Anyone working on AI Agent implementation has likely encountered this dilemma:You're using a flagship model, have revised your prompts hundreds of times, and tuned your RAG system countless times. Yet when deployed in real-world scenarios, the task success rate simply won't improve—the agent sometimes performs brilliantly, other times goes completely off-track.The problem doesn't lie with the model itself, but with the operating system running outside the model—the Harness.What Is Harness Engineering?The term "Harness" originally refers to r...
Posts tagged AI Agent Engineering
Beyond Prompt Engineering: Harness Engineering as the Key to Stable AI Agent Deployment
Developers working on AI Agent deployment have likely encountered this frustrating dilemma: using flagship models, revising prompts hundreds of times, tuning RAG systems repeatedly—yet task success rates remain stubbornly low in real-world scenarios, with performance fluctuating unpredictably between brilliant and completely off-track.The root problem lies not in the model itself, but in the operational system surrounding it—the Harness.Understanding Harness EngineeringThe term "Harness" originally refers to reins or restraint devices. In AI...
Beyond Prompt Engineering: The Core of Stable AI Agent Deployment — Harness Engineering
Introduction: The Real Challenge in AI Agent DeploymentDevelopers working on AI Agent implementations frequently encounter a frustrating paradox: despite using flagship models, refining prompts hundreds of times, and tuning RAG systems repeatedly, task success rates in real-world scenarios stubbornly remain below expectations. The system performs inconsistently—sometimes brilliant, sometimes completely off-track.The fundamental issue lies not with the model itself, but with the operational system surrounding it—the Harness.Understanding Harn...
Beyond Prompt Engineering: The Core of Stable Agent Deployment—Harness Engineering
Practitioners working on AI Agent deployment have likely encountered this frustrating dilemma: despite using flagship models, revising prompts hundreds of times, and fine-tuning RAG systems repeatedly, task success rates simply won't improve in real-world scenarios. The agent sometimes appears brilliant, other times goes completely off-track.The root of the problem lies not in the model itself, but in the operational system surrounding it—the Harness.Understanding Harness EngineeringThe term "Harness" originally refers to restraint or contro...