How to test agent cost-efficiency with Braintrust

In the context of AI-driven customer support systems, the focus should be on developing cost-effective control logic that ensures production-safe outcomes rather than solely choosing the cheapest models. This approach involves optimizing tool selection, model routing, retries, fallbacks, and safety-based escalations to balance quality and cost effectively. Evaluations show that strategies like "Retry, then escalate" and "Smart escalation" outperform cheaper, single-model approaches by resolving a higher percentage of tickets at a lower cost per resolved ticket. The effectiveness of these strategies is attributed to their ability to adapt to the complexities of real-world scenarios through dynamic routing and escalation, as opposed to static or purely cost-driven models that may fail to maintain quality. Tools like Braintrust provide the necessary infrastructure to log workflows, define evaluations, and compare strategies, enabling organizations to experiment with and refine their control logic to ensure efficient and high-quality customer support.

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