Staying ahead of the cost-quality curve

Agent performance is argued to depend less on continually selecting more capable models and more on decomposing tasks into measurable, testable, and optimizable steps within a structured “harness.” While model cost and task quality often increase together, each model has a quality ceiling that prompt engineering alone cannot overcome, and public benchmarks may not reflect performance on a specific business task; instead, teams should establish internal benchmarks and an “intent floor” defining minimally acceptable output quality. Breaking complex workflows into components such as research, planning, execution, review, classification, and routing can allow lower-cost models to handle mechanical work while reserving stronger models for difficult reasoning, improving observability, reliability, and cost efficiency. The approach requires continual benchmarking, A/B testing, and adjustment as new models emerge, while preserving room for agent creativity inside clear operational boundaries. The newly released Jev model is presented as a potentially useful option for rapid classification and decision routing, which could further simplify task decomposition and reduce the cost of agent systems.

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