limits, and pause or revoke procedures. Version the policy.
- Add trace coverage. Instrument task, model, retrieval, delegation, tool, authorization, approval, and downstream result events. Verify correlation with a trace viewer or query.
- Implement downstream enforcement. Pass the authenticated principal and action context to the service that owns the resource, and deny requests that lack permission regardless of the model’s explanation.
- Build detection and response. Start with prompt-injection indicators, scope mismatches, high-impact actions, repeated failures, and unusual destinations. Connect each rule to block, pause, approval, throttle, or incident handling.
- Exercise failure modes. Use adversarial and normal scenarios, inspect false positives and misses, and retest after every material system change.
The result is a defensible chain of evidence: who initiated the work, what the agent observed and attempted, which policy and authorization applied, whether a human approved it, and what the downstream system actually did.
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