What if the harness mattered more than the model? - Aditya Bhargava, Etsy

The performance of AI agents depends more on the "harness"—the surrounding framework and tools—than the underlying model. By prioritizing robust harness design, developers can achieve high-level performance using local, open-source models, reducing dependency on proprietary systems. Effective agent development requires language-level support, such as the "Agency" language, which simplifies tool integration and safety. A systematic approach to building these agents involves implementing safety handlers, using partial function application to constrain capabilities, and establishing reasoning loops like REACT. Further advancements include utilizing sub-agents to manage context bloat and employing self-optimization to measure and improve performance iteratively. This methodology transforms agent creation from a process of trial and error into a structured, analytical practice, ensuring that agents remain both autonomous and safe while executing complex tasks.