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Build agent observability around one trace for each user-visible agent execution, then add child spans for model calls, tool runs, retrieval, and downstream services. Emit structured logs with the active trace and span IDs, export both through OTLP, and keep prompts and tool content out of telemetry unless you have explicitly approved, filtered, access-controlled, and retention-limited capture.
This design lets an engineer answer practical questions—Which step was slow? Which tool failed? What did the agent decide immediately before the failure?—without turning every prompt, system instruction, or tool result into an uncontrolled data store.
What agent observability should show
OpenTelemetry defines a trace as a connected set of spans that records the path of an operation through a system. A span represents one operation, with timing, attributes, and optional events or structured messages. In an agent, the useful operation boundary is the complete task accepted from the user, not an individual model request.
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OpenTelemetry’s primer describes the goal this way: “An application is properly instrumented when developers don’t need to add more instrumentation to troubleshoot an issue, because they have all of the information they need.” See the OpenTelemetry observability primer for the broader model.
Design the trace for one agent execution
Use a stable root span
Create one root span for each user-visible execution, such as agent.execute or agent.run. The name describes the operation, never a user, prompt, ticket number, or other request-specific value. Stable names make latency and failure comparisons useful in a backend.
Put filterable, relatively low-cardinality facts in attributes: the agent workflow or version, model-provider category, deployment environment, result status, retry count, and whether a tool was invoked. Follow applicable OpenTelemetry semantic conventions instead of inventing a different spelling for every service.
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| Child operation | What the span establishes | Useful metadata |
|---|---|---|
| Model request | When a model call started, how long it took, and whether it succeeded | Model identifier, provider category, request type, response status, retry number, and usage fields when supported by your instrumentation |
| Tool execution | Which tool operation consumed time or failed | Stable tool name, validation outcome, timeout or error category, and result status |
| Retrieval | Where retrieval latency and failures occurred | Retriever or index identifier, result count, filter mode, and status |
| Downstream service call | External work performed on the agent’s behalf | Service role, operation name, status code category, and timeout state |
| Planning or handoff step | Distinct orchestration work that deserves timing and failure visibility | Workflow version, branch outcome, and bounded counters |
Use the same operation name for equivalent work across requests. Do not put a full URL, user ID, prompt text, tool arguments, or a unique request token in a span name. Those values either belong in carefully governed attributes or in filtered logs, and high-cardinality values can make queries expensive and confusing.
The OpenTelemetry trace semantic conventions explain span structure and naming principles. A span can also contain point-in-time events, but events do not replace child spans when an operation has its own duration, outcome, or nested work.
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Keep structured logs connected to the trace
Separate timed operations from events
Spans answer “how long did this operation take and what was its outcome?” A structured log or span event answers “what happened at this moment?” Use logs for validation failures, policy decisions, tool-selection decisions, retries, circuit-breaker changes, and other diagnostic events that do not need their own duration.
Emit records as structured fields rather than free-form sentences. A useful record has a severity, timestamp, event name, concise message, outcome, and bounded attributes. Keep the event name stable—for example, tool.validation_failed—and put changing values in fields.
Preserve trace and span context
When a log is emitted while a span is active, include the active trace ID and span ID. This correlation lets an engineer open a trace and find the exact log records generated during a model call or tool run. OpenTelemetry’s logging specification describes this context relationship and integration with existing logging libraries in OpenTelemetry Logging.
If your pipeline reads files, parsing is an additional failure point: multiline messages, missing fields, and inconsistent formats can break correlation. Prefer emitting structured records directly through a supported logging integration or the Logs API. If file collection is unavoidable, define and test a strict format that carries the context identifiers on every record.
{
"timestamp": "2026-10-16T12:00:00Z",
"severity": "WARN",
"event.name": "tool.validation_failed",
"trace_id": "active-trace-id",
"span_id": "tool-span-id",
"tool.name": "calendar.lookup",
"error.type": "invalid_arguments",
"retryable": false
}The record above is an illustrative shape, not a requirement to copy these exact field names where a current semantic convention provides a better one. Keep identifiers and metadata that are safe to retain; do not add the rejected arguments merely because they would make a log look more complete.
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Apply current semantic conventions
Semantic conventions give common names and meanings to telemetry fields so instrumentation and backends can interpret data consistently. Use generic conventions for common operations and the current GenAI conventions for model and agent-specific data where they apply.
The semantic-conventions index displays version 1.44.0 at the time of access, but that number is a version marker, not a permanent contract. The index points GenAI guidance to a separate repository, so check the current GenAI source rather than treating an older registry page as complete. Start with OpenTelemetry semantic conventions.
Agent-specific conventions are still evolving. OpenTelemetry’s 2025 discussion, AI Agent Observability – Evolving Standards and Best Practices, covers built-in and external instrumentation and the continuing standardization work. Record which convention version and instrumentation package your service uses, and review changes when you upgrade.
Build the instrumentation in an explicit sequence
- Define the boundary. Start the root span at the request handler, queue consumer, or other component that accepts the agent task. End it on success, cancellation, timeout, or uncaught failure. Ensure every exit path closes the span.
- Choose stable names. Name the root and child spans by operation, such as
agent.execute,llm.request,retrieval.search, ortool.execute. Keep user and request-specific values out of names. - Instrument meaningful child work. Wrap model requests, tool executions, retrieval, planning branches with real work, and downstream calls. Do not create a span for every trivial helper function; excessive spans obscure the execution and increase storage.
- Set outcomes on every span. Record success, expected rejection, cancellation, timeout, or error. Attach an error type and safe diagnostic detail. Preserve exception information according to your language’s OpenTelemetry integration.
- Record bounded attributes. Add dimensions needed to filter and compare, such as workflow version, model identifier, tool name, environment, retry number, and status category. Avoid raw prompts, completions, credentials, unrestricted tool arguments, and high-cardinality identifiers by default.
- Emit decision and failure logs. Log events such as policy denials, tool-validation failures, retries, fallback selection, and downstream errors. Emit them while the relevant span is active so trace and span IDs are attached automatically.
- Export through OTLP. Configure a traces-and-logs exporter that sends to an OTLP-compatible endpoint. During development, point it at a local viewer; in production, use a destination whose retention, access controls, deployment model, and operating cost fit your requirements.
- Verify end to end. Run a successful task, a tool failure, a timeout, a retry, and a cancellation. Open each trace, confirm child-span timing and outcomes, and search for correlated logs using the trace ID and span ID.
A language-neutral control flow looks like this:
root = tracer.start_span("agent.execute")
try:
add_safe_attributes(root, workflow_version, environment)
result = run_agent(root) # creates child spans for model, retrieval, tools
root.set_status("OK")
return result
except Cancelled as error:
root.set_status("CANCELLED")
logger.info("agent.cancelled", exc_info=error)
raise
except Exception as error:
root.record_exception(error)
root.set_status("ERROR")
logger.error("agent.failed", error_type=classify(error), exc_info=error)
raise
finally:
root.end()Adapt the lifecycle to your framework’s API. The important behavior is that the root span encloses the complete execution, child spans enclose real operations, and logs inherit the active context.
Choose an instrumentation path
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| Decision axis | Built-in framework instrumentation | External OpenTelemetry instrumentation |
|---|---|---|
| Setup effort | Often quicker when your framework already emits spans and logs | Requires wiring providers, context propagation, and instrumentation around framework operations |
| Data control | Field names and captured content may be determined by framework defaults | More direct control over attributes, filters, processors, and destinations |
| Convention support | Depends on the framework’s release and schema implementation | Lets you target current conventions, but you must maintain the mapping |
| Coverage | May understand framework-specific model, tool, and agent events automatically | Can cover custom tools and orchestration that the framework does not know about |
| Duplication risk | Can overlap with another library already instrumenting the same calls | Can create duplicate spans if built-in instrumentation remains enabled |
| Destination flexibility | Varies by framework and configuration | Typically routes through standard OpenTelemetry exporters and OTLP |
Inventory what your framework already emits before adding a second layer. If one model request appears twice in a trace, disable one instrumentor or configure a clear parent-child relationship. Check the framework’s current documentation for supported versions and fields; coverage changes as agent conventions evolve.
Export traces and logs through OTLP
OpenTelemetry’s GenAI walkthrough states that any OTLP-compatible backend can receive GenAI telemetry. A local development path is Aspire Dashboard, described in that walkthrough as a free, open-source viewer with trace, metrics, and structured-log views. The walkthrough is available at Inside the LLM Call: GenAI Observability with OpenTelemetry.
Use a local dashboard to check instrumentation before sending data to a shared or hosted system. For a production destination, evaluate:
- OTLP support: whether it accepts the protocol and signal types you emit, including traces and logs.
- Cross-signal navigation: whether a trace can be opened and its related log records found without manual ID copying.
- Deployment model: hosted service, self-managed installation, or a hybrid that matches your network and residency requirements.
- Retention and deletion: how long each signal is stored, how deletion requests work, and whether logs and traces have separate policies.
- Access control: least-privilege roles, tenant or project isolation, audit trails, and encryption.
- Operational fit: alerting, ingestion limits, outage behavior, and the team’s ability to operate the system.
Do not select a destination solely because it displays a trace. Confirm that the logs arrive with context, that failed exports are visible, and that an exporter outage does not block the agent’s user-facing request unless your reliability design explicitly requires that behavior.
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Metadata-only telemetry is the safer starting point. The 2026 walkthrough says its example captures metadata by default; opting into content adds prompts, system prompts, tool schemas, arguments, and results. Those fields can contain personal data, secrets, customer records, proprietary instructions, or regulated information.
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Before enabling content capture, document the purpose, permitted fields, owners, access roles, retention period, and deletion process. Treat content capture as a separate change from basic instrumentation.
Use a capture policy
- Default to off. Capture operation names, timings, statuses, bounded metadata, and correlation IDs without raw content.
- Allow-list fields. If a diagnostic case requires content, specify exactly which field and which workflow may emit it.
- Filter before export. Remove credentials, authentication headers, payment data, personal identifiers, and other sensitive patterns before a record leaves the process.
- Truncate deliberately. Set a maximum size for prompts, completions, arguments, and results. Truncation should be marked so investigators know the value is incomplete.
- Protect access. Restrict content-bearing telemetry to the smallest group and audit access.
- Limit retention. Keep detailed content for the shortest period that supports the approved purpose; retain aggregate timing and outcome data separately when useful.
- Test redaction. Send synthetic secrets and personal data through every model, tool, retry, and error path and verify that filters also handle exceptions and nested payloads.
Even metadata can identify a customer or workflow when combined with timestamps and IDs. Review the entire trace context, not only the prompt field, before treating metadata as harmless.
Validate the implementation with failure-focused checks
Trace completeness
- A successful execution has one root span and expected child spans for each model, retrieval, tool, and downstream operation.
- The root span remains open until the final response, including asynchronous tool work that belongs to that execution.
- Every error, timeout, cancellation, and retry has an explicit outcome rather than appearing as an unexplained gap.
Log correlation
- Open a child span and find its validation, retry, or failure logs by trace ID or span ID.
- Confirm that logs emitted from worker threads, callbacks, and asynchronous tasks preserve context; missing context often indicates a propagation boundary.
- Check that a file collector or parser has not dropped IDs, severity, timestamps, or multiline records.
Data-safety checks
- Inspect traces generated by prompts containing fake credentials, personal data, and long tool results.
- Verify that rejected arguments and exception messages are filtered as aggressively as successful payloads.
- Confirm that a content-capture configuration change is reviewed and separately approved from routine telemetry changes.
Performance and volume checks
- Measure telemetry overhead during long agent runs and high-concurrency tool execution.
- Watch for unbounded attributes, unique span names, duplicate instrumentation, and logs emitted inside tight token or polling loops.
- Define behavior when the collector or backend is unavailable: buffering, sampling, dropping low-value records, or failing closed for sensitive content.
Common failure modes and fixes
One giant span hides the problem
If the trace contains only the root span, add child spans around model calls, retrieval, tools, and downstream services. Keep the root for the user-visible outcome, but do not force investigators to infer every duration from one total.
Every span has a unique name
Replace names containing user text, IDs, URLs, or timestamps with stable operation names and bounded attributes. This restores useful grouping and prevents an accidental high-cardinality index.
Logs appear in a separate search
Check context propagation at the logger call site and across asynchronous boundaries. Then check the exporter and collector mapping for trace and span IDs. A log that was emitted without active context cannot be correlated later unless your application adds a safe reference explicitly.
Two copies of each operation appear
List enabled framework and external instrumentors. Disable one overlapping integration or configure one as the authoritative source. Duplicate spans inflate volume and can make a single model call look like two requests.
Investigators demand prompts in every trace
Do not enable global content capture as a shortcut. Use a narrowly scoped, reviewed diagnostic mode with field allow-lists, redaction, truncation, restricted access, and short retention. Return to metadata-only capture when the investigation ends.
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A practical operating checklist
- One stable root span represents each user-visible agent execution.
- Child spans cover model requests, retrieval, tools, and consequential downstream work.
- Span names describe operations; stable attributes carry filterable metadata.
- Structured logs carry the active trace ID and span ID.
- Spans represent timed work; logs or events represent point-in-time occurrences.
- Generic and GenAI semantic conventions are checked against their current sources.
- Only one instrumentation layer owns each operation.
- Traces and logs are exported through OTLP and can be explored together.
- Metadata-only capture is the default; content capture has an explicit policy.
- Success, failure, timeout, retry, cancellation, redaction, and exporter-outage paths are tested.
When these conditions hold, an engineer can move from a user-visible failure to the responsible operation and its diagnostic events without collecting more sensitive content than the investigation requires.
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