Prompt engineering is useful because it shapes the instruction given to a model. It can improve style, task framing, and the model's immediate behavior. But reliable AI agents need more than a well-written prompt.
Harness engineering is the surrounding system: the guardrails, evidence checks, fallback routes, tool permissions, trace logging, and evaluation loop that decide how an agent is allowed to act. A harness can verify whether an answer is grounded in sources, reject fake URLs, fall back to retrieval when confidence is low, and record failures for later analysis.
This matters because agent reliability is not only a model property. It is also a workflow property. Strong agents need controlled interfaces between the model, tools, data, users, and evaluation process.