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Harness² is a Google Research project arguing that much of an agent's reliability comes from its surrounding scaffolding (prompts, tools, memory, and control loop), not only the underlying model. Its central claim is that rules enforced in the control loop can work better than advice written into prompts, which add token overhead and can crowd out the actual task. Readers building agents should treat the harness as a design surface in its own right, though the article's own evidence is a hands-on sample, so the reported gains need checking against the research itself.
Harness²: Improving AI Agent Performance Without Model Upgrades
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 […] The post Harness²: Improving AI Agent Performance Without Model Upgrades appeared first on Analytics Vidhya.
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. No single harness wins everywhere! Why Self-Editing Prompts Fail? No single harness wins everywhere! 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…
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