Agentic systems often face a trade-off between accuracy and cost. The highest-performing proprietary frontier models and harnesses provide top accuracy but are expensive. Fine-tuning offers one way to address this problem. Smaller or more efficient open models starting with lower accuracy are taught to perform better with specific agents . However, fine-tuning requires expertise and hardware for training and hosting custom models.

While tuning prompts for specific use cases is common practice, formalizing this process for agent harnesses—and verifying that it produces significantly better results—is more recent.…