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To find which AI agent or workflow drove a cost spike, record its identity with every model request and agent run, then follow the trace and add up usage across the full run—including delegated agents, retries, and failed work. A provider or project total alone cannot show which agent generated the activity.
Why a cost total cannot identify the agent
A billing dashboard can show that a project or provider used more, but attribution depends on the information recorded when the work happened. If requests lack agent and workflow identifiers, an aggregate total usually cannot be reliably divided among agents after the fact.
Choose the unit you want to explain before instrumenting the system: an agent, workflow, task, customer, team, or completed outcome. Attach that identity before sending requests, and preserve it throughout the run.
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What to record for each run
Use a trace hierarchy that connects a workflow to its turns, model calls, tools, and delegated work. OpenAI describes traces as showing model responses, tool calls, and work delegated to other agents; its Agents API documentation also describes inspecting session logs, turns, and recorded usage for root-agent and subagent turns. OpenAI: Observability and usage and OpenAI: Tracing
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- Identity: stable agent and workflow names or IDs, plus the session or run ID and turn ID.
- Relationships: parent-agent and delegated subagent IDs so their work can be connected to the initiating run.
- Request details: provider, model, timestamp, and whether the activity was a model call, tool operation, retry, or failure.
- Usage: input, cached-input, and output token counts where available.
- Trace context: inputs, outputs, duration, and status where the instrumentation exposes them.
Include failed and retried activity, not just successful results. Otherwise, a loop of repeated requests or expensive work that eventually errors can disappear from the explanation of a spike.
How to trace a spike to its source
- Set a time window and scope. Identify the provider, project, or account and the period in which the observed increase occurred.
- Filter recorded runs to that scope. Group requests by the agent, workflow, task, team, or other identity you chose to capture.
- Open the relevant traces. Follow each run from its root agent through turns, model calls, tools, and delegated subagents. Compare duration and status as well as usage.
- Aggregate usage across the tree. Include every model call and delegated turn. A single agent run can make several calls, so counting only the root turn can understate its activity.
- Reconcile the estimate. Compare telemetry-derived usage or cost estimates with the provider’s billing records when invoice accuracy matters.
Why recorded usage may differ from the bill
Telemetry is useful for explaining behavior, but it is not automatically the final invoice. OpenAI says recorded usage can be best-effort, may be null or change as accounting data arrives, and should not be treated as a final bill. Its Agents API usage fields also have a cache-write pricing limitation. Token counts and calculated costs should therefore be labeled as estimates unless reconciled with billing data. OpenAI: Observability and usage
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- 【Leading AI Mini Workstation】MINISFORUM AI MS-S1 Max Workstation comes with AMD Ryzen AI Max+ 395 processor, which uses AMD's latest generation Zen 5 architecture. It has 16 Cores and 32 Threads, the boost clock is up to 5.1GHz. The overall processor performance is up to 126 TOPS, and the NPU performance reaches up to 50 TOPS. AMD Ryzen AI enables improved productivity, advanced collaboration, and improved efficiency.
- 【AMD Radeon 8060S Graphics 】The MS-S1 Max Mini PC equipped with AMD Radeon 8060S Graphics which built on the new generation of RDNA 3.5 architecture AMD graphics, it brings ultra-high frame rate experiences and advanced content creation features anywhere and delivers staggering performance. It can handle all your computing and multimedia tasks efficiently.
- 【Five 8K Video Output】This MS-S1 Max Workstation comes with five video outputs, 1x HDMI (8K@60Hz), 2x USB4(40Gbps,Alt DP2.0,PD out 15W) and 2x USB4 V2(80Gbps,Alt DP2.0,PD out 15W) Outputs, which support multiple monitors display at the same time and provide a larger and wider filed of view and improve your work efficiency. It is used in fields that require high-performance computing and graphics processing, including digital signage and securities trading, as well as work that uses CAD, such as engineering design, scientific calculations, animation production, and post-production for movies and television
- 【 Fast and Stable Wire & Wireless Speed】It comes with Two 10G Lan Ports for wired connection and and Wi-Fi 7 / BT5.4 for wireless connection, which increased the network speed greatly and expand its functions and improved performance of computer to a large extent and allows you to use more networks such as software routers (OpenWRT / DD-WRT / Tomato etc.), firewalls, NAT, network isolation etc.
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Keep provider and model alongside usage because pricing and prompt-caching rules differ. OpenAI’s documentation says each call follows the model’s token pricing and prompt-caching rules, as in the Responses API; a token total by itself is not necessarily a complete cost calculation. OpenAI: Observability and usage
Choosing an observability approach
First-party traces, cloud dashboards, and third-party observability products can overlap, but evaluate them against the same operational needs rather than assuming any one dashboard provides complete attribution.
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| Approach | What the cited documentation describes | What to verify |
|---|---|---|
| OpenAI agent observability and tracing | Session logs, turns, usage for root and subagent turns, and traces covering model responses, tools, and delegated work. OpenAI observability and OpenAI tracing | Usage completeness, pricing details, export needs, retention, and whether the records cover every provider and framework in your system. |
| Grafana Cloud | Documentation distinguishes billing dimensions for generations, evaluation or guard tokens, and standard telemetry. Grafana LLM monitoring | Which signals and cost dimensions apply to your deployment, along with integration, retention, and data-handling terms. |
| AWS CloudWatch Coding Agent Insights | AWS documents coding-agent dashboards based on OpenTelemetry signals, including attribution to teams or departments. AWS CloudWatch Coding Agent Insights | Whether your coding-agent deployment emits the required signals and whether its attribution dimensions match your reporting needs. |
| telemetry.dev | The product describes trace-level model calls, tool steps, token usage, and estimated cost. telemetry.dev | Current provider and framework compatibility, export options, retention, pricing, and data handling for your stack. |
Before adopting a product or building an in-house pipeline, check attribution granularity, trace detail, provider and framework coverage, cost-estimation limits, integrations and exports, retention and privacy controls, and how telemetry is billed. A monitoring tool’s own charges may depend on dimensions such as generations, spans, or other telemetry signals.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When the existing dashboard cannot answer the question
If historical records contain only a project-level total, the missing agent attribution may not be recoverable. Start capturing stable identifiers and trace relationships on new requests, then use provider billing records to validate totals. For future spikes, confirm that each run can be followed from workflow to root agent, subagents, and individual model calls.
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