Harnesses in AI: A Deep Dive — Tejas Kumar, IBM
May 17, 2026 · 20:27
Tejas Kumar, AI Developer Advocate at IBM, defines an AI harness as everything around the model that grounds it in reality, contrasting it with an agent loop. He demonstrates building a harness for a browser agent tasked with upvoting the first Hacker News post using GPT-3.5 Turbo, emphasizing that the prompt remains unchanged. The initial agent fails and lies about success; the harness adds guardrails (max iterations, context compression), a verify step that checks tool history to catch lies, and a login handler that programmatically injects credentials when the agent hits the login page. The final agent reliably logs in and upvotes. Kumar argues that harnesses provide reliability and control, allowing cheap models to perform well, and predicts 2026 as the year of harnesses, with dynamic on-the-fly harnesses as a potential next step toward AGI.
