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Monitor an AI agent as a distributed, probabilistic workflow—not as a single model response. Give every run a stable ID, trace each model generation, tool call, approval, handoff, and downstream request, then combine logs, metrics, traces, and quality evaluations. Alert on user-visible symptoms such as errors, timeouts, latency changes, dependency failures, abnormal token use, and deviations from a known-good action sequence. This approach lets an operator move from a failed conversation to the exact generation or API call that failed, determine whether the model or a dependency caused the incident, contain unsafe tools, and preserve evidence for recovery.
The sections below show what to capture, how to instrument it with portable telemetry, which alerts matter, and how to run an incident investigation without treating an agent’s probabilistic behavior as a deterministic program.
What does AI-agent monitoring need to cover?
An agent run crosses several components: a user interface, session state, prompts and policies, one or more model calls, retrieval, tools, approval gates, delegated agents, and external services. A normal application log that records only the final answer hides the decisions that produced it. Google Cloud describes telemetry as the only reliable way to inspect an agent’s nondeterministic decisions and selected tools, including failed API requests, infinite execution loops, and latency bottlenecks.
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- Logs are timestamped event records such as a tool invocation, policy decision, retry, or exception. They should carry the run identifier and enough context to reconstruct what happened.
- Metrics are numeric time series for volume, success and error rates, latency, timeouts, retries, tokens, resource use, and dependency health. They reveal fleet-wide changes quickly.
- Traces connect those events into a timeline. A trace should let an operator follow a session or run through turns, model generations, tool calls, approvals, handoffs, and downstream requests.
Quality and safety evaluations complete the picture. A request can return HTTP 200 while being factually wrong, ungrounded, unsafe, or operationally unsuccessful, so technical health alone is not an adequate service-level objective.
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What should I log for every agent run?
Create a stable conversation, session, or run ID before the first model call and attach it to every event and span. Capture the following fields, subject to privacy, legal, and data-residency requirements.
| Area | Capture | Why it matters during an incident |
|---|---|---|
| Identity and context | Run, conversation, session, request, tenant, and user or service identity IDs; timestamps and region | Correlates a user report with the exact execution and separates tenants or deployments. |
| Versions | Agent and workflow version, model name and version, prompt or policy version, tool-schema version, retrieval-index version, and deployment revision | Shows whether a rollout, prompt edit, model change, or index refresh introduced the behavior. |
| Model interactions | Input and output references or payloads where permitted, generation status, duration, token counts, finish reason, temperature or other relevant settings, and error details | Distinguishes model-call failure, truncation, refusals, context limits, and unusual usage. |
| Tool activity | Tool name, arguments, permission scope, approval requirement and decision, start and end time, result or error, retry count, and caller span | Reveals an unexpected tool, dangerous argument, denied operation, loop, or slow dependency. |
| Orchestration | Turn number, handoffs, delegated-agent identity, branch or route, loop counters, and stop reason | Explains repeated work, wrong routing, or an agent that never reaches a terminal state. |
| Downstream services | Service name, endpoint class, status code, timeout, request ID, circuit-breaker state, and dependency latency | Separates an external outage or throttling event from a model or orchestration defect. |
| Outcome and evaluation | Task success, human or automated evaluation scores, groundedness or factuality result, safety decision, and user feedback reference | Detects silent quality regressions that do not produce exceptions. |
OpenAI’s tracing documentation uses concepts such as sessions, turns, model generations, tool calls, and delegated agents, with inputs, outputs, duration, and status. Microsoft’s guidance adds request identity, retrieval provenance, and tool permissions and outputs. Cloudflare’s trace attributes include gen_ai.agent.name, gen_ai.agent.id, and gen_ai.conversation.id, along with tool arguments and results. These fields provide a practical naming model even when your runtime is different.
How do I instrument an agent with portable traces?
Use OpenTelemetry GenAI semantic conventions for model, agent, tool, and orchestration spans. Portable spans let you change backends or combine logs and metrics without rewriting application instrumentation. Instrument at boundaries rather than relying on a single middleware timer.
- Create the root span. Start a trace when the request enters your agent service. Add the run, session, tenant, deployment, and agent identifiers as attributes. Propagate the trace context across queues, worker processes, and delegated agents.
- Wrap each model generation. Record model and prompt versions, input and output references, token usage, duration, finish status, and provider error codes. Redact or hash sensitive payloads according to your data contract.
- Wrap every tool invocation. Create a child span before validation and include the tool name, schema version, permission scope, approval state, sanitized arguments, result status, retries, and downstream request ID. Keep a separate span for the actual external call so tool overhead and dependency time are distinguishable.
- Trace approvals and policy decisions. Record which rule required approval, who or what approved or denied it, the decision time, and the policy version. A denied action is an important security event, not merely an application error.
- Trace retrieval and handoffs. Capture retriever name, index or corpus version, document identifiers or provenance references, ranking metadata where allowed, and delegated-agent spans. This makes an unsupported answer or wrong handoff diagnosable.
- Export to logs, metrics, and a trace backend. Ensure each signal carries the same run and deployment identifiers. Configure sampling and retention deliberately; do not assume every conversation will be retained as a full trace.
Cloudflare explicitly warns that agent traces can be sampled, constrained by span-size limits, and are not a complete or lossless conversation record. Keep governed audit events for critical actions even when ordinary traces are sampled, and emit a compact event or hash when payloads exceed span limits.
Which metrics and alerts reveal errors or an outage?
Start with service-level symptoms, then drill into component metrics. Alert thresholds should be based on your normal baseline and user impact rather than arbitrary universal numbers.
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| Signal | Useful breakdowns | What a change can indicate |
|---|---|---|
| Request volume | Agent, route, tenant, region, model, and deployment | Traffic surge, routing failure, or an unavailable region. |
| Success and error rate | HTTP status, model provider, tool, error class, and workflow step | Code regression, provider failure, permission issue, or dependency outage. |
| Timeout rate | End-to-end, model, tool, queue, and downstream service | Latency saturation, an unresponsive dependency, or an execution loop. |
| Latency | End-to-end and per-step percentiles, route, model, tool, and region | Prompt growth, model slowdown, cold starts, queueing, or a slow API. |
| Retries and loop counts | By tool, error code, run, and agent version | Transient failures, bad retry policy, duplicate side effects, or infinite loops. |
| Token and resource use | Input/output tokens, run, model, tenant, and workflow step | Prompt bloat, repeated calls, runaway planning, quota exhaustion, or cost drift. |
| Dependency health | Status codes, throttling, circuit state, and dependency latency | Downstream outage, rate limiting, authentication failure, or network degradation. |
| Evaluation and safety scores | Task type, model, prompt version, and deployment | Quality regression, grounding failure, unsafe output, or policy drift. |
Use dashboards that show both fleet-level trends and a link from each anomaly to representative traces. A rising end-to-end latency graph is useful; a graph that cannot identify whether model generation, retrieval, approval, or a downstream request consumed the time is not sufficient for response.
How should alerts be prioritized?
- Page immediately for sustained user-impacting errors, timeouts, a safety-policy breach, unauthorized side effects, or a dependency failure affecting a critical workflow.
- Open a ticket or investigate during business hours for gradual token-cost growth, a moderate latency shift, evaluation-score decline, or a new but blocked tool attempt.
- Record for analysis for isolated model refusals, transient retries that recover, and low-severity deviations that do not affect the task.
Pair every alert with a runbook link, an owner, the affected deployment, and a query that opens traces filtered to the alert window. Alert fatigue grows quickly when every model refusal or retry is treated as an outage.
How can I tell whether an outage is the model or a downstream API?
Follow the trace timeline and compare component status, latency, and error codes. The first failing span is not always the root cause, so check the dependency span and the agent’s retry and fallback behavior around it.
- Confirm the symptom. Check request volume, user-visible error rate, timeout rate, and affected regions or tenants. Verify whether the issue is isolated to one workflow or all agent routes.
- Open a representative run trace. Move from the root request to the turn, model generation, retrieval, tool call, approval, and downstream request. Note start and end times for each span.
- Classify the first abnormal event. A provider error, refusal, context-limit response, or malformed generation points toward the model interaction. A DNS error, connection timeout, 429, 5xx, expired credential, or circuit-breaker trip points toward a dependency or integration.
- Compare unaffected runs. Use the same model, prompt version, region, and tool on a known-good trace. If model spans are healthy while one service’s latency and status codes changed, treat the dependency as the leading hypothesis.
- Check retries and side effects. Determine whether retries amplified load or repeated a non-idempotent action. Stop automatic retries for unsafe operations until the dependency is stable.
- Validate recovery. After mitigation, run a controlled request and confirm success, normal latency, correct tool results, and acceptable evaluation scores before restoring traffic.
This method also handles partial outages. An agent may answer general questions while failing only when it invokes billing, search, or a write-capable tool. Per-tool and per-dependency metrics prevent that failure from being hidden by an aggregate success rate.
How do I detect unexpected or unsafe agent actions?
Record the complete action sequence and compare it with a baseline of normal behavior for each task type. Behavioral monitoring should detect both actions that violate policy and actions that are technically allowed but unusual enough to investigate.
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Build a behavioral baseline
- List the expected tools, order or partial order, argument ranges, approval requirements, and terminal conditions for each workflow.
- Measure normal counts of model calls, tool calls, handoffs, retries, tokens, and elapsed time by task type and deployment.
- Store known-good trajectories and representative evaluation scores. A golden trajectory can be a sequence with permitted alternatives rather than one rigid script.
Alert on meaningful deviations
- A new tool, unrecognized delegated agent, or tool used outside its workflow.
- An argument pattern outside an allowlist, such as an unexpected account, destination, quantity, or permission scope.
- A denied or risky operation, especially one followed by repeated attempts or a request to weaken a policy.
- Excessive retries, repeated identical calls, a loop counter approaching its limit, or a run that never reaches a terminal state.
- An abrupt increase in tokens, model calls, latency, or handoffs for an otherwise stable task.
- A mismatch between retrieved evidence and the answer, a factuality or groundedness failure, an unsafe response, or an unsuccessful task outcome.
Microsoft recommends establishing behavioral baselines and alerting on deviations, while Google identifies tool usage, security and safety, and quality as core observability dimensions. Treat these alerts as signals for review; probabilistic systems can take a valid alternative path, so enforcement should combine policy rules with context and human approval.
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- Validate arguments against a strict schema and business rules before execution.
- Require an approval gate for irreversible, financial, privacy-sensitive, or externally visible actions.
- Apply least-privilege credentials, destination allowlists, rate limits, and idempotency keys.
- Provide a deny rule or emergency switch that disables the tool without taking the whole agent offline.
- Log the proposed action, policy decision, approver, executed result, and rollback or compensation action.
How should I monitor latency, token usage, and cost?
Measure latency and usage at the run, model-generation, tool, and downstream-request levels. A single end-to-end timer cannot show whether a long prompt, multiple generations, queueing, or a slow API is responsible.
- Latency: record queue time, time to first model output where available, generation duration, tool execution time, approval wait, downstream latency, and total run duration. Plot percentiles and break them down by model, route, region, and deployment.
- Tokens: record input and output tokens for every generation, cumulative tokens per run, context size, and the number of generations and tool calls. Flag sudden growth relative to the task baseline.
- Cost: join token usage and provider rate-card data in a separate billing or analytics system. Keep the calculation versioned because model prices, discounts, and internal allocations can change; do not treat a token count as a universal currency amount.
- Resources: measure queue depth, worker time, memory, concurrency, and external API quotas. Correlate resource saturation with latency and retry alerts.
Useful controls include maximum steps, per-run token or time budgets, bounded retries with backoff, cancellation on disconnect, and a circuit breaker for unhealthy tools. Log when a budget stops a run so an intentional guardrail is not mistaken for a model failure.
What privacy and retention controls are required?
Telemetry can contain prompts, personal data, credentials, retrieved documents, and tool results. Define a data contract that balances forensic value with privacy, data residency, minimization, retention, legal obligations, access controls, and encryption, as Microsoft recommends.
- Classify fields before enabling payload capture. Store references, hashes, redacted excerpts, or structured attributes when full content is unnecessary.
- Never log raw secrets, access tokens, payment data, or unrestricted tool responses. Scrub arguments and outputs before export, and test the scrubber with adversarial strings.
- Separate operational metrics from sensitive conversation content. Restrict trace access by tenant and role, and audit access to high-sensitivity spans.
- Set different retention periods for aggregate metrics, diagnostic traces, and legally required audit events. Document deletion and export procedures.
- Encrypt data in transit and at rest, and verify that exporters and third-party backends meet the regions and contractual controls your deployment requires.
Sampling reduces storage and may improve performance, but it can omit the one trace needed for an incident. Keep unsampled, compact audit events for approvals, denied actions, and irreversible side effects even when detailed conversational spans are sampled.
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Which monitoring architecture and platforms should I compare?
Choose a stack by capability and control rather than by a dashboard screenshot. The important question is whether an operator can connect a user-visible symptom to the exact model, tool, policy, and dependency event while retaining enough evidence to act safely.
| Comparison axis | Questions to ask |
|---|---|
| Trace depth | Does it show model generations, tool arguments and results, handoffs, approvals, retrieval, queues, and dependency spans in one timeline? |
| Metric coverage | Can it report errors, latency, timeouts, retries, tokens, cost inputs, volume, and resource use by agent, version, tenant, and region? |
| Evaluation and regression | Can you run groundedness, factuality, safety, tool-use, and task-outcome evaluators online and against a retained dataset? |
| Portability | Does it ingest and export OpenTelemetry GenAI spans and preserve semantic attributes when the backend changes? |
| Alerting and SLOs | Can alerts combine service symptoms with quality or safety signals, route to an owner, and link directly to traces? |
| Privacy and retention | Can you redact fields, control sampling, set retention by signal, enforce tenant isolation, encrypt data, and meet residency requirements? |
| Completeness | Are traces sampled or limited by span size? Which audit events remain available when a full conversation is not? |
| Fleet views and export | Can operators compare agents and versions, investigate cohorts, query an API, and export governed evidence? |
OpenTelemetry-based instrumentation is a sound foundation because Google Cloud and Microsoft recommend its GenAI conventions, AWS ingests OpenTelemetry traces, and Cloudflare exposes compatible agent attributes and export. Google Cloud’s observability guidance covers LLM interactions, tool usage, behavior, performance, security, safety, and quality. AWS CloudWatch Omni provides trace analysis, agent topology, evaluations, datasets, experiments, and aggregate production-health views. These are capability examples to evaluate against your requirements, not substitutes for a data contract or incident process.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What is a practical implementation sequence?
Implement in the order that shortens diagnosis and limits risk. Do not wait for perfect payload capture or a new platform before adding basic correlation and guardrails.
- Define the run identity and event schema. Standardize IDs, timestamps, versions, status values, error classes, tool names, policy decisions, and dependency identifiers.
- Instrument the critical path. Add root, model, tool, approval, handoff, retrieval, and downstream spans. Verify propagation across asynchronous workers.
- Build symptom dashboards. Start with volume, success and error rate, timeout rate, end-to-end and per-step latency, retries, dependency health, and token use.
- Add trace-linked alerts. Every alert should open representative traces and identify the affected agent version, region, tenant cohort, and dependency.
- Define behavioral baselines. Save known-good trajectories and expected tool and argument patterns for high-value workflows.
- Add evaluators. Score tool-use correctness, groundedness, factuality, safety, and task outcome. Track scores by prompt, model, and deployment version.
- Enforce containment. Add schema validation, least privilege, approvals, rate limits, bounded retries, idempotency, and emergency disablement for risky tools.
- Exercise the runbook. Simulate model errors, provider timeouts, dependency 5xx responses, malformed tool arguments, loops, and denied actions. Confirm that responders can find evidence and stop side effects.
- Close the learning loop. Add each confirmed failure to an evaluation dataset and run regression checks before restoring or expanding traffic.
How do I run an agent incident from alert to recovery?
Use a consistent workflow so responders do not guess from the final answer or restart a service before preserving evidence.
- Alert on elevated errors, latency, timeouts, dependency failures, abnormal token use, or a safety deviation.
- Open the affected run trace and inspect the timeline from session to turn, model generation, tool call, approval, handoff, and downstream service.
- Compare the action sequence and evaluation scores with a known-good baseline or golden trajectory.
- Contain risky tools with approval gates, deny rules, rate limits, or temporary disablement. Stop retries that could repeat side effects.
- Preserve governed logs, trace IDs, policy versions, deployment information, and dependency responses sufficient for reconstruction.
- Add the failure case to an evaluation dataset, run regression checks, and verify recovery before restoring or expanding traffic.
Microsoft notes that AI observability is a continuous process with operational overhead. Assign ownership for dashboards, evaluators, retention, and runbooks; otherwise instrumentation will decay after the first launch.
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What common monitoring mistakes make incidents harder?
- Logging only the final answer: add model, tool, approval, and dependency spans so the decision path is visible.
- Using one global success rate: split by workflow, model, tool, deployment, region, and tenant to expose partial outages.
- Retrying every failure: classify errors and make side-effecting operations idempotent before retrying.
- Capturing unrestricted payloads: apply redaction, minimization, access controls, encryption, and retention rules before exporting content.
- Assuming traces are complete: account for sampling and span limits, and retain compact audit events for critical actions.
- Ignoring quality signals: add groundedness, factuality, safety, tool-use, and task-outcome evaluations to technical metrics.
- Hard-coding a single path: baseline permitted alternatives and investigate deviations instead of rejecting every valid probabilistic choice.
- Failing to test the runbook: rehearse dependency outages, loops, malformed arguments, and policy denials with safe test tools.
Frequently Asked Questions
Can I monitor agent conversations without storing the full prompts and answers?
Yes. Store structured attributes, redacted excerpts, hashes, document identifiers, and references to separately governed content instead of raw payloads. Keep full content only where the incident, legal, or product requirement justifies it, and document who can access it.
How long should AI-agent logs and traces be retained?
There is no universal retention period. Set separate periods for aggregate metrics, sampled diagnostic traces, and audit events for approvals or irreversible actions, based on your incident-response needs, contracts, privacy rules, and regional obligations.
Do I need a separate evaluator for every agent workflow?
Use risk-based coverage rather than one evaluator per agent. High-impact workflows should have dedicated tool-use, safety, grounding, and task-outcome checks, while low-risk routes can share evaluators and a smaller regression set.
What should an on-call engineer include in an incident ticket?
Record the alert window, affected agent and deployment versions, representative run and trace IDs, first abnormal span, model and dependency status codes, policy decisions, containment action, and validation evidence after recovery. This makes the ticket useful for later regression tests.
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