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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Measure an AI agent as a distributed trace, not as one API timer. Create a root run for each user task, then child spans for orchestration, every model request, retrieval, tool call, retry, fallback, and final response. Attach stable run, tenant, workflow, version, model, provider, release, and environment identifiers. Record monotonic start and end times, status, errors, retries, parent span IDs, provider usage, and a versioned price-table reference. That single structure lets you explain latency, token consumption, and the actual cost of each successful outcome.
What production measurement must answer
A local benchmark can report one model call in isolation. A production agent usually performs several calls, waits on external services, retries failures, invokes tools, and may run subagents concurrently. Measuring only the final response hides the work that determines both user experience and spend.
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- How fast was the task? Measure wall-clock completion time, first-token responsiveness, model time, tool time, retrieval time, and the serial critical path.
- What did it consume? Preserve provider-reported input, cached-input, cache-creation, output, reasoning, and total token fields.
- What did it cost? Calculate an instrumented estimate for every call and reconcile it with the provider’s billed usage and cost reports.
- Was the result worth the work? Join quality, task success, or user feedback to the same run ID so speed and cheapness are not optimized at the expense of outcomes.
Use percentiles rather than averages for operational decisions. A P50 describes a typical request; P95 and P99 reveal queueing, slow tools, long contexts, and retry storms that affect the users most likely to complain.
Build one trace for every agent execution
Start with an immutable root run
Create a run_id as soon as the request is accepted at ingress. Keep it unchanged through HTTP handlers, queues, workers, model SDKs, retrieval systems, and tools. A trace context propagated across those boundaries is what lets you reconstruct a workflow when execution is asynchronous or distributed.
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The root span should represent the user task from acceptance to a usable final result. Record its start and end using a monotonic clock for duration calculations, and also store wall-clock timestamps for correlation with logs and billing exports.
Use child spans for work that can affect time or money
Create a child span for orchestration and one for each model request, retrieval operation, external tool invocation, retry, fallback, and final-response assembly. Give every span a parent span ID. Concurrent spans should retain their actual start and end times rather than being flattened into a single serial duration; this distinguishes elapsed time from total work.
Attach stable dimensions
At minimum, attach:
run_id, trace ID, span ID, and parent span ID;- workflow or task type and tenant or customer ID;
- agent version, deployment or release, environment, region, and service tier when applicable;
- provider, model, endpoint, request mode, and response timestamp;
- status, error class, timeout flag, retry number, fallback target, and tool name;
- quality or feedback identifiers that can be joined later without copying sensitive payloads.
Keep high-cardinality identifiers available for accounting and investigation, but avoid putting raw prompts, full documents, or secrets into every metric label. Store payload samples separately under a retention and access policy.
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End-to-end task latency
End-to-end latency is the wall-clock interval from request acceptance to the final usable result. It includes queueing, orchestration, model calls, retrieval, tool waits, retries, serialization, and response delivery. This is the primary user-experience measure. Report it for successful tasks and separately for timed-out or failed tasks; otherwise a system can appear faster by excluding the work that failed.
Time to first token (TTFT)
TTFT is the interval from a model request start to the first streamed output token. It exposes queue and prefill delay even when the eventual answer is long. Record TTFT for each streamed model span and, where useful, the first visible token of the overall task. A workflow may have a fast first model token but still feel slow if it performs a hidden tool call before showing useful progress.
Model request duration
For every provider request, store elapsed request duration, status, model, endpoint, and whether the call was a retry or fallback. This lets you distinguish a slow model from time spent waiting for your own orchestration or an external dependency.
Tool and retrieval latency
Instrument every external call, including network wait, serialization, authentication, and response parsing. A retrieval span should cover embedding or query preparation, the search operation, and result decoding if those happen synchronously. A tool span should identify the tool and operation without placing secrets or complete user arguments in metric labels.
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Time per output token
Decode throughput is measured after the first token. OpenTelemetry’s GenAI semantic conventions name this metric gen_ai.server.time_per_output_token: divide the duration after the first output token by the number of subsequent output tokens. Do not divide total request duration by total output tokens; that incorrectly assigns queue and prefill delay to decoding. The related convention names are gen_ai.server.request.duration for request latency and gen_ai.server.time_to_first_token for first-token latency.
Critical path and parallel work
For serial spans, durations add to the critical path. For concurrent subagents or tools, wall-clock time is close to the longest overlapping branch plus coordination overhead, while total work is the sum of all branches. Store both views:
- Elapsed task time: root span end minus root span start.
- Critical-path time: the serial and dependency-constrained spans that determine completion.
- Total span time: the sum of child durations, useful for capacity and cost analysis.
- Overlap: concurrent branches, their start and end times, and the degree of parallelism.
Two parallel subagents can reduce elapsed time while increasing tokens, tool calls, and cost. A dashboard that reports only wall-clock latency will miss that trade-off.
Capture and normalize token usage
Prefer provider-returned usage
At call completion, preserve the raw provider response and map it into a common schema. The normalized record should include:
| Field | Meaning | Handling rule |
|---|---|---|
| Input or prompt tokens | Tokens sent for the request | Keep the raw value and the normalized integer. |
| Cached-input tokens | Input tokens served from a provider cache | Keep separate from uncached input; cached tokens are generally included in input totals. |
| Cache-creation or cache-write tokens | Tokens used to populate a cache when the provider exposes this class | Store as its own billable class. |
| Output or completion tokens | Tokens generated in the response | Keep separate from input and reasoning counts. |
| Reasoning tokens | Provider-reported internal reasoning output | Store separately when available; some providers include it in output totals. |
| Total tokens | Provider’s aggregate | Retain the raw total for reconciliation; do not recompute it blindly. |
For example, OpenAI usage objects can expose input_tokens, input_tokens_details.cached_tokens, output_tokens, output_tokens_details.reasoning_tokens, and total_tokens. Cached tokens are included in input tokens, and reasoning tokens are included in output tokens. Provider-recorded usage is best effort: it may arrive after the turn, be null, or differ from the final bill.
Handle streaming correctly
Many streaming APIs emit usage only in a final event. Capture that event before closing the span. If the connection ends first, mark the usage record incomplete; never convert a missing usage object to zero. Queue an asynchronous reconciliation attempt when the provider offers one and publish a usage-completeness rate alongside token metrics.
Use Anthropic’s dimensions when applicable
Anthropic’s Usage API reports uncached input, cached input, cache-creation, and output tokens. It can group or filter results by model, workspace, service tier, context window, geography, and other dimensions. Store those dimensions in a way that can be joined to your internal tenant, deployment, and release labels.
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Separate raw, normalized, and derived data
Keep three layers:
- Raw event: the provider response, request ID, timestamp, and endpoint-specific usage object.
- Normalized event: consistent token fields, model and provider identifiers, run and span IDs, and a completeness state.
- Derived metrics: tokens per successful task, tokens per customer outcome, percentile distributions, and cost calculations.
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Maintain a versioned price table
Price data changes by provider, model, endpoint, token class, currency, geography, and service tier. Key each price record by those dimensions and an effective timestamp. Save the price-table version on every estimated cost event so a later rate change cannot silently rewrite historical accounting.
At call completion, calculate:
estimated_call_cost = Σ(token_class_count × price_per_unit) + provider_tool_charges + applicable_fixed_charges
Token classes can include uncached input, cached input, cache creation, output, and reasoning output. Add web-search, code-execution, hosting, or other third-party tool charges when they are billed separately. Apply currency conversion only with a recorded rate and timestamp.
Roll up all work in a run
Aggregate model-call estimates, tool charges, retries, fallbacks, parallel subagents, and fixed charges to the root run. Then roll up by workflow, tenant, deployment, model, release, and day. A workflow that appears to use one model may actually make several calls; omitting hidden retries or subagents produces an understated number.
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Illustrative calculation
Suppose one run records 12,000 uncached input tokens, 8,000 cached-input tokens, 2,000 cache-creation tokens, 1,500 output tokens, and 600 reasoning tokens. Your price table contains a separate unit rate for each class. Multiply each count by its matching rate, add two tool charges and any fixed fee, and save the resulting estimate with the run ID. The numbers are illustrative; use the rates and token definitions in the provider’s current billing data rather than a generic token price.
Reconcile instead of trusting estimates
Keep two values:
- Estimated instrumented cost: explains which span, token class, retry, or tool produced spend.
- Provider-billed cost: the financial control total from provider usage or cost exports.
Reconcile daily or at another operationally appropriate interval. Alert on unexplained variance, and classify expected causes such as delayed usage events, rounding, currency conversion, or charges that your trace cannot observe. Anthropic’s Cost API reports USD amounts as decimal strings in cents and includes token usage, web-search, and code-execution cost types.
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Do not equate caching with savings
A high cached-input percentage does not prove a task is cheaper. Cached input can still be billed, and repeated calls may process a large history. Compare total cost and latency per successful outcome at the quality level your application requires.
Design the production dashboard
The minimum useful dashboard combines reliability, latency, usage, cost, and quality. Every panel should be filterable by model, workflow, tenant, geography, release, and agent version without changing the run identifiers used in logs.
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| Area | Metrics to display | Useful breakdowns |
|---|---|---|
| Reliability | Successful-task rate, error rate, timeout rate, tool-error rate, retry rate, fallback rate | Model, tool, workflow, release, tenant |
| Latency | End-to-end, TTFT, model, retrieval, tool, and critical-path latency at P50, P95, and P99 | Streaming mode, model, region, workflow |
| Tokens | Input, cached input, cache creation, output, reasoning, and total tokens per task | Successful versus failed runs, model, tenant |
| Cost | Estimated and reconciled provider cost per task and per successful outcome | Model, workflow, customer, day, release |
| Operations | Tool-call count, retry depth, fallback count, parallel branch count, usage completeness | Agent version, dependency, error class |
| Quality | Task success, human feedback, evaluation score, or business outcome | Join by the same run ID and compare against cost and latency |
LangSmith documents hosted dashboards with token usage, latency including P50 and P99, error rates, cost breakdowns, feedback scores, threshold alerts, integrations for OpenAI and Anthropic SDKs, other frameworks, and OpenTelemetry. A hosted dashboard can shorten implementation time, but it does not remove the need to validate provider billing and retention behavior.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Implement the instrumentation in eight steps
- Create the root context. At ingress, generate an immutable
run_id, trace ID, tenant or user reference, workflow name, agent version, release, and environment. - Propagate context. Carry W3C trace context or your equivalent through queues, worker processes, model SDK middleware, retrieval clients, and tool adapters.
- Emit spans. Create one span per model request and tool invocation, plus orchestration, retrieval, retry, fallback, and final-response spans. Record monotonic duration and wall-clock timestamps.
- Capture raw usage. Save the provider request ID, model, endpoint, response timestamp, status, and unmodified usage object. Mark a stream incomplete when the final usage event is absent.
- Normalize tokens. Map provider-specific names into input, cached input, cache creation, output, reasoning, and total fields while retaining the raw record.
- Price the call. Join token classes and tool charges to the effective price-table version, then write an immutable estimated-cost event.
- Reconcile. Import provider usage and cost exports daily, match them to runs where possible, and track variance and unmatched spend.
- Alert and protect data. Alert on tail latency, cost per successful task, usage completeness, retry loops, and reconciliation variance. Sample payload bodies for privacy and storage control while retaining unsampled metadata needed for accounting and incidents.
Choose an observability approach
| Approach | Best fit | What it provides | What it does not replace |
|---|---|---|---|
| OpenTelemetry | Teams needing portability across providers and backends | Vendor-neutral traces, metrics, context propagation, and GenAI semantic attributes | Provider-specific usage definitions, billing exports, and reconciliation |
| OpenAI Agents observability | Applications built on OpenAI Agents | Native session traces, step inputs and outputs, duration, status, usage categories, timeline ordering and overlap, and OTLP JSON export | Independent accounting controls for other providers or external charges |
| Anthropic OpenTelemetry plus Usage/Cost APIs | Claude deployments requiring request/tool traces and organization-level reports | Spans for model requests and tool use, token dimensions, and USD cost categories | Instrumentation for work outside the Anthropic path |
| LangSmith | Teams that value a hosted dashboard and framework integrations | Percentile charts, token and cost views, feedback, alerts, and integrations with SDKs and OpenTelemetry | Proof that provider-billed totals exactly match your estimate |
OpenTelemetry is a practical baseline even when you also use a provider-native console. Generic spans preserve portability; native views retain billing semantics and fields that a generic exporter cannot reconstruct.
Compare models or agent designs fairly
Run both designs against the same workload, tools, quality target, timeout policy, and sampling period. Compare these six axes rather than declaring a winner from one latency number:
- Speed: TTFT, total latency, critical-path latency, and P95/P99 tail behavior.
- Efficiency: input, output, cached-input, cache-creation, and reasoning tokens per successful task.
- Cost: reconciled USD per successful task and per customer outcome, including retries and tools.
- Reliability: success, timeout, retry, fallback, and tool-error rates.
- Quality: task completion or human feedback under the same workload.
- Operational burden: instrumentation coverage, retention, cardinality, alert maintenance, and reconciliation effort.
A design that is 20 percent faster but requires twice the tokens may be a poor choice for a low-margin workflow. Conversely, a more expensive model can be justified if it materially improves successful outcomes and reduces retries or human intervention. Use cost per successful outcome as the decision metric, not cost per API call alone.
Common measurement failures and fixes
Timing only the final HTTP request
Symptom: dashboards show acceptable latency while users wait on queues and tools. Fix: start the root span at ingress and instrument every child operation, including asynchronous boundaries.
Recording missing usage as zero
Symptom: token totals and cost appear artificially low for interrupted streams. Fix: mark usage unknown or incomplete, retry reconciliation, and publish completeness separately.
Counting only the visible model call
Symptom: cost does not explain provider invoices. Fix: include retries, fallbacks, parallel subagents, cached-input and reasoning classes, provider tool charges, and applicable fixed charges.
Using averages for alerting
Symptom: a small number of very slow tasks is hidden by a healthy mean. Fix: alert on P95 or P99 and inspect the trace timeline for overlap and dependency waits.
Assuming cached tokens are free
Symptom: a high cache-hit percentage is reported as savings without a lower bill. Fix: price cached input according to the provider’s current schedule and compare total task cost and quality.
Over-collecting payloads
Symptom: telemetry increases privacy exposure, storage cost, and metric-cardinality problems. Fix: retain unsampled metadata for accounting, sample or redact bodies, restrict access, and define retention by data class.
Failing to version prices and schemas
Symptom: historical costs change after a rate update or parser deployment. Fix: make price-table versions, schema versions, and parser versions explicit on immutable events.
Operational controls worth adding
- Usage completeness: percentage of model spans with a final, provider-confirmed usage record.
- Reconciliation variance: estimated versus billed cost by provider, model, tenant, and day.
- Retry-loop detection: maximum retry depth, repeated error classes, and runs that exceed a time or spend budget.
- Budget guards: per-run and per-tenant token or cost limits that stop runaway orchestration safely.
- Cardinality review: regular checks that labels such as user IDs or request IDs are not being emitted as unbounded metric dimensions.
- Quality linkage: a durable join from evaluation or feedback records to the trace so optimization does not reward cheap failures.
Keep accounting events immutable and make corrections additive: append a reconciliation adjustment rather than overwriting the original estimate. This preserves an audit trail for why a number changed.
A practical definition of “fast” and “cheap”
An agent is fast when it reaches a useful first response quickly, completes the task within an acceptable tail-latency budget, and avoids slow retries or dependencies on its critical path. It is efficient when it uses the fewest input, output, cached, reasoning, and tool resources needed to achieve the required quality. It is financially controlled when estimated span-level costs reconcile to provider-billed totals and can be allocated to the workflow and customer that caused them.
Those definitions require the same run identity across traces, usage events, cost records, and quality feedback. Without that join, latency, tokens, and spend remain disconnected dashboards; with it, you can identify the exact model call, tool, retry, or release responsible for a production change.
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