Show HN: Harness Tuner – eval-driven tuning loop for agent tools, prompts, model (github.com)

Harness Tuner has been unveiled as a powerful tool designed to enhance the performance of AI agents by optimizing not just prompts, but all components surrounding the model. With remarkable results, agents utilizing this tuning method achieved 2.5 times fewer failures, reduced costs by sixfold, and lowered latency by 50% when tested with a smaller model and over 200 tools. The tool rewrites existing API wrappers tailored for agents, refines prompts, and adjusts model settings like temperature, enabling fine control over an agent's output and behavior. The significance of Harness Tuner lies in its evaluation-driven approach, which allows users to set specific goals—be it quality, cost, or speed—while tracking numerous metrics across experiments. It employs a systematic experiment design where only targeted evaluations are performed, ensuring efficiency and preventing regressions. By integrating with the Kiln app, which manages experiment tracking and result comparison, Harness Tuner represents a shift towards enhancing the overall efficacy of AI agents, making it easier for developers to adapt to changing requirements without heavy resource investments. This development is expected to streamline the deployment of AI solutions in various applications, ultimately pushing the boundary of what's achievable with machine learning.