India's Most Futuristic AI Conference Is Back – Bigger, Sharper, Bolder
When an AI agent fails to complete a task, upgrading the model can seem like the obvious next step. Yet an agent’s performance also depends on how it receives instructions, uses tools, and manages its work. Understanding those factors can reveal opportunities to improve reliability with the model already in place.
Google Research’s Harness² examines that surrounding setup and its role in agent performance. I installed and ran the project to explore the idea firsthand. In this article, I look at the research, the implementation, and what developers can learn from it when building their own agents.
A harness shapes how an AI agent behaves through its prompts, tools, memory, and control loop. But different harnesses excel at different tasks. Claude Code performs better on coding and spreadsheets, while Codex leads on web tasks.
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