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Monitor an enterprise AI agent as an execution trace, not merely as an HTTP service. A useful trace follows one request through model calls, tool actions, retrieval, state changes, handoffs and the final result, while layered metrics show whether the task was completed correctly, safely, on time and at an acceptable cost.
The practical operating loop is: instrument every run, connect quality and business impact to the trace, alert on actionable deviations, investigate the earliest divergence, apply a targeted fix, and turn the sanitized failure into a regression evaluation. This approach catches failures that ordinary uptime, error-rate and latency dashboards cannot.
Why ordinary service monitoring is not enough
Traditional application monitoring answers questions such as whether a service is reachable, how long requests take and how often an API returns an error. An agent can look healthy on all three measures while producing an ungrounded answer, using an unauthorized tool, omitting a required step or claiming an action it never completed.
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Design around the questions your team will actually ask:
- Why did this task cost dramatically more than usual?
- Did the agent actually do what it said it did?
- Why did it call the same tool repeatedly?
- Was the answer wrong because retrieval returned poor context, because a tool failed, or because the model selected the wrong action?
- Did a prompt, model, policy or deployment change alter behavior for only one customer segment?
A trace that cannot answer those questions is incomplete even when the service reports a successful HTTP status.
Represent every request as an execution trace
Create one root trace for each user request, job or conversation turn. Propagate its trace and span identifiers across services so the original request remains connected to model providers, retrieval systems, tool services and sub-agents. Preserve event order and parent-child relationships; sequence is often the difference between a useful diagnosis and a list of unrelated logs.
| Span or event | Capture | Diagnostic purpose |
|---|---|---|
| Root request | Request/session ID, tenant or user segment where permitted, environment, task type, start and end time, final status | Finds the run and supports filtering without exposing unnecessary content |
| Model call | Model identifier, prompt or deployment version, relevant parameters, input/output token counts when available, latency, finish reason and error | Shows which model decision or release changed behavior and attributes cost and delay |
| Tool action | Tool name, authorized identity and context, redacted arguments, result or error, duration and retry count | Reveals bad arguments, permission failures, repeated calls and downstream outages |
| Retrieval | Query, returned references, ranking or relevance data and latency, subject to policy | Tests whether the model received useful, current and appropriately scoped context |
| State transition | State key or type, operation, version and success or failure; record values only when approved | Finds unexpected mutations, stale state and lost checkpoints |
| Handoff or sub-agent | Parent trace, receiving agent, task or capability, authorization context and outcome | Exposes routing errors, loops between agents and work that disappears across service boundaries |
| Result and evaluation | Completion status, policy result, user feedback, evaluator scores and links to the supporting spans | Connects a user-visible outcome to the actions that produced it |
Capture operational decision and action metadata rather than unrestricted hidden chain-of-thought. The least sensitive telemetry that still explains behavior is safer, cheaper and easier to retain. If a team needs more detail for a narrowly defined investigation, use a controlled, time-limited mechanism instead of making all production content permanent.
Build a layered signal and alert model
Dashboards should combine infrastructure, agent-behavior, quality, economic and security signals. An alert is useful only when it names an owner and a next action. Set thresholds from your service objectives, risk tolerance and observed baseline; no universal threshold is established for every agent or workload.
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| Signal | What it detects | Example response |
|---|---|---|
| Availability and provider errors | Agent endpoint failures, rate limits, model-provider errors and dependency outages | Check provider status, quotas, retries and fallback behavior |
| End-to-end and step latency | Slow model calls, retrieval, tools, queues or stalled orchestration | Open the slow traces and identify the first span that breached the budget |
| Task completion and quality | Incomplete, incorrect, ungrounded or unsafe results despite successful requests | Sample affected traces for domain review and compare with the release baseline |
| Tool errors and repetition | Invalid arguments, permission failures, retries and loops that consume resources | Inspect the first failed action, tool contract and stop conditions |
| Retrieval relevance | Empty, stale or poorly ranked context | Review query, references, ranking and index freshness |
| Trace completeness | Missing model, tool, retrieval or handoff spans | Check instrumentation initialization, propagation and custom spans |
| Cost and token use | Prompt growth, excessive iterations, expensive model selection or runaway loops | Compare the run with its task baseline and locate the costliest span |
| User correction and abandonment | People fixing, retrying or abandoning apparently successful results | Link feedback to the trace and add a representative evaluation case |
| Policy and security events | Unauthorized tool attempts, suspicious prompts, data exposure or privilege misuse | Apply the incident process, preserve permitted context and audit access |
Use online checks for live traffic when latency and privacy allow, and offline evaluations before release. Human or domain-expert review remains important when correctness depends on business context that an automated score cannot judge.
Instrument the execution graph step by step
1. Establish identifiers and propagation
Generate a root trace ID at the request boundary and carry it through queues, synchronous calls, asynchronous jobs and agent handoffs. Add a stable request or session identifier, deployment and prompt versions, task type, environment and permitted tenant or user-segment dimensions. Avoid putting raw personal data into identifiers.
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Record the selected model, relevant generation parameters, prompt and deployment version, timing, token counts or provider usage data when available, finish reason and error. Store content only under an approved policy. A model span without the version that produced it cannot explain a regression after a prompt or model change.
3. Instrument tools as first-class operations
For each action, record the tool name, authorization context, redacted arguments, result class, error, duration and retry count. Include whether the tool was selected by the model, a deterministic guardrail or a workflow step. Tool contracts should make invalid arguments and idempotency behavior visible; otherwise a harmless retry can become a duplicate transaction.
4. Instrument retrieval and state
Capture the retrieval query, returned references, relevance or ranking data and latency where policy permits. For state, capture the operation and version rather than unrestricted values unless a reviewed diagnostic need exists. State and retrieval spans should remain children of the same trace, even when implemented by separate services.
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When one agent delegates to another, keep the parent-child relationship, receiving capability, authorization context, input contract and outcome. A handoff that starts a new unrelated trace makes loops, missing work and permission boundaries difficult to see.
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Use asynchronous, buffered or batched delivery to the telemetry backend. Sabith K Soopy, a Principal Engineer at StackGen, wrote in a CNCF article dated August 4, 2026: “Tool execution should never wait on a synchronous HTTP POST to a tracing backend.” If the backend is slow or unavailable, the agent should continue according to its own reliability policy while buffers apply bounded backpressure, sampling or local spillover. Define what data can be dropped and what security events must be retained.
Make dashboards answer operational questions
Organize views around a run and its population, not around isolated log lines. A run view should show the ordered timeline, each span’s duration, model and tool metadata, retrieval references, state transitions, final status and evaluation or user feedback. A population view should show distributions and trends by task, deployment, model, environment and permitted user segment.
- Cost view: cost and tokens per run, by model and span, with outliers linked to traces.
- Latency view: end-to-end and per-step percentiles, queue time and downstream duration.
- Reliability view: provider errors, tool failures, retries, timeouts and incomplete traces.
- Quality view: completion, evaluator results, grounding checks, user corrections and safety outcomes.
- Behavior view: tool repetition, iteration count, handoff count and unusual state transitions.
Define an owner and runbook for every alert. For example, a spike in tool errors should route to the tool or platform owner with the failed operation and trace links, while a quality regression should route to the agent product and domain owner with representative examples.
A six-step troubleshooting loop
- Locate the run. Start with the request or session identifier, timestamp, deployment and prompt versions, alert and user feedback. Confirm that a trace exists and allow for normal ingestion delay.
- Check the trace shape. Verify that expected model, retrieval, tool, state and handoff spans appear in order. Missing framework spans can mean instrumentation was not initialized or enabled. Missing tool spans can mean tools were not bound to the model or tool nodes were absent from the graph.
- Find the earliest divergence. Compare the actual sequence with the expected task plan. Look for the first unexpected tool choice, poor retrieval result, repeated failure, unauthorized attempt or state mutation. Do not begin with the final answer if the underlying fault occurred several steps earlier.
- Separate behavior from infrastructure. Check provider and runtime errors, network limits, permissions, queues and downstream tool health alongside the agent’s actions. An authorization error while querying telemetry may be a reader-role or RBAC problem rather than evidence that the trace was never created.
- Assess quality and impact. Write down the expected task outcome, then compare the result and supporting context with that criterion. Quantify cost, latency, retries, user correction, data exposure and policy implications for the affected run.
- Fix and prevent recurrence. Save the trace or a sanitized representative example, create a targeted evaluation, change the prompt, tool contract, retrieval, policy or infrastructure component implicated by evidence, and rerun the evaluation before release. After deployment, inspect live traces for the same failure signature.
Microsoft Foundry trace troubleshooting checks
Microsoft’s troubleshooting documentation gives concrete checks for common trace symptoms. Treat configuration names and procedures as version-sensitive and verify them against the current documentation and framework release.
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- No traces: verify the Application Insights connection, generate recent traffic and account for ingestion delay.
- No LangChain or LangGraph spans: ensure the Microsoft OpenTelemetry distribution and framework instrumentation are initialized and enabled before agent execution.
- Missing tool-call spans: verify that tools are bound to the model and that tool nodes exist in the graph.
- Incomplete spans: check content-recording and generative-AI semantic-convention settings, or add manual spans around custom operations.
- Authorization errors: verify access control on Application Insights and Log Analytics for the reader and operator roles.
- Sensitive content in telemetry: disable content recording in production and redact sensitive values before capture.
Protect telemetry as sensitive production data
Prompts, tool arguments, retrieved documents, outputs and state can contain personal, financial, health, confidential or security-sensitive information. Microsoft advises disabling content recording in production and redacting sensitive data before it enters telemetry. Apply that rule to every exporter, local buffer, debugging dump and evaluation store, not only the primary dashboard.
- Define a field-level classification and redact or hash values before export.
- Restrict trace access by role and review privileged-reader permissions.
- Set retention and deletion rules that match legal, contractual and operational needs.
- Use sampling for routine traces while preserving enough complete examples for high-risk tasks and incidents.
- Audit privileged tool actions and retain the event context needed to investigate them.
- Monitor suspicious prompt activity and behavior drift without collecting unnecessary content.
Privacy-aware logging and permission audits are also emphasized in government guidance, including Singapore’s addendum on AI security. Your organization’s data policies and applicable rules determine the final retention, residency and access design.
Turn incidents into evaluations
A complaint, domain correction or policy breach should enter a tracked review process rather than remain only in a chat transcript or incident thread. Preserve a sanitized input, expected outcome, relevant references, action sequence and failure label. Keep enough context to reproduce the problem without retaining data that the evaluator does not need.
Build an evaluation set from recurring failure classes such as wrong-tool selection, retrieval miss, unauthorized action, incomplete handoff, hallucinated completion and excessive iteration. Run those checks before deployment and after release. Use automated metrics for stable criteria, online checks for live behavior where appropriate and human review for domain-dependent correctness. Tie evaluation results to prompt, model, tool and policy versions so a score change has an identifiable cause.
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Choose an observability approach by capability
Teams commonly combine framework instrumentation, OpenTelemetry, cloud-native monitoring and a dedicated agent-observability service. LangSmith describes framework-agnostic tracing, OpenTelemetry integrations and managed-cloud, bring-your-own-cloud and self-hosted deployment options. Langfuse describes native SDK and OpenTelemetry ingestion, framework integrations, trace-linked monitors, user feedback and evaluations. These are vendor claims; validate current availability, security posture, deployment boundaries, data location and plan-specific features for your region.
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| Decision axis | Questions to answer |
|---|---|
| Trace fidelity | Does it cover model, tool, retrieval, state and handoff spans? Can engineers add manual spans and preserve order? |
| Interoperability | Does it work with the existing orchestration framework, model providers, cloud backends and OpenTelemetry pipeline? |
| Quality workflow | Can reviewers inspect traces, collect human feedback, run offline and online evaluations, maintain regression datasets and alert on quality? |
| Security and residency | Are content capture, redaction, access control, retention, deployment model and data location suitable for the workload? |
| Operational reliability | Does export buffer asynchronously, handle backpressure and backend outages, and provide sampling and retention controls? |
| Economics and operations | What are the costs of trace volume, storage, evaluation usage and engineering maintenance, and how is cost attributed to each run? |
A small, single-call application may need structured logs and basic metrics. A multi-step or multi-agent system has a much stronger case for full traces and evaluation workflows. No tool should be called best without workload, compliance, infrastructure and budget requirements.
Control cost, volume and failure behavior
Token and tool costs should be attributable to a root trace and, where possible, to individual spans. Track both average and outlier runs; an average can hide a small number of runaway loops. Set explicit iteration, timeout and budget policies, and record when a guardrail terminates a run.
Sampling reduces storage but can remove the exact trace needed during an incident. Use risk-based rules to retain complete traces for privileged actions, safety events, evaluation cases and severe user impact, while sampling routine low-risk traffic. Document what is sampled, what is redacted and how an operator can temporarily increase diagnostic detail under approval.
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Telemetry failure must have a defined degradation mode. Bounded buffers, retry limits and local metrics can preserve application availability, while security-critical events may require a durable path. Test exporter outages so the failure mode is known before a production incident.
A practical rollout sequence
- Map the graph: list model calls, tools, retrieval, state stores, queues and handoffs for one important task.
- Instrument the critical path: create root traces, propagate context and add model and tool spans before expanding coverage.
- Secure the data: classify fields, implement redaction, restrict access and set retention before collecting production content.
- Define service objectives: choose completion, latency, cost, safety and trace-completeness measures with owners and escalation actions.
- Build the failure loop: connect alerts and user feedback to trace review, sanitized examples and evaluations.
- Exercise failure modes: test provider limits, tool outages, missing permissions, retrieval misses, exporter outages and repeated-call loops.
- Expand by risk: add sub-agents, state transitions and high-value tasks, then tune sampling and retention from observed diagnostic value.
What current industry survey figures do—and do not—say
LangChain’s 2026 survey of more than 1,300 professionals reported that 89% of surveyed organizations had some form of agent observability and 62% reported detailed tracing. Among respondents with agents in production, 94% reported some observability and 71.5% reported full tracing. The same survey reported offline evaluation at 52.4% and online evaluation at 37.3%. Among enterprise respondents with 2,000 or more employees, 24.9% cited security as a major concern.
These are survey results, not independent census estimates or universal enterprise rates. They indicate adoption and concern reported by the survey’s respondents; they do not establish a standard architecture, alert threshold, vendor reliability, price or compliance outcome.
Quick Recap
Production readiness checklist
- Every request has a root trace and propagated context.
- Model, tool, retrieval, state and handoff spans preserve order and parent-child relationships.
- Prompt, deployment and model versions are attached to runs.
- Tool arguments and results are redacted according to a documented policy.
- Cost, token use, latency, retries, repetition and completion are visible per run.
- Dashboards separate infrastructure health from task quality.
- Alerts have owners, thresholds based on local baselines and written next actions.
- Telemetry export is asynchronous or buffered and has a tested outage behavior.
- Trace access, retention, sampling and deletion are governed like other sensitive production data.
- User corrections, domain reviews and policy events create evaluation cases.
- Regression evaluations run before release and live behavior is checked afterward.
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