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This design gives an engineer a timeline for every execution, searchable events for decisions and failures, and enough consistent metadata to compare latency and outcomes without turning an observability system into an uncontrolled copy of application data.
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What agent observability should show
OpenTelemetry defines a trace as a related set of spans that records a request’s path through components. A span represents an operation with a start time, end time, attributes and, where useful, events or structured log messages. For an LLM-powered agent, the request is usually the complete task submitted by a user or calling application. The trace should let an investigator answer: when did the task start, which model and tools did it use, where did time accumulate, what failed, and which point-in-time events explain the decision?
Use one root span for the agent execution. Create child spans only for work whose timing, outcome or dependency is useful to investigate. Typical children are a model request, a tool execution, a retrieval operation, a database or HTTP call, and a downstream service operation. The resulting parent-child tree makes a multi-step agent run legible without requiring a separate trace for every internal function.
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Name spans for the operation, not for an individual user, prompt or request identifier. Put stable details needed for filtering and comparison in attributes. This keeps names low-cardinality and prevents sensitive or unbounded text from becoming part of indexes. The OpenTelemetry trace semantic conventions describe the general model and naming principles.
The practical test is whether a developer can investigate an incident without adding another round of ad hoc instrumentation. OpenTelemetry’s observability primer expresses 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.”
A reference signal model for an LLM agent
Keep the three kinds of telemetry distinct even though they are viewed together in a backend:
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| Signal | What it represents | How it helps with an agent |
|---|---|---|
| Trace and spans | A connected execution and its timed operations | Shows the critical path, nesting, latency and operation outcome for the root task, model calls, tools, retrieval and services. |
| Structured logs | Point-in-time records carrying execution context | Explains decisions, retries, validation failures, policy checks and other events that do not need a duration. |
| Span events | Point-in-time occurrences attached to a span | Associates a notable occurrence directly with the operation that was active when it happened. |
A span answers “how long did this operation take?” A log or event answers “what happened at this moment?” Do not replace a long-running tool span with a series of free-form log lines, and do not create a new span for every debug message. The logging specification explains how OpenTelemetry logs can carry trace context, including trace ID and span ID, so records can be correlated with the operation that produced them: OpenTelemetry Logging.
Build the instrumentation in six stages
1. Define the agent execution boundary
Start the root span when the application accepts an agent task, not when a later model client happens to run. End it when the task returns a result or fails permanently. This boundary should include orchestration overhead such as planning, retries, tool selection and final response assembly, because those costs affect the user-visible outcome.
Give the root span a stable operation name such as an application-level agent execution operation. Do not put the user’s name, request ID, prompt text or generated answer in that name. If the same endpoint can launch different workflows, use a bounded workflow or operation identifier as an attribute and keep the span name reusable across requests.
- Record whether the execution completed, failed, was cancelled or exceeded a deadline.
- Record timing for the complete execution and preserve the active context while child operations run.
- Make the root span survive asynchronous boundaries used by your framework; otherwise child spans and logs may become disconnected.
The root span is the anchor for every later query. If it is missing or ends before the final response, backend searches will show an incomplete agent run even when individual model and tool spans exist.
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2. Add child spans around meaningful work
Instrument operations that have meaningful duration, a dependency boundary or a distinct failure mode. A model request should normally be one span per call. A tool span should cover the call from argument validation through the tool’s returned result or error. A retrieval span should cover the retrieval request and response processing that belongs to that operation. Create spans for downstream services when they are independently useful to diagnose.
Use the parent context from the root execution so the backend renders a tree. Avoid wrapping every helper function: excessive spans increase volume and make the trace harder to read. Conversely, a single span for an entire multi-tool plan hides the operation that consumed time or failed.
- Choose names that describe the operation type, such as a model request, retrieval, tool execution or service call.
- Attach stable attributes that let you compare operations, such as a bounded operation category, outcome and other non-sensitive dimensions supported by your instrumentation.
- Keep request-specific content out of names. Treat attributes as indexed data that may be retained longer and exposed to more operators than the original application payload.
- Close each span on every success, error, timeout and cancellation path so duration and status remain accurate.
For retries, decide whether each attempt needs its own child span. Separate attempt spans make backoff and per-attempt latency visible; a parent operation can represent the logical model or tool request. Apply the same rule consistently so dashboards do not mix logical operations and attempts under an ambiguous name.
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3. Emit structured logs and preserve context
Use structured records for events that explain decisions or failures without representing a duration: a tool was rejected by policy, a response failed schema validation, a retry was scheduled, a fallback model was selected, or a circuit breaker opened. Emit fields as typed key-value data rather than concatenating a sentence and asking a collector to guess its structure.
While the root or a child span is active, the logging integration should attach the current trace ID and span ID. A log search can then lead directly to the span that was active when the event occurred. If your application reads existing log files, the collection path may require parsing; direct structured emission or a supported logging integration avoids the ambiguity of parsing free-form text.
Choose a small, documented event vocabulary. For example, use a bounded event name for a validation failure and separate fields for the validation stage, result category and safe diagnostic code. Do not place a full prompt, completion or tool result into a field merely because the logger accepts arbitrary strings. If an operator needs a payload to reproduce a problem, use a separately governed application store and place only a reference or redacted summary in telemetry.
4. Apply current semantic conventions
Semantic conventions provide common names and meanings so instrumentation and backends interpret fields consistently. Start with the generic conventions that apply to your operations, then check the current OpenTelemetry GenAI convention source for model, agent and tool fields. The conventions index currently displays version 1.44.0, but GenAI conventions are maintained separately and continue to evolve; treat a registry snapshot as a versioned dependency, not a permanent list of attributes. See OpenTelemetry semantic conventions 1.44.0.
Before adding a custom attribute, check whether a convention already defines the concept and its type. If a framework’s instrumentation emits a field with a different name or meaning, map it deliberately rather than producing two nearly identical attributes. Record the convention version and instrumentation version in your engineering documentation so a later schema change can be explained.
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5. Export through OTLP and verify the complete path
Send traces and logs through the OpenTelemetry Protocol (OTLP) to a destination that supports the signals you emit. During development, the OpenTelemetry GenAI walkthrough demonstrates Aspire Dashboard as a free, open-source local viewer for traces, metrics and structured logs. A local viewer lets you inspect the shape and correlation of telemetry before a production retention policy is involved.
In production, select an OTLP-compatible service or self-managed deployment that fits your hosting, retention, access-control and operational requirements. The protocol gives you a transport and data model; it does not decide where data should be retained or who may query it.
- Start the application with a deliberately small test task that exercises one model call and one safe tool or retrieval operation.
- Confirm that the exporter sends the root span and that the child spans appear under it in the viewer.
- Open a structured log record and verify that its trace ID and span ID resolve to the expected operation.
- Force a controlled validation failure or timeout and check that the failed span, diagnostic event and root outcome are all visible.
- Repeat the test with content capture disabled, then inspect the exported payload or backend view to ensure prompts and tool content are absent.
Do not consider instrumentation complete because the application starts without exporter errors. The useful test is end-to-end: a trace can be opened, its logs can be found, and an operator can distinguish a model delay from a tool or downstream-service delay.
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The May 2026 GenAI walkthrough describes metadata capture as the default example and shows that opting into content can add prompts, system prompts, tool schemas, arguments and results to telemetry. Those fields may contain personal data, credentials, proprietary documents or other sensitive material. Metadata-only capture is the safer starting point.
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Before enabling any content field, document why it is needed, who may view it, how long it will be retained and how it will be filtered. Apply controls at the instrumentation boundary where possible, rather than exporting raw content and hoping a backend query hides it.
- Filter: remove secrets, authentication material, personal identifiers and regulated data before export.
- Truncate: cap lengths for error details and diagnostic excerpts so one large document cannot dominate storage.
- Classify: distinguish low-risk metadata from prompt, completion, system-instruction and tool-payload content.
- Restrict access: separate developers who need timing and error data from operators authorized to see approved payload samples.
- Set retention: keep high-volume operational metadata for the period needed to troubleshoot, and use a shorter, stricter policy for any approved content.
- Audit changes: review and record who enabled content capture, which fields were enabled and when the setting was reverted.
Sampling is not a privacy control by itself. A sampled prompt can still contain a secret, and a single captured tool result can expose an entire customer record. Treat sampling as a cost and volume control after content policy has been established.
Choosing an instrumentation path
Most agent stacks fall between two approaches: instrumentation supplied by the framework and instrumentation added externally with OpenTelemetry APIs or integrations. Neither is universally best. Evaluate both against the operations your agent actually performs.
| Decision axis | Built-in framework instrumentation | External OpenTelemetry instrumentation |
|---|---|---|
| Setup effort | Often quicker when the framework already creates model, tool and retrieval telemetry. | Requires more implementation work to place spans and context at the right boundaries. |
| Control over emitted data and destination | Depends on the framework’s configuration and exporter support. | Usually gives direct control over span boundaries, filtering and OTLP destinations. |
| Convention alignment | Check which semantic-convention version and GenAI fields the framework emits. | Lets you choose and map fields, but you must maintain that mapping as conventions evolve. |
| Coverage of framework tools | May automatically cover framework-managed calls while missing custom code. | Can cover custom tools and services when you add the relevant boundaries. |
| Duplicate-instrumentation risk | Can overlap with an external integration for the same model or tool call. | Requires an inventory of existing instrumentation to avoid duplicate spans and logs. |
Inspect one representative execution before enabling both paths. If the same model call appears twice, decide which instrumentation owns the span and disable or filter the other. Duplicate telemetry inflates cost, confuses latency analysis and can expose content through the less restrictive path.
Design names, attributes and events that remain useful
Keep names bounded and stable
A span name should remain the same for the same kind of operation across users and requests. Use the operation category and, where needed, a bounded workflow variant. Never interpolate a prompt, URL with arbitrary query text, user identifier or tool argument into a name.
Put comparison dimensions in attributes
Attributes are appropriate for stable details used to filter and compare operations, provided they are supported by the applicable semantic conventions and approved by your data policy. Examples of dimensions to evaluate include operation outcome, model or tool category, retry attempt, deployment environment and a bounded workflow name. Keep values low-cardinality where possible; an attribute containing an unbounded document ID or full URL can create expensive indexes and reveal sensitive relationships.
Use events for point-in-time explanations
Attach an event to a span when the occurrence belongs tightly to that operation, such as a schema-validation failure or a retry decision. Use a structured log when the event should be searchable independently across executions. In both cases, include a short, stable event name and structured fields rather than a raw payload.
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If an incident workflow requires examining a complete prompt or tool result, store that material in a controlled system with its own authorization and retention policy. Put a non-sensitive correlation reference, classification and reason code in telemetry. This preserves the ability to find the relevant record without making every telemetry reader a payload reader.
Correlating logs with traces in practice
Correlation works only while context is propagated. The application must make the active trace and span context available to the logger at the point the record is emitted. Verify this across asynchronous tasks, worker queues and callbacks; a context that is lost at a queue boundary produces logs with no usable parent even though the original request was instrumented.
When investigating a slow answer, open the root trace first. Follow the longest child span, then inspect logs attached to that span for retries, rate-limit responses, validation failures or tool decisions. If a log has the correct trace ID but an unexpected span ID, check where the logger context was switched; the event may have been emitted after the operation ended or from a detached background task.
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For records read from files, parsing must reliably extract the structured fields and context identifiers. Prefer a logging integration that emits structured records directly when available. If file parsing is unavoidable, test malformed lines, multiline exceptions and rotation behavior, because one parser failure can remove the very diagnostic event needed to explain an incident.
Selecting an OTLP destination
Compare destinations by capability rather than by an assumed vendor ranking. The OpenTelemetry walkthrough establishes OTLP compatibility and the local Aspire Dashboard example; it does not establish current commercial pricing or partner terms.
| Requirement | Questions to answer before adoption |
|---|---|
| Protocol and signal support | Can the destination receive the OTLP signals you emit, including traces and structured logs? |
| Investigation workflow | Can an operator open a trace and search the correlated logs from the same execution? |
| Deployment model | Is a local, self-managed or hosted deployment compatible with your network and ownership requirements? |
| Retention | Can retention differ for metadata and any approved content, and can policies be verified? |
| Access control | Can access to payload-bearing records be restricted separately from timing and error metadata? |
| Operational fit | Can your team monitor exporter health, ingestion limits, storage growth and schema changes? |
Use a local dashboard during development to catch naming, context and privacy mistakes early. Promote the same OTLP-shaped telemetry to production only after reviewing sampling, redaction, access and retention settings for the production backend.
Failure modes and fixes
The trace contains only one large span
Cause: instrumentation was added only at the request boundary, or child operations run outside the active context.
Fix: add children around model, tool, retrieval and downstream-service work, then verify context propagation through asynchronous code.
Logs cannot be found from a trace
Cause: the logger is not receiving the active trace and span context, or a file parser is dropping structured fields.
Fix: inspect one emitted record for trace ID and span ID, use a supported logging integration where possible, and test parser behavior with multiline and rotated logs.
Latency appears duplicated
Cause: built-in and external instrumentation both created spans for the same operation.
Fix: inventory instrumentation sources, assign one owner for each boundary, and remove or filter duplicate records.
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Telemetry contains sensitive content unexpectedly
Cause: a framework enabled content capture, an exception field includes a payload, or a logger serializes the request object.
Fix: disable content capture, add boundary-level filtering and truncation, rotate or restrict affected data according to your incident policy, and inspect exported payloads rather than relying on UI redaction.
Conventions do not match across components
Cause: different libraries use different schema versions or custom names for the same concept.
Fix: document the convention version, map fields at an integration boundary and review the current generic and GenAI convention sources when upgrading.
Exporter errors are hidden by the application
Cause: telemetry delivery is treated as best-effort logging with no health signal.
Fix: monitor exporter and collector health separately from business success. A successful agent response does not prove that its trace and logs were delivered.
A staged rollout that limits risk
Stage 1: metadata-only development
Instrument the root execution, model calls, tools, retrieval and downstream services. Export to a local OTLP-compatible viewer. Capture timing, bounded operation attributes, status and context identifiers, but no prompts, completions, system instructions or tool payloads.
Stage 2: correlation and failure tests
Exercise successful runs, model timeouts, tool errors, validation failures, retries and cancellation. Confirm that every case produces a complete root outcome, useful child spans and searchable structured logs. Check asynchronous workers and queue boundaries explicitly.
Stage 3: convention and volume review
Compare emitted fields with current semantic conventions, remove duplicate instrumentation, review span cardinality and estimate telemetry volume under realistic concurrency. Keep names stable and avoid adding unbounded identifiers as indexed attributes.
Stage 4: production controls
Choose the production OTLP destination, configure retention and access policies, monitor exporter health and document who can change content-capture settings. Treat schema and instrumentation upgrades as observable changes that require a small regression run.
Stage 5: narrowly justified content samples
If metadata cannot answer a demonstrated diagnostic question, enable the smallest possible content field for the shortest justified period. Filter and truncate before export, restrict access, document the change and verify that disabling it removes content from subsequent telemetry.
The resulting operating model
A useful agent trace is not a transcript. It is a navigable execution record: one root span for the user-visible task, child spans for operations that matter, and structured events that explain what happened at a point in time. Semantic conventions make those records portable across components, while trace and span context make logs searchable from the same investigation.
OTLP provides a consistent path to a local viewer or production backend, but the engineering decisions remain yours: operation boundaries, field names, sampling, access, retention and content policy. Start with metadata, prove end-to-end correlation, and add payload capture only when its diagnostic value outweighs its security and governance cost.
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