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Monitor the agent’s executed actions, not just the text it generates. Capture every model-to-tool request and downstream side effect with a stable run ID, user and agent identity, permission scope, destination, arguments, timestamp, result, error, approval decision, and state change. Correlate those records across retrieval, reasoning, tool calls, memory, and external systems, then compare the sequence with the user’s task and your policy.
Detection is only one layer. Enforce least privilege, tool and destination allowlists, downstream authorization, rate limits, and human approval for high-impact operations. When behavior deviates, your monitoring system should be able to pause the run, revoke credentials, quarantine the agent, preserve an audit trail, and support a review of what happened.
Define what you are monitoring
An agent workflow is broader than a chat transcript. A useful monitoring boundary follows data and control from the initial request through model calls, planning, memory, retrieval, tool or MCP calls, delegated agents, and the systems that ultimately read or change data. The Government of Singapore’s Securing Agentic AI addendum describes a scope that includes models, databases and files, memory, agents, tools, MCP interactions, agent-to-agent communication, external actions, and relevant software or hardware systems.
Start by drawing that path for each production workflow. Mark every point where an identity is established, a permission is checked, untrusted content enters, or an external effect occurs. A call to read a document is different from a call that sends a message, changes a record, executes a shell command, grants access, or initiates a payment. Those boundaries determine where to log, where to enforce policy, and where to place an approval gate.
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Include delegated and multi-agent activity
If one agent can invoke another, record both identities and the delegation relationship. The parent run, child run, user, task, and workflow should remain linked. Otherwise an investigator may see an apparently valid child action without knowing which prompt, permission, or agent decision caused it.
Separate visibility from authorization
Logs reveal what happened and alerts create an opportunity to respond; neither makes an action safe. Authorization must be enforced by the tool gateway or downstream service using the user’s identity, the agent’s delegated scope, and the operation’s policy. Do not ask the model to decide whether its own request is permitted.
Instrument the action path with structured events
Record events at the orchestration or tool boundary and independently in important downstream systems. A downstream audit record is essential because an agent log can claim that a request was blocked while a service log shows that a state change occurred. Use a common event format so a single correlation ID can connect both records.
| Event data | What it lets an investigator determine | Implementation notes |
|---|---|---|
| Run, task, session, workflow, and parent-child correlation IDs | Which request and execution path produced an action | Generate stable IDs at intake and propagate them through model, tool, queue, and service calls. |
| User, agent, delegated-agent, and service identities | Who requested, selected, and executed the operation | Store the identity actually presented to each downstream service, not only a display name. |
| Permission and scope context | Whether the operation was within the authority granted for this task | Record role, token scope, tenant, resource boundary, and policy version where applicable. |
| Tool name, operation, destination, resource, and relevant arguments | Exactly what the agent attempted | Normalize tool and operation names; capture arguments selectively and redact secrets. |
| Triggering task, declared plan, and policy or approval decision | Whether the action matched the user’s intent and required approval | Keep the policy result, approver identity, and approval timestamp with the event. |
| Retrieval source identifier or URL | Which page, file, email, or API response preceded the action | Record only what is necessary and permitted; preserve a content hash when storing the full source is inappropriate. |
| Timestamp, duration, attempt number, and retry information | Ordering, latency, loops, bursts, and repeated attacks | Use synchronized clocks and record both start and completion times. |
| Result, error, exception, blocked or approved status, and downstream state change | What actually happened rather than what was merely proposed | Capture service response codes and a concise before-and-after state reference. |
Use an event envelope that survives retries
A minimal record can look like this:
{run_id, task_id, session_id, parent_run_id, timestamp, user_id, agent_id, delegated_agent_id, tool, operation, destination, resource, arguments_redacted, permission_scope, policy_decision, approval_id, source_id, attempt, status, error_class, duration_ms, state_change_ref}Keep the envelope immutable or append-only. If an event must be corrected, append a correction that references the original rather than overwriting the evidence. Hash-chain or write-once storage can help detect tampering when your threat model requires it.
Protect sensitive telemetry
Prompts, tool arguments, retrieved documents, and outputs can contain personal data, confidential business information, or credentials. Minimize collection, redact secrets before they enter the log, restrict access by role, encrypt records, and set retention periods based on legal and operational requirements. Do not log a token, private key, password, or full customer record merely because it might improve debugging. Keep a secure reference or content hash when investigators can retrieve the original through an audited process.
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Establish expected behavior before writing detections
A suspicious event is contextual. Build an expected profile for each workflow from its task definition, approved plan, declared capabilities, normal destinations, resource scope, and downstream effects. A summarization agent may legitimately retrieve many documents but should not write to a customer database. A support agent may update a ticket but should not grant administrative privileges.
Create a policy map
- List allowed tools and operations for each workflow.
- List allowed destinations, tenants, resources, and data classifications.
- Mark operations that always require a human decision, such as sending external communications, deleting data, changing permissions, executing code, or moving money.
- Set workflow limits for runtime, number of tool calls, retries, data volume, and spend.
- Record which user or service identity is allowed to delegate each action.
Version this map alongside the agent and record the version in every policy decision. A later investigation then can distinguish a genuine deviation from an intentional policy change.
Baseline normal sequences, not just averages
Collect successful traces for common tasks and describe their normal order: retrieve, parse, draft, request approval, then write, for example. Track ranges for call count, latency, data volume, and destinations, but avoid treating a single number as a universal threshold. The Singapore guidance recommends monitoring usage patterns, execution logs, and behavioral drift; it does not establish one cross-agent threshold or false-positive rate.
Detect deviations that matter
Prefer policy- and context-aware rules over alerts on raw call counts. A high call count may be normal for a batch workflow, while one unexpected permission change may be critical.
Unauthorized tools or destinations
Alert when an agent invokes a tool outside its allowlist, reaches a new host or tenant, uses an unapproved protocol, or accesses a resource unrelated to the task. A destination that appears only after a document retrieval deserves extra scrutiny because the retrieved content may have influenced the route.
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Impact beyond the requested task
Compare the requested outcome with the side effects. Flag a read-only request that produces a write, delete, send, payment, permission change, shell command, or deployment. Also flag arguments that broaden scope, such as an unbounded query, an entire database export, or a wildcard resource when the task named one record.
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Repetition, loops, and abnormal volume
Detect retry bursts, repeated identical calls, oscillating tool sequences, runaway pagination, and sudden increases in records, tokens, latency, or cost. Include attempt numbers so a responder can distinguish a transient service failure from an agent repeatedly trying a blocked operation.
Identity and permission mismatches
Alert when the user, agent, delegated agent, token subject, tenant, or resource owner do not line up. A valid tool call made with a broader service credential than the task allows is still a policy violation. Compare the identity in the orchestration trace with the identity accepted by the downstream service.
Sequence anomalies after retrieval
Correlate retrieval events with subsequent actions. A page, document, email, or API response can contain instructions aimed at the agent rather than information for the user. An unusual high-impact call immediately after such content is a strong investigation signal, especially when the call is not part of the approved plan.
Drift from tested behavior
Maintain a behavioral baseline per workflow and alert on a sustained change in tools, destinations, ordering, or side effects. Baselines should be reviewed after model, prompt, tool, or policy changes; otherwise a legitimate release can look like an intrusion.
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These signals are design patterns, not a validated universal detector. The sources available for this guidance do not establish a common anomaly threshold, cross-vendor benchmark, or measured false-positive rate for agent-monitoring systems.
Treat retrieved content as untrusted input
Indirect prompt injection places malicious instructions in data that an agent is expected to read. The agent may encounter them in a web page, document, email, ticket, image transcript, or API response. OWASP Cornucopia’s Agentic AI card AAI7 states: “Tool output must be treated as untrusted external data and handled with the same caution applied to any other user-supplied input.”
NIST’s Center for AI Standards and Innovation describes the risk more specifically: “Currently, many AI agents are vulnerable to agent hijacking, a type of indirect prompt injection in which an attacker inserts malicious instructions into data that may be ingested by an AI agent, causing it to take unintended, harmful actions.” The NIST technical blog was released January 17, 2025 and updated December 19, 2025.
Instrument the retrieval-to-action boundary
- Tag every retrieved item with a source identifier, trust classification, and retrieval event ID.
- Pass content to the model as data, clearly separated from system and developer instructions.
- Record which source items were in context when the agent selected a tool.
- Require a policy check independent of the model before executing the resulting call.
- Raise the severity when a newly retrieved source is followed by an unapproved destination, expanded scope, or high-impact operation.
NIST’s initial evaluation reported a 57% average attack success rate across five example injection tasks. After 25 attempts for each task, the average rose to 80%. These figures describe that experiment and its task-specific setup; they are not a prevalence estimate or a universal measure of agent risk. Repeated attempts matter because an attacker can keep supplying altered content until one trial changes the agent’s behavior.
Pair monitoring with preventive controls
Use deterministic controls at the tool gateway and downstream service. Monitoring should tell you that a request was attempted; the control should decide whether it can proceed.
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- Least privilege: issue narrowly scoped credentials for the exact resources and operations a workflow needs, with short lifetimes where practical.
- Tool and destination allowlists: permit named operations and approved endpoints rather than exposing a general-purpose network or shell.
- Downstream authorization: re-check the user, tenant, resource, and operation at the service that owns the data.
- Human approval: pause before configured high-impact actions and show the intended destination, arguments, scope, and expected effect to the approver.
- Rate and workflow limits: cap retries, records, runtime, concurrency, and spend to reduce damage and create investigation time.
- Isolation: run risky parsing or code execution in a sandbox with no unnecessary credentials or network access.
Approval should be bound to the exact operation and arguments. If the agent changes the destination, resource, or amount after approval, require a new decision.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Design alerts that lead to action
Send alerts for a meaningful deviation with enough context to decide quickly. Include the task, user, agent version, policy version, source retrieved before the event, tool and arguments after redaction, identity and scope, downstream result, and links or references to the surrounding trace in your internal system.
| Severity pattern | Immediate response | Evidence to preserve |
|---|---|---|
| Blocked call to an unapproved tool or destination | Keep the run paused, notify the workflow owner, and inspect the preceding retrieval and plan. | Policy decision, attempted arguments, source IDs, and all prior calls. |
| Repeated retries, loops, or abnormal data volume with no confirmed state change | Apply the workflow limit, stop further retries, and check for prompt injection or service failure. | Attempt sequence, timing, response codes, and resource counts. |
| Confirmed high-impact state change outside scope | Pause related runs, revoke the agent’s credentials, and start incident response with the downstream owner. | Immutable orchestration and service logs, approval records, before-and-after state references, and identity data. |
| Credential misuse or cross-tenant access | Disable the credential or quarantine the agent, then review other runs using the same identity. | Token subject and scope, affected tenants or resources, and a time-bounded activity export. |
Define a kill switch and quarantine path
Document who may pause a run, disable a tool, revoke credentials, quarantine an agent version, and authorize a restart. The OWASP Top 10 for Agentic Applications 2026 highlights immutable audit logs, behavioral monitoring, alerts, kill switches, credential revocation, and quarantine as operational safeguards. Test these actions so a responder does not discover during an incident that the pause control affects only the user interface while queued jobs continue executing.
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Build tests around direct and indirect prompt injection, excessive tool permissions, unauthorized destinations, unexpected data access, privilege changes, loops, and harmful state changes. Test each action alone and in a realistic sequence, because a benign retrieval followed by a dangerous call can evade isolated checks.
- Define the user task, allowed tools, resources, identities, and expected side effects.
- Insert hostile instructions into representative pages, files, emails, tickets, and API responses.
- Attempt disallowed calls, broadened arguments, alternate destinations, and privilege changes.
- Repeat each attempt with varied wording and timing to model an attacker who can retry.
- Verify that the gateway blocks unauthorized actions and that the trace records the attempted path.
- Exercise pause, credential revocation, quarantine, log preservation, and recovery procedures.
- Measure task-specific outcomes, alert quality, and time to containment; do not rely only on an aggregate score.
NIST recommends adaptive evaluations, task-specific analysis in addition to aggregate results, and multiple attempts where attackers can retry. Those recommendations help test your controls; they do not prove that one detector or monitoring product catches every attack.
Evaluate observability software by coverage
Government guidance from Singapore names Langfuse, LangSmith, Helicone, OpenLLMetry/Traceloop, Azure Agent Monitoring, and AWS Bedrock Trace Events as examples of monitoring technologies. The list is not a comparative test or endorsement. Verify current features, editions, data handling, and regional availability before adoption.
| Evaluation area | Questions to ask | Why it matters |
|---|---|---|
| Action coverage | Does it trace tool calls, MCP interactions, delegated agents, retrieval, and downstream actions, or only model text? | Text-only traces cannot prove what the agent actually changed. |
| Correlation | Can events be queried by agent, user, task, session, workflow, parent run, and policy version? | Investigators need the complete execution path. |
| Action detail | Are destination, resource, redacted arguments, result, error, approval, and state-change references visible? | These fields support both detection and incident review. |
| Detection and response | Can you define policy deviations, baselines, alerts, rate limits, pause actions, and integrations with your response process? | An alert without containment leaves the risky execution path open. |
| Privacy and retention | Can you redact secrets, restrict access, set retention, encrypt records, and export auditable evidence? | Telemetry can itself become a sensitive data store. |
| Deployment fit | Does it work with your orchestrator, tools, MCP servers, queues, and downstream services? | Uninstrumented boundaries create blind spots. |
Feature availability and scope vary by product and change over time. Treat vendor documentation and a controlled proof of concept as the authority for a current deployment decision.
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Quick Recap
A practical rollout sequence
- Map one workflow: document its identities, tools, retrieval sources, destinations, permissions, and external effects.
- Capture baseline traces: add correlation IDs and structured events at orchestration, tool, and downstream boundaries.
- Enforce the obvious boundaries: narrow credentials, allowlist tools and destinations, and block unapproved high-impact operations.
- Add context-aware detections: start with task-policy mismatches, identity mismatches, retrieval-to-action anomalies, and retry loops.
- Protect telemetry: redact secrets, control access, set retention, and make audit records tamper-evident.
- Connect response: implement pause, revoke, quarantine, evidence preservation, and restart approvals.
- Exercise and tune: run injection and misuse scenarios, review false positives with workflow owners, and update baselines after every material change.
Common monitoring mistakes
- Watching only generated text: a harmless explanation can accompany a dangerous tool call; inspect execution and downstream state.
- Logging everything verbatim: unrestricted prompts and outputs expose secrets and personal data; minimize and redact.
- Using call counts as the detector: volume without task context creates noise and misses one-off critical actions.
- Trusting retrieved instructions: external content is data, not authority; keep it separate and correlate it with later actions.
- Relying on model refusal: enforce permissions outside the model and re-check them downstream.
- Alerting without containment: a notification is not a kill switch; define and test operational response.
- Ignoring retries: repeated attempts can turn a low-probability injection into a successful one, as NIST’s evaluation illustrates.
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