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Instrument an AI agent by tracing each meaningful boundary—its overall invocation, model calls, planning steps, tool executions, workflows, and memory operations—then export the telemetry over OTLP and inspect it in Grafana. Use OpenTelemetry GenAI semantic attributes, preserve parent-child context across operations, and decide how to handle sensitive prompt and response content before capture is enabled. Grafana Agent Observability can organize generations into conversations and support cost, quality, and evaluation workflows; its OpenTelemetry ingestion is documented as public preview, so check its current status before relying on it in production.
What should AI-agent instrumentation show?
A useful trace should answer more than “how long did the request take?” It should let you follow one agent run through the model inferences, decisions, tools, memory operations, retries, and delegated work that contributed to its result. Metrics add trends across runs: operation duration, inference and tool-call counts, token usage, time to first token, and errors.
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Map the agent’s work before adding spans
Start by listing the actual boundaries in your system. A simple agent might have one invocation, several model calls, and a tool call. A more involved system may also decompose tasks, consult memory, run a workflow, or delegate work to other agents. Add telemetry at the operations that help explain behavior, not just at framework or network boundaries.
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OpenTelemetry guidance describes instrumentation as code in system components emitting traces, metrics, and logs. In practice, a framework’s built-in instrumentation is a good starting point only if it captures the operations and GenAI attributes you need. Otherwise, supplement it with an instrumentation library or manually create spans at the missing boundaries.
Which operations should be spans?
Use a parent span for the in-process agent run and child spans for operations performed during that run. For coordinated work across agents, represent the larger process as a workflow and keep the delegated agents’ relationships in the trace so an operator can follow the complete path.
| Boundary | Suggested operation name or example | Why it belongs in the trace |
|---|---|---|
| One agent invocation | invoke_agent |
Establishes the end-to-end boundary for a single in-process run and provides the context for its child work. |
| Coordinated multi-agent process | invoke_workflow |
Shows the broader process while retaining child relationships for delegated agents. |
| Model inference | chat, generate_content, or text_completion |
Separates inference timing and errors from the agent’s other work. |
| Tool execution | execute_tool |
Shows which tool was called and how that operation contributed to the run. |
| Memory operation | search_memory, create_memory, update_memory, upsert_memory, or delete_memory |
Makes memory access and changes visible as distinct operations. |
| Planning or task decomposition | Planning or task-decomposition span | Helps distinguish time spent reasoning about work from time spent on inference or external tools. |
These names describe operation boundaries; they are not a requirement to use a particular agent framework. OpenTelemetry’s GenAI conventions include operation names such as invoke_agent, invoke_workflow, and execute_tool. Microsoft’s example hierarchy also illustrates how nested agent, inference, tool, planning, and memory work can be related. Choose names and boundaries that match the operations your code actually performs.
How do I instrument the agent step by step?
Implement instrumentation in an order that makes the trace useful before adding every possible attribute. First establish the invocation boundary and context propagation; then add child operations, attributes, metrics, and a tested export route.
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- Inventory the operations. Identify the agent entry point, model calls, planning or decomposition, tools, workflows, memory actions, and any delegated agents. Decide which operations must be visible to troubleshoot a slow or incorrect run.
- Choose the instrumentation source. Check whether your framework’s native instrumentation emits the spans and GenAI attributes you need. Grafana cautions that generic OpenTelemetry instrumentation may not capture AI calls or their GenAI attributes. If the framework leaves gaps, add an instrumentation library or create spans manually.
- Create the invocation span. Represent one in-process run with an
invoke_agentspan. When the run coordinates multiple agents as a larger process, use aninvoke_workflowboundary and retain the child relationships. The invocation span is the natural scope for end-to-end duration and invocation-level counts. - Add child spans at meaningful boundaries. Trace each model inference, tool execution, planning step, and memory operation. Keep the parent-child relationships intact so a trace can explain which operations belonged to the same invocation. For delegated agents, preserve the relationship to the coordinating workflow rather than presenting each agent as an unrelated request.
- Set GenAI semantic attributes. At minimum, record
gen_ai.operation.name,gen_ai.provider.name, andgen_ai.request.modelwhere they apply. Use operation names that reflect the boundary, such aschatfor a chat inference orexecute_toolfor tool execution. Consistent names make it easier to interpret and query data across implementations. - Record operational metrics. Capture operation duration, token usage, time to first token, inference-call counts, tool-call counts, errors, and model or provider identity. The development conventions define
gen_ai.invoke_agent.duration,gen_ai.invoke_agent.inference_calls, andgen_ai.invoke_agent.tool_calls. Scope the inference and tool counts to the agent invocation so they are not double-counted across a call tree. - Make a content-capture decision. Before enabling input or output messages, system instructions, tool definitions, arguments, or results in telemetry, decide what should be filtered, truncated, redacted, or excluded. These fields can contain sensitive or personally identifiable information, and the OpenTelemetry conventions explicitly warn about the risk.
- Export through OTLP. Route telemetry through your existing OpenTelemetry Collector or Grafana Alloy deployment if you have one. Verify that the route preserves the span and resource attributes Grafana needs. Grafana’s Python guide uses HTTP/protobuf exporter settings; use the configuration appropriate to your own language and existing pipeline rather than assuming one exporter setup fits every deployment.
- Inspect and refine in Grafana. Confirm that a single run appears as a connected trace, that its model and tool operations are distinguishable, and that expected attributes and metrics are present. Then use trace search, dashboards, and alerts to investigate failures, loops, slow tools, latency, errors, and token or cost trends.
Which attributes and metrics matter most?
Start with a small, consistent set that makes operations identifiable and measurable. Add detail only when it helps answer a production question, and treat message content as a separate privacy decision rather than a default debugging field.
| Telemetry item | What it helps you answer | Implementation note |
|---|---|---|
gen_ai.operation.name |
What kind of GenAI operation is this? | Use a name that matches the boundary, for example chat, invoke_agent, or execute_tool. |
gen_ai.provider.name |
Which provider handled the operation? | Record provider identity consistently for relevant model operations. |
gen_ai.request.model |
Which requested model was involved? | Keep model identity available for investigating latency, errors, or behavior across versions. |
gen_ai.invoke_agent.duration |
How long did the agent invocation take? | Defined by the current development conventions as end-to-end duration for one agent invocation. |
gen_ai.invoke_agent.inference_calls |
How many inference calls were in the invocation? | Scope to the agent invocation to avoid counting the same work repeatedly across the call tree. |
gen_ai.invoke_agent.tool_calls |
How many tool calls were in the invocation? | Use the same invocation-level scoping principle as the inference-call count. |
| Token usage, time to first token, errors, and operation duration | Where are usage, responsiveness, and failure patterns changing? | Capture the signals relevant to your operations; do not assume every framework emits them automatically. |
The three named gen_ai.invoke_agent.* metrics are described in development conventions, so confirm the conventions and instrumentations your environment supports. The named metrics define boundaries and signals, not benchmark results. The official material cited here does not establish a general instrumentation-overhead figure, latency reduction, accuracy gain, or cost saving.
How should I handle prompts, responses, and tool data?
Treat telemetry content as potentially sensitive. Inputs, outputs, system instructions, tool definitions, tool arguments, and tool results may contain personal information or secrets; capturing them can turn an observability system into another store of sensitive data.
Decide before rollout whether to omit content, redact selected values, filter fields, or truncate captured text. Set retention and access expectations for the resulting telemetry as part of the same decision. You can often troubleshoot structure and performance with operation names, model and provider identity, timings, errors, and call counts without retaining full prompt and response bodies.
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Do not assume a provider or framework automatically removes sensitive content. The OpenTelemetry GenAI conventions warn about content capture and support filtering or truncation, but the right controls depend on what your agent handles and what your telemetry pipeline retains.
How do I get the telemetry into Grafana?
Export OpenTelemetry data over OTLP and use the Collector or Grafana Alloy route that fits your deployment. The important checks are that the data arrives, context relationships remain usable, and the span and resource attributes needed for investigation survive the pipeline.
Keep the route you already operate when possible
If your services already send OTLP through an OpenTelemetry Collector or Grafana Alloy, extend that route rather than creating an unrelated path solely for the agent. This keeps routing and operational ownership aligned with your existing telemetry setup. Grafana’s Python example documents HTTP/protobuf exporter settings, but the specific setup depends on your application language and deployment.
Verify the result in Grafana
Start with a known agent run and check whether you can follow the invocation through its child inference, tool, planning, and memory spans. Confirm the provider, requested model, and operation attributes appear where expected; compare the invocation’s duration and call counts with the work represented in the trace. Missing child operations usually point to incomplete instrumentation or lost context, while absent GenAI attributes may mean the instrumentation is generic rather than AI-aware.
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Grafana Agent Observability can organize generations into conversations and connect generations with traces and metrics. Grafana documents views for versions, prompts, tools, latency, errors, quality scores, and cost trends, as well as online guards and evaluations and offline experiments for comparing prompts, models, and agent versions. Its OpenTelemetry ingestion is currently documented as public preview; confirm current availability and assess possible breaking-change risk before making it a production dependency.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should I choose an instrumentation approach?
Choose based on coverage and operational fit, not merely on whether an option can emit a trace. A framework-native integration may be quick to adopt, while manual spans can fill gaps; either approach is incomplete if context breaks or the GenAI attributes needed for diagnosis are absent.
| Decision area | What to check |
|---|---|
| Coverage | Are model calls, planning, tools, workflows, memory, streaming, and delegated agents represented where relevant? |
| Standards alignment | Are gen_ai.* attributes and operation names emitted consistently? |
| Context propagation | Can you follow a request across services, agents, tools, and retries? |
| Data controls | Can prompts, responses, tool arguments, and results be filtered, truncated, redacted, or excluded? |
| Backend and operations | Can OTLP use the existing Collector or Alloy route, and do dashboards, alerts, retention, residency, and access controls fit your needs? |
| Evaluation | Does the selected backend support the cost, quality, guard, or offline experiment workflows you need? |
There is no universal instrumentation-overhead or performance-improvement number established by the official material described here. Evaluate overhead in your own environment if it is a deployment concern, and assess the observability approach against the agent’s actual workload and data-handling requirements.
What should I troubleshoot first when traces are incomplete?
Use the trace’s shape to narrow down the problem. A trace can arrive at Grafana and still be unhelpful if operations were never instrumented, context was not propagated, or the attributes required for interpretation were omitted.
- The agent appears, but model calls do not: Check whether the framework instrumentation covers model inference or only general application activity. Add AI-aware instrumentation or a model-call span and its GenAI attributes if needed.
- Operations appear as unrelated traces: Check parent-child context across the agent, tools, services, and delegated agents. The intended invocation or workflow relationship needs to survive the boundaries between components.
- Calls are visible but hard to distinguish: Check
gen_ai.operation.nameand use consistent operation names that identify inference, tool execution, agent invocation, or workflow work. - Counts seem inflated: Check whether invocation-level inference and tool counts are being recorded at multiple levels of the call tree. The development conventions say to scope these counts to the agent invocation to avoid double-counting.
- Prompts or tool results create a data concern: Revisit the capture policy and apply filtering, truncation, redaction, or exclusion rather than treating full content as necessary for every trace.
- Telemetry reaches the backend but useful fields are missing: Inspect the instrumentation and export route for the required span and resource attributes; generic instrumentation may not supply GenAI attributes, and a pipeline must preserve the fields Grafana needs.
How do I turn traces into operational improvements?
Use trace search to identify failed requests, loops, slow tools, and unexpected model behavior. Then pair those investigations with dashboards and alerts that track latency, errors, token or cost trends, and tool-call rates. The trace explains an individual execution; aggregated metrics help reveal whether a pattern is recurring.
For teams using Agent Observability, Grafana also documents online guards and evaluations and offline experiments for comparing prompts, models, and agent versions. These workflows can help make changes measurable, but they do not replace trace coverage: an evaluation score alone does not show which inference or tool operation caused a particular production failure.
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Frequently Asked Questions
Does generic OpenTelemetry instrumentation automatically trace AI model calls?
Not necessarily. Grafana warns that general-purpose instrumentation may miss AI calls and the GenAI attributes needed to identify them, so verify emitted operations and attributes rather than judging by whether any traces appear.
Is Grafana Agent Observability ready for production OpenTelemetry ingestion?
Grafana documents OpenTelemetry ingestion for Agent Observability as public preview. Preview status can involve availability or breaking-change risk, so verify current support and operational suitability before adopting it as a production dependency.
Can I use an OpenTelemetry Collector instead of Grafana Alloy?
The instrumentation sequence supports routing OTLP through an existing Collector or Alloy deployment. Choose the route already operated by your team and verify it preserves the attributes and relationships needed in Grafana.
Do published sources establish how much instrumentation slows an AI agent?
The official material described here defines telemetry signals and boundaries, but does not establish a general overhead benchmark. If overhead matters for your workload, measure it in your own deployment rather than relying on an unsupported universal percentage.
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