The Tool Desk
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What to track: activity, usage, and customer attribution
Use traces to answer operational questions: what did the agent do, where did a run fail, and how long did each step take? Use usage records to answer accounting questions: which provider and model handled each request, how many billable units were used, and which SaaS tenant or workflow should receive the associated cost?
These are related but distinct records. A framework trace can show a model call or tool invocation without being a customer-level billing ledger. Conversely, a provider usage total may tell you how much was consumed without explaining which tool or workflow caused it. Build an explicit join between run activity, call-level usage, and your own stable customer identifiers.
For a useful trace, capture the end-to-end run and its component events, with timestamps, status, and enough context to investigate. OpenAI’s Agents API describes sessions containing turns, with spans for model responses and tools; its trace view can expose recorded inputs and outputs, duration, status, and tool-call detail. OpenAI Agents API observability documentation.
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A practical implementation sequence
- Define the questions. Separate operational measures—run status, failure location, and latency—from usage and cost measures such as requests, tokens, model, tenant, and workflow.
- Instrument the whole run. Use your agent framework’s tracing or add spans around the workflow, model requests, tools, handoffs, guardrails, and relevant custom events. Preserve parent-child relationships when work is asynchronous or delegated. OpenAI’s Agents SDK lists those event types in its tracing documentation.
- Attach business context deliberately. Add stable tenant/customer, user, environment, workflow, and agent identifiers to spans, traces, or associated usage records where supported. Prefer durable IDs over display names that can change. Observability tools may offer filtering by users or tags, but that does not mean they know your SaaS tenant identity automatically.
- Record usage at each API-call boundary. Save provider, model, request or response ID, request count, and input/output usage fields returned by the SDK or provider. Include cached-token, reasoning-token, or modality-specific fields when exposed and relevant. Preserve per-call records as well as run totals when available; totals alone can obscure retries, nested agents, and individual expensive steps.
- Export and retain telemetry. Send traces and usage to a backend that supports the queries, access controls, retention, and exportability you need. OpenAI trace export returns paginated OTLP JSON, but requires trace export to be enabled and a key with appropriate read permission; exporting existing traces does not configure automatic delivery of future traces. See the OpenAI trace export documentation.
- Reconcile cost before customer billing. Prefer provider-reported cost when available. Otherwise calculate an estimate using a versioned price mapping for provider, model, applicable region, and unit type. Keep the mapping current and reconcile estimates against provider statements before treating them as billable customer amounts.
- Build views and alerts. Begin with spend and usage by tenant, model, workflow, and time period, alongside run counts, latency, and errors. Add thresholds for unusual volume, spend, or failure rates. Keep operational and billing views distinct enough that a missing usage value cannot silently appear as zero spend.
- Test edge cases and data handling. Verify failed or cancelled runs, retries, delegated agents, streaming, tool calls, unknown usage, and any other billable requests are handled according to the provider’s accounting behavior. Review recorded payloads, redaction, access, retention, export permissions, and regional requirements before rollout.
How to choose an instrumentation approach
The right route depends on your stack and whether you need a shared telemetry pipeline or a product UI for agent debugging and cost reporting. The documented capabilities below are product descriptions, not independent comparative performance evaluations.
| Approach | Best fit | What it can provide | Check before choosing |
|---|---|---|---|
| Provider- or framework-native tracing | A stack centered on one provider or agent SDK | Low-friction visibility into native events and usage fields. OpenAI’s Agents SDK includes tracing and aggregates run usage across model calls, including calls associated with tools or handoffs. | Coverage of non-native tools and providers, export options, retention and policy fit, and whether required data is available when you need it. |
| OpenTelemetry instrumentation | A team seeking a shared or portable span-based pipeline | Integration with existing telemetry infrastructure. Langfuse documents OpenTelemetry instrumentation, and LangSmith describes connecting existing pipelines through OpenTelemetry. | Whether semantic fields survive export, backend compatibility, telemetry volume and cardinality, and how usage is attached to spans. |
| Dedicated LLM or agent observability service | A team wanting trace exploration, dashboards, and evaluation or debugging workflows in a product UI | Langfuse documents per-generation usage and cost, dashboards, alerts, and metrics queries. LangSmith describes dashboards for usage, latency, errors, cost breakdowns, and feedback, as well as framework and OpenTelemetry support. | Data region and retention, self-hosting needs, access controls, model-price maintenance, and current service terms. |
When comparing options, check framework coverage, per-call usage fidelity, tenant-level aggregation, export portability, data residency and retention, cost-estimation method, query and alert capabilities, and integration effort. No single approach is established as the universal winner.
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Usage and cost: avoid common accounting errors
Keep raw usage separate from calculated cost
Store the usage values returned for each call before calculating cost. A cost inferred from a price table is only as accurate as the model identification, unit mapping, and rates used at calculation time. Preserve enough information to recompute it if a model or rate changes, and label inferred spend as an estimate rather than provider-confirmed billing.
Langfuse documents both ingested usage/cost values and inferred cost based on project model definitions, including custom model definitions. Its reporting can filter metrics by application type, user, or tags; your application still needs to attach correct tenant context. See Langfuse usage and cost analytics.
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Do not treat missing usage as zero
Usage can be unavailable or provisional. OpenAI documents that Agents API usage is best-effort, may be null when unknown, and can change as accounting arrives. The Agents SDK also aggregates usage across calls within a run, but a run total should not replace per-request data when you need to diagnose or reconcile individual calls. Consult the Agents API observability guide and Agents SDK usage documentation.
Represent an unknown value explicitly, with a status such as pending or unavailable, rather than storing zero. That distinction prevents dashboards and customer invoices from misrepresenting incomplete accounting.
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Privacy, permissions, and deployment checks
Agent traces may contain prompts, model outputs, tool inputs, and application data. Before enabling collection, establish what is recorded, whether sensitive fields are redacted, who can view or export the data, how long it is retained, and which region processes it. Inspect the actual integration behavior: Langfuse notes that wrappers may forward prompts, model information, and outputs, and its setup documentation lists regional endpoint examples. Verify service, contract, and data suitability for your deployment in its getting-started documentation.
OpenAI states that Agents SDK tracing is unavailable for organizations using OpenAI APIs under a Zero Data Retention policy. Session trace export also requires enablement and suitable read permission, so confirm that tracing and export meet your organization’s policy before making them part of a production workflow. See the Agents SDK tracing guide and Agents API observability guide.
Quick Recap
What a reliable customer-level view should show
- Run activity by tenant and workflow, including status, timing, and relevant tool or handoff events.
- Call-level provider, model, request identifiers, and available usage fields, linked to the run and tenant.
- Usage totals by customer, model, workflow, and time range, with unknown or provisional values clearly distinguished.
- Cost with its basis identified: provider-reported or calculated from a maintained price mapping.
- Operational alerts for errors or latency and usage/spend alerts for unusual consumption.
- A path back from a reported customer total to the underlying runs and calls for investigation and reconciliation.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
