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What should LLM observability show?
A useful view connects an end-user operation to the work that produced its response. Create a root trace for the request and related child spans for application handling, retrieval or vector search, each model call, tool execution, retries, and post-processing. This lets an engineer move from an alert or user report to the particular step and execution involved. AWS documents hierarchical traces across these kinds of operations in its agent observability material.
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Observability has three related but distinct jobs:
- Operational monitoring: show whether the service is available and performing acceptably through request volume, errors, latency, usage, and, where reliably calculable, cost.
- Execution tracing: show how an individual request moved through code, retrieval, models, and tools, including parent-child relationships, status, and timing.
- Quality evaluation: assess whether responses are correct, relevant, safe, or otherwise fit for the application. A fast, successful model call is not proof of a good answer.
How should you map and trace a request?
Draw the actual execution path
Start with one representative user request and diagram its path from ingress to response. Include the orchestration framework and application boundaries, retrieval, provider calls, tool execution, retries, asynchronous work, and any post-processing. The diagram should reflect the real request lifecycle, not just the model SDK call.
Make related spans form one trace
Instrument the root user operation and create child spans for each meaningful operation. Propagate trace context through asynchronous work where the framework and transport support it. Record start time, duration, status, trace ID, and parent relationship so a team can reconstruct the sequence. Keep retries visible as distinct operations; otherwise, a long request may look like one opaque model call.
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Do not capture hidden reasoning or raw content simply because tracing makes it possible. Record the identifiers and structured metadata needed to diagnose behavior, subject to the data-handling policy described below.
Which fields should each trace contain?
Use OpenTelemetry where it fits your existing stack, and add GenAI-specific attributes alongside ordinary span data. The OpenTelemetry GenAI semantic conventions provide shared terminology, but exact attributes, mappings, and backend support evolve; validate the convention version and destination behavior you deploy.
| Field group | Useful data | Why it matters |
|---|---|---|
| Trace relationships | Trace ID, span ID, parent span ID, timestamps, duration, and status | Connects one user request to its component operations and exposes the slow or failing step. |
| AI operation | Operation name, provider or system, requested model, and tool or agent operation where applicable | Distinguishes model, retrieval, and tool work and supports analysis by operation. |
| Usage | Input and output token counts when available | Helps explain usage and supports cost estimates when paired with reliable pricing data. |
| Application context | Application version, environment, workflow or feature name, and a privacy-safe request correlation ID | Helps identify changes and compare executions without using a person’s identity as a metric label. |
Keep high-cardinality or user-identifying values out of metric dimensions. Use trace attributes for appropriate request-level context, and restrict sensitive attributes according to policy. AWS’s OpenSearch documentation provides an example of registering an OpenTelemetry trace provider and exporter and adding model and token attributes; treat it as an implementation example, not a universal schema requirement.
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Which operational signals and alerts are useful?
Begin with request count, error rate, end-to-end latency, step latency, provider and model, token counts, and estimated cost when your pricing data makes that estimate reliable. Break down signals by model, provider, application version, route, or environment where privacy and metric cardinality permit.
Build alerts around actionable symptoms rather than every individual bad answer. Useful conditions may include sustained latency changes, error-budget deterioration, provider failures, unusual token or cost spikes, or missing telemetry. Use trace exemplars or equivalent links from metric anomalies to representative executions so responders can inspect what happened.
Dashboards are implementation-specific. For example, LangSmith describes dashboards for token usage, P50/P99 latency, errors, cost breakdowns, and feedback. Those are vendor feature examples, not a universal required dashboard or an independent performance benchmark.
How do you measure answer quality?
Operational health and answer quality answer different questions: metrics reveal whether the system is functioning, while evaluations assess what it produced. Keep a versioned set of representative tasks and known failure cases, and run repeatable offline evaluations when changing prompts, models, retrieval configuration, or tools.
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Combine deterministic checks with semantic review
- Use deterministic checks for crisp requirements such as valid output schemas, required fields, and tool permission constraints.
- Use carefully designed model-based or human evaluation for semantic properties such as relevance, correctness, and safety.
- Evaluate a selected production sample or high-risk flows, and route uncertain or consequential cases to human review.
- Track evaluator agreement and false positives: evaluators can be wrong, too.
Production traces can supply candidate examples for curated evaluation data, but do not turn raw traces into a benchmark without review and appropriate data controls. Datadog documents a workflow for promoting traces into version-controlled datasets and comparing prompts, parameters, models, and agent strategies; LangSmith describes online evaluation as a monitoring option. These are examples of vendor capabilities, not guarantees that evaluation is accurate by itself.
How should you protect prompts and other telemetry?
Treat prompts, retrieved documents, tool arguments and results, and model outputs as potentially sensitive. Decide what information is actually needed for operations or security before capturing content. OWASP’s LLMX Cornucopia guidance, updated 2026-09-20, recommends logging only the minimum AI interaction metadata needed for security monitoring and minimizing and redacting or anonymizing prompt or output content before storage.
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- Omit secrets and unnecessary content; redact or anonymize retained identifiers where feasible.
- Restrict who can view traces, and define retention and deletion rules.
- When feasible, filter or redact in an OpenTelemetry Collector or equivalent controlled gateway before telemetry leaves the application network.
- Review the whole data path, including provider processing, observability destinations, processors, error queues, backups, and support access.
Provider data controls and observability storage are separate. OpenAI’s API data-controls documentation, accessed 2026-10-07, says default abuse-monitoring logs may include prompts and responses and are retained for up to 30 days. Eligible organizations may apply for modified abuse monitoring or zero data retention, subject to limitations and endpoint- or account-specific behavior. That policy does not set retention for traces stored independently by your application or observability provider.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which implementation path fits your team?
There is no single mandatory vendor. Choose based on the platform you already operate and the trace, evaluation, and data controls your application needs. Vendor documentation describes each vendor’s own features; it does not establish an independent head-to-head winner.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitches| Approach | When it may fit | What to verify |
|---|---|---|
| OpenTelemetry with an existing stack | You want common instrumentation and correlation with existing service traces. | How the receiving backend maps GenAI attributes, displays nested traces, and supports the queries and policies you need. |
| Dedicated LLM or agent observability platform | You need specialized nested trace views, annotation, prompt workflows, or online and offline evaluation. | Framework and provider coverage, evaluation workflow, deployment and data controls, export options, and operating cost. LangSmith documents these feature categories and OpenTelemetry integration. |
| Cloud-native observability service | You want the data path, identity, and operations to fit existing cloud infrastructure. | Collector and ingestion architecture, authentication, query model, integrations, access policies, and infrastructure ownership. AWS documents an OpenTelemetry Collector-to-OpenSearch architecture and GenAI agent trace views. |
Datadog’s 2025-12-01 article describes support for OpenTelemetry GenAI conventions v1.37 and later, along with governance processors, trace analysis, and an evaluation dataset workflow. Treat the version statement as dated vendor compatibility information: confirm current SDK, convention, and backend support for your deployment rather than treating it as a permanent minimum.
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Before committing, compare framework and provider coverage; fidelity of nested agent and retrieval traces; metric-to-trace correlation; evaluation and human-review workflow; access controls and privacy or deployment options; retention and regional requirements; interoperability; query usability at expected volume; and full operating cost.
How should you validate and roll out instrumentation?
- Choose a known request in development or staging. Send it through the representative retrieval, model, and tool paths in your request map.
- Inspect its trace. Confirm the root and child spans, parent relationships, trace context, provider and model fields, token counts where available, timings, and error status.
- Check privacy behavior. Verify that redaction and omission rules apply before data reaches the destination, including on error paths.
- Exercise failure cases. Test retries, tool and provider failures, and telemetry-export unavailability. Confirm export failures do not break user requests and that a fallback is documented.
- Review sampling and access. Make sure sampling does not erase rare high-risk events and that access and retention match policy.
- Roll out progressively. Watch telemetry volume and operating cost as traffic increases, then adjust instrumentation and sampling without losing the evidence needed to diagnose important failures.
These checks follow from the tracing and Collector architectures described by AWS and the data-handling guidance from OWASP; they are implementation recommendations, not reported hands-on test results.
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