Cost-Quality-Latency Tradeoffs
Balancing evaluation cost, quality, and speed.
Cost-Controlled Benchmarking
Instead of asking “what is the best score an agent can achieve?”, cost-controlled benchmarking asks “what is the best score at a given cost per task?” – a question far more relevant to production deployment decisions.
Evaluation at Scale
Scaling agent evaluation from 50 hand-run tasks to 50,000 automated runs requires fundamental shifts in infrastructure, organization, data management, and cost discipline – transforming evaluation from a developer activity into a production service.
Evaluation Budget Optimization
Given a fixed evaluation budget, maximize the information gained about agent performance through adaptive testing, early stopping, progressive evaluation, and intelligent budget allocation between breadth and depth.
Latency-Aware Evaluation
Time is a critical and often overlooked evaluation dimension – measuring not just whether an agent succeeds but how quickly it succeeds, where the time goes, and how latency interacts with perceived and actual quality.
Model Cascading Evaluation
Model cascading routes easy tasks to cheap, fast models and hard tasks to expensive, capable models – and evaluating these routing strategies requires measuring both the router’s accuracy and the system’s aggregate cost-quality tradeoff.
The Evaluation Triangle
Every evaluation decision involves a three-way tradeoff between thoroughness (how deep and broad the evaluation), cost (compute, API calls, human time), and speed (time to get actionable results).