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What traditional monitoring misses in an LLM application
Traditional application monitoring focuses on service health: how much traffic a system handles, how long requests take, whether they fail, and how requests move across service boundaries. Those signals still matter in an AI application, but they may not explain why a user received a slow, incorrect, or unhelpful result.
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An LLM request can pass through several distinct stages: a model operation, one or more tool calls, retrieval of context, and a larger agent or workflow. A service-level error rate can reveal an outage; a trace linking those stages can help identify whether the delay or failure came from the model, a tool, or retrieved context. Quality evaluations add another view: a healthy endpoint does not by itself establish that its answer was useful or correct.
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What to track across the stack
Use aggregate metrics to spot broad changes, then traces to investigate individual executions. The following layers combine ordinary service-health monitoring with signals specific to AI workflows. The fields are options present in OpenTelemetry GenAI semantic-convention material, not a requirement to capture every payload; those conventions are evolving, and some registry entries have moved to a dedicated repository.
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| Layer | Signals to track | What they help answer |
|---|---|---|
| Service health | Request volume, latency distributions, error rate, and end-to-end distributed traces | Is the application experiencing a broad traffic, reliability, or performance regression? |
| Model operation | Provider and model identity, operation type, input and output token counts, operation duration, and request or response metadata | Which model operations are running, how are they performing, and what is driving usage? |
| Workflow and agent | Workflow or agent name where meaningful, invocation duration, session or conversation correlation, and linked spans for each step | How did execution move through the agent or workflow, and where was time spent? |
| Tools and retrieval | Tool name or type and call identifier; retrieval query or data-source identifiers; documents or scores where appropriate; and arguments or results only when safe to capture | Did a tool call or retrieved context contribute to the result or failure? |
| Streaming | Time to first chunk and full operation duration | How quickly did output begin, and how long did completion take? |
| Quality and outcome | A named evaluation metric, its score or label, and a product-defined outcome or human review where available | Did the output meet the quality criteria the product actually uses? |
Microsoft’s guidance for generative and agentic AI observability likewise calls out token usage, latency, errors, tool-call or request volume, and end-to-end tracing. Google Cloud’s agent observability guidance describes logs, metrics, traces, and prompt/response data used for quality and decision evaluation. OpenTelemetry’s GenAI attributes include model and workflow identity, token usage, evaluation fields, retrieval, tool calls, and time-to-first-chunk.
Instrument meaningful boundaries, not just one duration
A single latency number can conceal different kinds of work. OpenTelemetry’s GenAI metrics distinguish provider-facing client-operation duration from the duration of a bounded agent invocation and the duration of a larger workflow. Instrument each boundary when it is reliable and meaningful for your system; do not treat those measurements as interchangeable.
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- Model operation: Measure the provider-facing call. This is the boundary for understanding an individual model operation.
- Agent invocation: Measure a bounded agent execution, which may include more than one operation.
- Workflow: Measure the larger multi-step execution when the workflow boundary is clearly defined.
Use spans to connect those boundaries with tool and retrieval activity. A trace should make the execution path intelligible, rather than presenting a model call as if it were the entire application request.
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Operational metrics answer whether a system is available and responsive; evaluation signals address whether its behavior meets a defined standard. Record an evaluation’s metric name and score or label together. A score without its evaluator and meaning is not universally interpretable: OpenTelemetry’s convention material notes that score labels depend on the metric and evaluator.
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There is no single evaluation method established for every LLM application. Choose criteria that fit the product and document what the evaluator measures. Where the product has a meaningful outcome or human review, correlate that with the evaluation rather than treating a passing service-health metric as evidence of a good answer.
Choose an observability approach by coverage and control
An existing APM stack may be extendable; a dedicated LLM observability product may offer AI-specific views. The useful comparison is whether either approach can connect infrastructure health to model behavior and user outcomes, not whether it replaces one dashboard with another.
- Trace depth: Can it connect service spans to model, tool, and retrieval operations?
- Signal coverage: Does it expose token usage, model identity, latency, errors, and evaluation data alongside infrastructure metrics?
- Portability: Does it use OpenTelemetry GenAI conventions, and can telemetry be exported to the team’s existing backend? OpenTelemetry describes its conventions as a way to structure telemetry across tools and environments.
- Quality workflow: Can the team associate evaluations with prompt/response behavior and inspect changes across versions?
- Data controls: What prompt, response, and tool details are retained, who can access them, and how is sensitive content redacted?
- Metric boundaries and cardinality: Can it distinguish a provider call from an agent invocation or workflow duration? Can it avoid unbounded workflow labels?
The GenAI metrics specification recommends low-cardinality workflow naming and says not to capture a workflow name by default when it is meaningless. OpenTelemetry’s walkthrough makes full content capture configurable, but the cited guidance does not establish a universal retention period or data policy; set those controls according to your application’s requirements.
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A practical rollout sequence
- Preserve the existing baseline. Keep request volume, latency distributions, errors, and distributed traces so AI-specific instrumentation does not obscure ordinary service regressions.
- Add model identity and usage. Record provider/model and workflow or agent identity where available, along with input and output token counts and operation duration.
- Connect workflow steps. Link model operations to tool calls and retrieval spans, using session or conversation correlation where useful. Capture arguments, results, prompts, and responses only when justified and safe.
- Separate duration boundaries. Decide which measurements represent a provider call, an agent invocation, and a whole workflow; instrument only boundaries the system can define consistently.
- Define quality signals. Name the evaluation metric and record its score or label with enough context to interpret it. Add product outcomes or human review when they are part of the product’s quality process.
- Review conventions and instrumentation. OpenTelemetry’s GenAI semantic conventions are in active development. Check current definitions and versions when implementing or upgrading, rather than assuming a registry attribute is final or unchanged.
Protect telemetry data and keep it useful
Prompts, completions, retrieved documents, and tool arguments or results may contain sensitive information. Decide whether full content is necessary for debugging or evaluation, configure capture accordingly, and apply the application’s access and redaction policies. The official guidance cited here does not prescribe one retention period or universal policy.
Start with stable aggregate metrics for volume, latency, errors, and token usage; use traces to investigate specific workflows. Keep workflow labels bounded and meaningful, and review convention versions as part of instrumentation changes. This limits avoidable cardinality while preserving the detail needed to locate a failing stage.
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