Scaling the Harness: System-Level Design for Agentic AI

A new paper on arXiv (2605.26112) suggests that the biggest hurdle for agentic AI isn't just about scaling the models themselves but rather about scaling the entire system. The authors stress the need to 'scale the harness,' which means focusing on the structured execution layer that supports the foundational models. They point out that current evaluation methods tend to concentrate too much on the models, reducing agents to their final outcomes and missing important elements like memory, retrieval, tool use, orchestration, verification, and governance. They argue that agent performance is influenced by various components working together, and they call for creating architectures that are auditable, persistent, modular, and verifiable.