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What AI-agent monitoring must cover
A conventional service dashboard answers questions such as “Is the API up?” and “How long did requests take?” An agent needs a wider control loop. It may select tools, retrieve sensitive records, delegate work to another service, change data or send an external communication. A successful HTTP response does not prove that any of those decisions were correct.
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Microsoft’s guidance puts the limitation plainly: “Uptime and error rates are not good indicators of quality and reliability in AI systems.” Treat service telemetry as one layer, then add agent traces, evaluations, security signals, permissions and human review. The resulting record should explain what the agent attempted, what it was allowed to do, what actually happened and who approved consequential steps.
- Execution visibility: follow a request from entry point through model calls, retrieval, tool invocations, downstream services and final outcome.
- Behavioral assurance: evaluate task quality, safety, policy compliance and tool-use correctness continuously.
- Action control: classify actions by risk and enforce approval, denial or blocking in application logic.
- Accountability: retain an audit record that can support incident reconstruction without collecting unnecessary personal or secret data.
- Change detection: notice drift after a model, prompt, tool, memory, retrieval source or policy changes.
1. Define the agent boundary and ownership
Inventory every capability
Start with a current inventory of agents, models, prompts, plugins, tools, data stores, retrieval indexes and downstream services. Record which agent can invoke each capability, in which environment and under which identity. Include scheduled jobs and background workers; an agent that acts without a user waiting at a screen still needs an owner and an audit trail.
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Assign an accountable business or technical owner to every agent. Give each agent a unique, auditable identity rather than sharing a broad service account. The identity should remain stable across runs while the trace records the exact model, configuration and permission set used at that time.
Use least privilege and controlled changes
Grant only the tools, data and operations required for the agent’s role. Separate read, write, delete, payment, messaging and administrative capabilities instead of exposing one unrestricted connector. Version prompts, tool schemas, retrieval settings, model selections, policies and approval rules. A change record should identify who made a change, when it took effect and which agents inherited it.
This boundary makes later questions answerable: which system acted, which capability was available, which policy version applied and whether the behavior began after a specific deployment.
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2. Build an end-to-end trace, not a final-answer log
Use a run identifier across services
Create a globally unique run or conversation identifier at the system boundary. Propagate it through model requests, retrieval calls, tool adapters, policy checks, approval events, queues and downstream APIs. Add a parent-child span relationship so a single run can be reconstructed even when work is asynchronous or delegated.
Microsoft recommends aligning telemetry with OpenTelemetry GenAI semantic conventions. The exact backend can vary; the requirement is that your fields remain consistent enough to correlate events and compare runs over time.
Capture the minimum event set
| Event | Record | Why it matters |
|---|---|---|
| Request intake | Run ID, user or calling service, timestamp, channel, purpose and applicable tenant or region | Establishes who or what initiated the work and the context in which it was authorized |
| Agent execution | Agent identity, model and version, prompt or policy version, configuration hash, start and end times | Shows the exact software and instructions that produced a decision |
| Retrieval | Source identifiers, query representation, ranking or filter metadata, timestamps and access result | Provides provenance without requiring unrestricted copies of source documents |
| Tool request | Tool name and version, operation, validated parameters, requested scope and authorization decision | Explains what the agent attempted and whether it was allowed |
| Tool response | Success or failure, status, bounded result summary, latency and downstream request ID | Distinguishes an intended action from an action that actually completed |
| Policy check | Policy version, rule or classifier result, risk category, score when used and reason code | Allows reviewers to understand why execution was permitted, paused or blocked |
| Approval | Approval ID, reviewer identity or role, decision, timestamp, scope and expiration | Proves that the review covered the action that was ultimately executed |
| Outcome | Final state, user-visible result, reversibility or rollback status and related incident ID | Closes the chain from plan to real-world effect |
Keep sensitive content bounded
Do not assume that storing every prompt, retrieved document and tool response is the only way to obtain traceability. Where full content is unnecessary, store structured fields, classifications, hashes, source IDs or redacted summaries. If investigators may need the original data, place it in a separately protected store and reference it from the trace with an access-controlled pointer.
A final response by itself can hide a failed write, an unauthorized retrieval, an approval bypass or a tool that returned stale information. Intermediate events and outcomes are therefore part of the evidence, not optional debugging detail.
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3. Measure quality and safety alongside service health
Operational measures
Track latency by stage, error and timeout rates, request and tool volume, queue depth, retry counts, token use and infrastructure cost. Segment these by agent, model, tool, tenant, region and release so a healthy aggregate does not conceal a failing workflow.
Agent-specific measures
Add evaluations that reflect the task the agent is supposed to perform. Useful measures include answer or task correctness, citation or retrieval grounding, policy-decision accuracy, tool selection and parameter correctness, completion rate, unsafe-output rate, escalation rate and the proportion of actions requiring rollback. Define an expected range or baseline for each measure and alert on meaningful deviation rather than on every fluctuation.
Automated evaluators can score large volumes, but they should be calibrated against human-reviewed samples. Preserve the evaluator version and rubric with each result; changing the judge can create an apparent quality shift even when agent behavior is unchanged.
Security and anomaly signals
- Repeated attempts to bypass an approval or policy check.
- Unexpected elevation of privilege or use of a tool outside the agent’s normal role.
- Abnormal tool-invocation frequency, unusual call ordering or access to a new data class.
- A sudden increase in high-risk actions, denials, circuit-breaker trips or rollbacks.
- Prompt-injection indicators, suspicious retrieved instructions or output that attempts to change monitoring rules.
Correlate these signals with identity, release and data-source changes. A single unusual call may be legitimate; a new pattern across many runs is more useful for detection.
4. Classify actions and enforce approval deterministically
Create explicit action classes
Write a policy that maps each operation to an execution mode. The model may propose an action, but it must not decide whether its own proposal is exempt from review. The orchestrator or a policy service should make that decision from the tool, target, data sensitivity, amount, reversibility and current context.
| Class | Typical examples | Execution rule |
|---|---|---|
| Low risk | Read-only lookup in an approved, non-sensitive source; formatting or classification with no external side effect | May run automatically when identity, scope and input validation pass |
| Moderate risk | Updating an internal record, sending a draft for user confirmation or accessing sensitive business data | Use additional checks, narrow scopes and configurable review or user confirmation |
| High risk | Changing permissions, exporting sensitive data, placing an order, deleting records or sending an irreversible external communication | Pause before execution; require an identified approver and a scoped approval token |
| Prohibited | Operations forbidden by law, organizational policy or the agent’s purpose | Block deterministically and record the denial; do not offer a model-generated workaround |
Risk scores can help prioritize review, but a score is not a substitute for a rule. Define what happens when scoring is unavailable, contradictory or outside its calibrated range: fail closed for prohibited or high-impact operations, and route uncertainty to a human.
Bind approval to the exact action
An approval should identify the agent, target, operation, material parameters, policy version, expiration and permitted quantity or scope. Recompute a digest of the action at execution time. If any material field changes after approval, invalidate the approval and request a new decision. This prevents an agent from obtaining approval for one operation and substituting another.
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5. Design a reviewer workflow people can actually use
- Show the plan before execution. Present the intended operation, target, data involved, expected effect, risk class and proposed parameters in plain language, with a machine-readable detail view available.
- Expose relevant evidence. Link the plan to the triggering request, retrieval provenance, prior tool results, policy checks and any uncertainty or missing information.
- Offer clear decisions. Provide approve, deny, edit-and-retry, request-more-information and stop options. Require a reason for denial or modification when governance rules call for it.
- Enforce the decision in the orchestrator. The tool adapter should accept an execution token issued by the approval service, not a natural-language statement that approval occurred.
- Show progress and interruption controls. Display queued, running, succeeded, failed and rolled-back states. Let an authorized person pause or cancel work that has not yet reached an irreversible point.
- Publish an outcome summary. Report what executed, what changed, which steps failed, whether compensating actions ran and where the complete log is stored.
Microsoft’s agent-risk guidance emphasizes planned-action visibility, real-time status, outcome summaries and accessible post-execution logs. These features reduce the chance that a reviewer approves a vague intention without understanding the concrete side effect.
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6. Preserve an audit record that supports reconstruction
Required decision context
For each consequential action, retain the action classification, risk score when applicable, authorization result, approval identifier, execution result and policy version. Add timestamps from a trusted clock, agent and tool versions, principal identities, target resource, correlation IDs and any rollback or incident reference.
Make records tamper-evident and searchable
Use append-oriented storage with restricted write paths, encryption in transit and at rest, and separate administrative duties for operating the agent and altering evidence. Record access to the logs themselves. Hash chains, signed batches or an equivalent tamper-evidence mechanism can reveal unauthorized modification, but they do not replace access controls or retention policy.
Index records by run ID, agent ID, user or service principal, action class, approval ID, tool, target, release and time window. Investigators should be able to answer “show every high-risk action by this agent after the last policy change” without joining raw application logs manually.
7. Detect drift, abuse and control failures
Establish a baseline
For each agent, document normal tool frequency, call sequences, data domains, latency distribution, approval rate, denial rate, escalation rate and task-quality range. Baselines should be segmented by legitimate modes such as business hours, batch processing and regional workloads. Alert thresholds that ignore these modes create noise and encourage teams to disable monitoring.
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Use layered detection
- Rule alerts: block a forbidden operation, expired approval, scope mismatch or unrecognized tool.
- Statistical alerts: detect a sustained change in volume, latency, failure rate or high-risk-action share.
- Sequence alerts: flag unusual chains such as repeated failed approvals followed by privilege requests.
- Evaluation alerts: identify declining correctness, grounding, safety or tool-use scores.
- Human escalation: route ambiguous or high-impact anomalies to an investigator with the trace attached.
Every alert should state the evidence, severity, owner, immediate containment action and a linkable run or event set. A detector that produces an uncorrelated notification is difficult to investigate and easy to ignore.
8. Test changes before and after deployment
Agent behavior can change when any of the following changes: model, prompt, tool schema, retrieval index, memory, system instruction, policy, approval logic or external API. Test these components together in representative and adversarial scenarios.
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Pre-deployment checks
- Run regression cases for ordinary tasks, boundary inputs and known failure modes.
- Run adversarial tests for prompt injection, data exfiltration, privilege escalation and approval bypass.
- Verify that tools reject malformed, over-broad or unauthorized parameters independently of the model.
- Exercise denial, timeout, reviewer absence, tool failure and circuit-breaker paths.
- Store the tested configuration, test data version, observed approvals and denials, and evaluator results.
Post-deployment verification
Release gradually where possible, compare new traces with the established baseline and watch quality and safety measures rather than only infrastructure health. Define rollback or disable conditions before deployment. If a release changes the agent’s authority, require a fresh review of its inventory, permissions and action classifications.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.9. Protect privacy and govern retention
Write a data contract for telemetry
For every field, document its purpose, sensitivity, lawful or organizational basis, retention period, owner and access roles. Redact credentials, authentication tokens, unnecessary personal data and secret values before they enter general-purpose logs. Mask or tokenize identifiers when investigators can work without the original value.
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Most operators need metrics and bounded event summaries, not unrestricted prompt and document content. Use role-based access, just-in-time elevation for investigations, approval for exports and detailed access logging. Encrypt sensitive stores and manage keys separately from the systems that write telemetry.
Set retention by risk and obligation
Keep enough detail to meet incident-response, contractual, regulatory and legal requirements, but do not retain every interaction indefinitely. Consider regional residency, deletion requests, litigation holds and backup copies. Document what happens when a record must be deleted or redacted while a derived security finding remains necessary.
10. Choose an implementation approach
Teams can assemble these controls from existing observability, identity, policy, workflow and data-governance systems or adopt an integrated agent-monitoring service. Compare implementations on the controls below rather than on dashboard appearance.
| Evaluation axis | Questions to ask | Evidence of a workable fit |
|---|---|---|
| Trace completeness | Can one query connect request, run, retrieval, tool calls, policy checks, approvals and downstream outcomes? | Correlated spans and structured events with stable IDs across asynchronous services |
| Review enforcement | Can high-risk actions pause before execution and resume only with a scoped, verifiable approval? | Orchestrator-enforced gates, expiration, action digests and fail-closed behavior |
| Detection coverage | Can the system evaluate quality and safety as well as latency, errors and tokens? | Versioned evaluators, anomaly rules, sequence analysis and human escalation |
| Evidence governance | Can access, redaction, encryption, retention and residency meet your obligations? | Field-level controls, auditable access, configurable retention and protected forensic storage |
| Interoperability | Does it fit your existing telemetry and identity stack? | OpenTelemetry-compatible data, documented APIs and portable exports |
| Operating effort | Can the team maintain detectors, evaluators, policies and reviewer queues? | Named owners, service-level objectives, runbooks and a budget for ongoing calibration |
Azure Monitor and Application Insights are examples of production monitoring options described in Microsoft guidance, while OpenTelemetry provides an interoperability direction. They are implementation examples, not a universal requirement; the control objectives remain the same with another stack.
11. Establish an operating cadence
Per run
Validate identity and scope, emit correlated events, enforce policy and approval gates, and record the outcome. A missing trace, authorization result or approval token should be treated as a control failure, not silently ignored.
Daily or continuous
Review critical alerts, high-risk actions, denied requests, detector health, queue backlogs and data-quality failures. Sample successful runs as well as failures; otherwise a system can optimize for clean-looking error rates while producing plausible but incorrect work.
After each material change
Compare regression and adversarial results with the prior version, verify permissions and tool contracts, and monitor a staged rollout. Record the decision to expand, pause or roll back.
Periodic governance review
Reassess action classifications, owners, retention, access roles, evaluator calibration and reviewer workload. Remove obsolete tools and data access. Confirm that incident exercises can reconstruct a run from the retained evidence.
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12. Troubleshoot common monitoring failures
“The dashboard is green, but users report wrong actions.”
Check whether you are measuring only uptime, latency and error codes. Add task-quality evaluations, retrieval provenance, tool correctness and policy outcomes. Sample completed runs and compare the intended action with the actual downstream state.
“We have logs, but cannot reconstruct a run.”
Look for broken correlation across queues and services, missing parent-child relationships, unversioned prompts or tools, and summaries that omit authorization and outcome fields. Make run IDs mandatory at service boundaries and reject events that cannot be associated with one.
“Reviewers approve too slowly, so teams bypass the gate.”
Separate low-risk automation from genuinely consequential work, show a concise plan with relevant evidence, expire stale requests and measure queue time. Do not solve throughput by allowing the model to self-approve. Add an emergency stop and a documented escalation route.
“Alerts are constant and nobody investigates.”
Segment baselines by workload, tune thresholds against reviewed cases and suppress duplicate notifications while preserving the underlying events. Every alert needs a severity, owner and response action. Retire detectors that do not lead to a decision.
“The audit log contains secrets.”
Rotate exposed credentials, block the logging path that captured them, and add pre-ingestion redaction or tokenization. Restrict historical access, document the incident and test that new tool responses cannot leak credentials into general telemetry.
13. A practical rollout sequence
- Map the system: inventory agents, models, tools, data and owners; assign unique identities.
- Define action policy: classify operations as automatic, review-required or prohibited; specify fail-closed behavior.
- Instrument the trace: propagate run IDs and capture request, execution, retrieval, tool, policy, approval and outcome events.
- Protect the evidence: set redaction, access, encryption, residency and retention rules before collecting large volumes.
- Add evaluations: establish quality, safety and tool-correctness baselines, then combine them with operational metrics.
- Implement review: display plans, evidence and progress; enforce scoped approvals in the orchestrator.
- Deploy detection: alert on policy bypass, privilege changes, abnormal tool use, drift and rising high-risk activity.
- Test and rehearse: run adversarial and regression suites, exercise outages and approvals, and practice reconstructing an incident.
- Operate and improve: review alerts and samples, recalibrate evaluators, remove unnecessary access and revise cadence from measured workload and risk.
The result is not merely more telemetry. It is a chain of evidence and control: the system can show what the agent planned, what it was permitted to do, what a human approved, what executed and what changed in the world.
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