Evals Belong at the Center of Harness Engineering, Argues New Guest Essay
hugobowne · x · 2026-09-08
Hugo Bowne-Anderson shares a Vanishing Gradients guest post by Antaripa Saha (Quotient AI), informed by Hamel Husain and Shreya Shankar's AI Evals course.
Key points:
- Agent = Model + Harness: the model supplies capability; the harness turns it into work via tools, state, memory, execution environments, constraints, and feedback.
- Most harness discussions focus on enabling agent action (tools, filesystems, terminals, sandboxes), while feedback — the least discussed part — determines whether that capability helps or just adds complexity.
- Evals are the only way to know whether changes to tools, memory, permissions, or context actually improved the system, so they should be a first-class harness component.
- The essay outlines a workflow from traces to failure modes, domain-specific criteria, and validated judges.
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