Your agent's cost problem isn't the model. It's the steps you never measured.

A recent analysis of agentic pipelines suggests that high operational costs are rarely caused by the choice of frontier models, but rather by a lack of granular observability. Developers often default to expensive models for every step of a process—from simple classification to complex reasoning—without tracking individual token consumption. The author argues that cost management in agentic loops requires a tree-based approach to attribution, logging metrics like step ID, tool usage, and retry counts. By implementing per-step routing and intelligent failover mechanisms, teams can optimize budgets without sacrificing performance. Instead of blindly switching to cheaper models, the article emphasizes that the most effective lever for cost control is building a robust routing layer that can measure, attribute, and dynamically swap models based on the specific requirements of each individual step in the workflow.