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To monitor an AI agent in Grafana, instrument every model generation, workflow step, and tool call with Grafana Agent Observability or compatible OpenTelemetry instrumentation. Export both generation data and OpenTelemetry traces and metrics, route them to Grafana Cloud or an OpenTelemetry Collector/Grafana Alloy deployment, then build dashboards for duration, time to first token (TTFT), token usage, estimated cost, tool calls, and errors. Use trace links and exemplars to move from an aggregate spike to the exact model call or tool that caused it.
The important implementation detail is provider configuration: your generation exporter, OpenTelemetry TracerProvider, and MeterProvider all need to be configured. Grafana warns that traces and metrics can otherwise disappear silently. The workflow below shows how to set up the signals, investigate incidents, and alert on service objectives without treating estimated cost as a provider invoice.
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What does Grafana Agent Observability capture for an AI agent?
Agent Observability is an OpenTelemetry-based layer for recording the complete path of an agent request. Instead of treating an agent as one opaque API call, it represents the user request, workflow steps, model generations, and tool calls as related telemetry.
- Metrics show latency distributions, TTFT, token consumption, generation counts, tool-call counts, and error rates.
- Traces connect a user request to each workflow step, model call, and tool invocation, so you can locate a bottleneck rather than merely observe that one exists.
- Logs and events carry the error payload and operational context for an individual failure.
- Conversation and generation records retain model, prompt, tool, token, cost, and quality context for analysis.
The SDKs support Go, Python, TypeScript, Java, and .NET. Framework integrations are documented for LangChain, LangGraph, OpenAI Agents, LlamaIndex, Google ADK, and the Vercel AI SDK. If your framework is not on that list, use its OpenTelemetry hooks or instrument the generation and tool boundaries yourself.
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Use metrics for trends and alerting, traces for causality, and generation records when you need the token or cost breakdown for one model operation.
What architecture should I use before collecting telemetry?
Choose where telemetry is generated, how it is transported, and where each signal is stored. A common architecture is an instrumented agent sending OTLP to Grafana Cloud directly or through Grafana Alloy/OpenTelemetry Collector. Metrics follow a Prometheus-compatible path, while traces are stored in a trace backend such as Tempo.
Minimum components
- An agent instrumented with a Grafana Agent Observability SDK or compatible OpenTelemetry instrumentation.
- A generation-export path for LLM generation, token, cost, and quality fields.
- An OpenTelemetry
TracerProviderfor spans and aMeterProviderfor metrics. - An OTLP destination: Grafana Cloud, Grafana Alloy, or an OpenTelemetry Collector.
- Retention, access-control, redaction, and data-residency decisions made before prompts or tool payloads are exported.
Do not assume that configuring one exporter configures the others. A generation exporter can be active while spans or metrics are absent if the corresponding providers are not registered.
How do I instrument an AI agent in Grafana?
Instrument the boundaries that matter to an operator: the incoming conversation, each generation, every workflow step, and every tool call. Follow these steps in the order shown so missing providers or disconnected exporters are caught early.
- Install the SDK or OpenTelemetry integration for your language and framework. Use the SDK for Go, Python, TypeScript, Java, or .NET, or add the documented integration for your agent framework. If there is no integration, create spans around the workflow and tool functions and attach generation attributes at each model call.
- Configure the generation exporter. The exporter must receive one record for every model generation, including retries and fallback calls. Preserve the operation name, model, provider, agent identity, and a correlation identifier that can connect the generation to its parent trace.
- Register a
TracerProvider. Set the provider before the agent handles traffic. Export spans through OTLP so a trace contains the parent conversation, workflow steps, generation spans, and tool spans. - Register a
MeterProvider. Configure the meter exporter and verify that counters, histograms, and gauges are emitted. Grafana specifically warns that traces and metrics can be silently lost when these providers are not configured. - Propagate context through asynchronous work. Agent frameworks often schedule tools, retries, and parallel model calls. Preserve the active OpenTelemetry context across those tasks; otherwise the calls may appear as unrelated traces and cannot be tied back to the user request.
- Generate a controlled test request. Make one request that invokes a known tool and one model generation. Confirm that a conversation, generation, workflow step, and tool call are visible before enabling production traffic.
What should one generation record contain?
Capture the model and provider, operation name, start and end times, input and output token counts, reasoning-token counts when exposed, request-level fees, estimated cost, status, and links to the parent conversation and trace. Record tool name, duration, status, and error details on tool spans. Apply redaction before export rather than relying on a dashboard to hide sensitive values later.
How do I route Grafana agent telemetry?
Send OTLP directly to Grafana Cloud when you want a managed endpoint, or place Grafana Alloy/OpenTelemetry Collector between the agent and the backends when you need centralized processing, filtering, sampling, or routing.
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Direct OTLP to Grafana Cloud
Configure the agent’s OTLP exporter with the Grafana Cloud endpoint and credentials, then verify that metric and trace pipelines are both enabled. Keep generation export enabled as a separate path if your SDK uses one; OTLP alone may not carry every generation or cost field.
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Through Alloy or an OpenTelemetry Collector
Use a collector when several services share the same export policy. It can receive OTLP, apply redaction or attribute limits, batch records, and send metrics to a Prometheus-compatible destination and traces to a backend such as Tempo. Test collector queues and retry behavior so a temporary backend outage does not silently discard the agent’s telemetry.
How can I prove the route works?
- Check that a test request increments a generation count and creates a trace.
- Open the trace and confirm that its child spans include the model generation and each tool call.
- Query the metric stream for the same time range and verify a non-zero duration and token measurement.
- Force a safe test failure, such as an invalid tool argument in a staging environment, and confirm that the error payload is attached to the failed call.
Which Grafana metrics belong on the first dashboard?
Start with a small operational dashboard that answers four questions: how many generations are running, how long they take, how much they consume, and where they fail. Grafana documents the following metric names for these panels.
| Metric | Use it to measure |
|---|---|
gen_ai_client_operation_duration_seconds |
End-to-end duration of a model operation |
gen_ai_client_operation_time_to_first_chunk_seconds |
Time until the first streamed chunk arrives |
gen_ai_client_time_to_first_token_seconds |
Time until the first token is available |
gen_ai_client_token_usage |
Input, output, and other reported token consumption |
gen_ai_client_tool_calls_per_operation_count |
Tool-call volume per model operation |
agento11y_generation_cost_usd_total |
Accumulated estimated generation cost in US dollars |
agento11y_model_price_usd_per_1k_tokens |
Configured model price per 1,000 tokens |
agento11y_model_price_request_usd |
Configured per-request model fee |
Pair the duration metrics with generation count and error rate. A latency percentage without traffic volume can mislead: one slow request and a sustained degradation can produce very different operational risks.
How do I monitor AI agent latency and TTFT?
Use histograms and percentiles rather than averages alone. Average duration hides long-tail delays that users experience during overloaded models, slow tools, or retries.
Separate waiting time from total completion time
TTFT measures responsiveness while the user is waiting for the first token. Total operation duration measures how long the complete generation takes. Put both on the dashboard: a high TTFT points toward queueing, connection setup, provider scheduling, or prompt processing, while a normal TTFT with a high total duration points toward slow token streaming, long output, or downstream work.
Break latency down by execution stage
- Compare generation duration with tool-call duration to determine whether the model or a tool dominates the critical path.
- Inspect workflow-step spans for retrieval, validation, planning, and post-processing delays.
- Compare p95 or another agreed percentile by model, provider, agent version, and operation.
- Use exemplars or dashboard links to open a representative trace from a latency spike.
Set the percentile and time window to match your service objective and traffic pattern. A short window can be too noisy for a low-volume agent; a long window can hide a newly introduced regression.
How do I track token usage and estimated cost?
Use the Usage and cost analytics views to find agents with large prompts, unnecessary context, expensive model choices, or excessive reasoning tokens. Grafana records token and cost data per generation, which lets you compare one operation, one conversation, an agent version, or a model.
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Why recorded cost may not equal a current list-price calculation
Recorded cost can reflect the prices configured when the generation occurred, historical rates, long-context tiers, and per-request fees. Multiplying an aggregate token count by today’s published token price may therefore produce a different number. Treat Grafana’s value as an estimate for operational analysis, not as a provider invoice.
Useful cost views
- Rank agents by total estimated cost and by cost per conversation.
- Compare input, output, and reasoning-token shares to identify prompts that carry excessive context.
- Compare model and provider choices for the same operation so quality and latency can be considered alongside spend.
- Track tool-call volume and duration next to generation cost; repeated tool retries can increase both latency and model usage.
The documented cost metrics are agento11y_generation_cost_usd_total, agento11y_model_price_usd_per_1k_tokens, and agento11y_model_price_request_usd. Preserve the price configuration used for each period so a later report can explain changes in recorded totals.
How do I find which model or tool caused an agent error?
Start with the error-rate panel, then move from the aggregate metric to the failed trace and its generation or tool record. This preserves the execution order and shows whether the failure originated in the model, a tool, or the workflow code between them.
- Filter the error panel by time range. Match the window to the user reports or deployment event.
- Break down errors by agent, model, provider, version, and operation. A concentration in one model or version narrows the search before you open individual traces.
- Open an exemplar or linked trace. Follow the parent conversation into workflow steps, generations, and tools.
- Inspect the
call_errorpayload. Read the status, provider message, retry information, and operation context attached to the failed call. - Open the Tools view for tool-specific evidence. Compare each tool’s duration and error rate, then inspect its span arguments and result after redaction.
- Check retries and fallbacks. One user request can contain several generations; a successful final response may still include failed model or tool attempts that increase cost and latency.
When the trace is missing, first verify context propagation and provider registration. When the trace exists but the error payload is empty, inspect the instrumentation around the exception boundary and ensure the exporter is not dropping event attributes.
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Build alerts around a service objective, not an arbitrary value. Grafana documents alert patterns for error-rate thresholds, p95 latency, daily estimated-cost budgets, and evaluation-score drops.
| Alert target | Signal to evaluate | Design consideration |
|---|---|---|
| Failures | Error rate over a defined window | Require enough requests to avoid paging on one transient failure |
| Latency | p95 operation duration or TTFT | Choose a percentile and window that match the user-facing SLO |
| Spend | Daily estimated cost | Use a budget threshold and label the alert as an estimate |
| Quality | Evaluation-score change | Pair the score drop with the affected agent or model version |
Include agent, model, provider, and version labels in the alert annotation. Link the notification to the dashboard and, where supported, to an exemplar or trace so the responder can begin with evidence rather than a blank query.
Preventing noisy alerts
- Use a pending period so a brief provider hiccup does not immediately page.
- Combine a percentage threshold with a minimum request count for low-volume agents.
- Separate warning and critical thresholds; a budget warning should arrive before the daily limit is reached.
- Review alert cardinality. Labels such as conversation ID or raw prompt text can create an unmanageable number of time series and expose sensitive data.
Which dimensions should I use in dashboards?
Break panels down by agent, model, provider, version, tool, and time range. These dimensions support practical questions such as whether a new agent version increased TTFT, whether one provider is slower, or whether one tool is responsible for most failures.
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A conversation can contain several generations. Show conversation-level totals separately from individual model-call totals so a dashboard does not imply that one conversation equals one generation. Keep high-cardinality identifiers, full prompts, and unbounded user data out of metric labels; place detailed context in traces or generation records with controlled retention.
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Both managed and self-managed designs can use OpenTelemetry-compatible instrumentation. The decision depends less on the SDK and more on operations, retention, privacy, and query requirements.
| Comparison axis | Questions to answer |
|---|---|
| Instrumentation coverage | Does the SDK or integration capture your framework’s generations, tools, and workflow steps? |
| OpenTelemetry compatibility | Can the stack receive the OTLP traces and metrics your agents emit? |
| Retention | How long must metrics, traces, prompts, and generation records remain available? |
| Cardinality and query performance | Can the system handle dimensions for agent, model, provider, version, and tool? |
| Drill-down | Can a metric exemplar open the relevant trace and generation record? |
| Alerting and reporting | Are error, latency, cost, and evaluation alerts supported with your notification workflow? |
| Cost model | How do ingestion, storage, query, and collector operations affect total cost? |
| Privacy and residency | Where are prompts and tool payloads stored, and how are redaction and tenant isolation enforced? |
| Framework support | Are your agent framework and language covered without custom instrumentation? |
Document these choices before production rollout. A low-cost telemetry design that cannot retain the trace needed for an incident is not an effective observability design.
How should I protect prompts, tool payloads, and user data?
Agent telemetry can contain the same confidential information as application logs, plus complete prompts, retrieved documents, tool arguments, and model responses. Decide what is collected at the instrumentation boundary.
- Redact secrets, credentials, personal data, and regulated fields before export.
- Separate operational metadata from content so most dashboards work without storing raw prompts.
- Apply retention periods appropriate to traces, metrics, conversations, and generation records; they need not be identical.
- Restrict access by tenant, team, and environment, and audit who can open detailed traces.
- Verify data-residency requirements for the selected hosted or self-managed deployment.
- Test that error payloads do not echo sensitive request or tool content.
What changed on October 1, 2026?
Grafana Labs documents that Agent Observability generation metering, including evaluator and guard-token metering, starts on October 1, 2026. Because feature names, pricing, and billing rules can change, check the current Grafana documentation and your account’s pricing details when planning budgets or interpreting usage reports.
This date does not turn estimated generation cost into a provider invoice. Continue to distinguish telemetry estimates, configured prices, and the charges shown by the model provider.
How do I troubleshoot missing or misleading telemetry?
Metrics are absent but traces exist
Check that a MeterProvider is registered and that its exporter is enabled. Verify the OTLP metric endpoint, authentication, and collector pipeline separately from the trace pipeline.
Traces are absent but generation records exist
Check the TracerProvider, span exporter, and context propagation through asynchronous tasks. A generation exporter does not automatically create a complete distributed trace.
Token totals look too low
Confirm that retries, fallback generations, streaming chunks, and reasoning tokens are included in the instrumentation. Compare a single generation record with the conversation total before changing dashboard queries.
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Cost totals do not match a spreadsheet
Review configured historical prices, long-context tiers, and per-request fees. Reconcile by generation and time period instead of multiplying one current list price by an aggregate token counter.
Tool failures cannot be isolated
Ensure each tool invocation has its own span, name, duration, status, and error event. Avoid putting the tool name only in an unindexed log message; the Tools view and breakdown panels need a structured attribute.
What is a practical operating checklist?
- Instrument conversations, generations, workflow steps, and tools.
- Configure the generation exporter,
TracerProvider, andMeterProvider. - Verify OTLP routing for both metrics and traces.
- Build panels for generation count, duration, TTFT, tokens, cost, tool calls, and errors.
- Add agent, model, provider, version, and tool dimensions without unbounded labels.
- Test exemplars and trace drill-down with a controlled success and failure.
- Create error-rate, p95-latency, daily-cost, and evaluation-score alerts tied to your SLOs.
- Review redaction, retention, access control, residency, and tenant isolation.
- Recheck Grafana feature names, prices, and metering rules before changing budgets or dashboards.
Frequently Asked Questions
Can I monitor an agent that uses a framework without a Grafana integration?
Yes. Add OpenTelemetry spans around the framework’s workflow, generation, and tool boundaries, then emit generation and metric data through the same providers. The key requirement is consistent context propagation so those custom spans remain connected to the parent request.
Why does a streamed response need both first-chunk and first-token metrics?
A transport may deliver a first chunk before the first usable token is available, and different SDKs define chunks differently. Keeping both measurements lets you distinguish network or framing delay from model tokenization and rendering delay.
How can I keep low-traffic alerting from paging on one request?
Use a minimum request-count condition or a longer evaluation window alongside the percentage threshold. For very small volumes, combine the alert with a synthetic or scheduled probe so there is enough signal to judge a service objective.
What should I retain when privacy rules prohibit storing prompts?
Keep redacted operational metadata, token counts, durations, statuses, model identifiers, and trace relationships while omitting raw prompt and tool content. This still supports latency, cost, and failure-rate analysis, but detailed root-cause work may require a separately controlled, short-lived capture process.
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