Prompt versions, traces, token usage, latency, and model monitoring are necessary. They do not show whether the complete business workflow finished correctly or economically.
A trace can prove that an agent called a tool. Operators also need to know whether the call was authorized, whether the system of record changed, and what remains unresolved.
Record the complete path:
Workflow request → configuration → identity → source data → tool calls → policy and approvals → system changes → final outcome
Useful production measures include:
This evidence lets engineering, operations, security, audit, and business leaders investigate the same event without relying on the model’s own account of success.
Token cost is one input. Total cost may also include retrieval, model retries, tool APIs, cloud execution, policy checks, security monitoring, human review, exception handling, and support.
Use the metric:
Total operating cost ÷ correctly completed business workflows
The denominator prevents false optimization. A cheaper model may increase retries, incorrect actions, or manual rework. A more expensive path may cost less per successful outcome.
Confirm correctness first. Then reduce unnecessary calls, improve retrieval, route simple tasks to economical models, automate deterministic checks, and remove avoidable rework. Segment economics by workflow and risk rather than averaging unrelated use cases.
Cayru calls the operating layer connecting workflow contracts, evaluations, authority, business-state observability, and economics AI Production Control. It is Cayru’s framework for extending model-level LLMOps to the complete business workflow.
Connect AI telemetry to one business outcome and establish its real unit economics.