Building AI Agents Part 1: Defining Purpose, Designing Prompts, and Selecting Models

The critical first steps that determine whether your AI agent succeeds or fails in production — with real examples from banking, retail, and healthcare A healthcare startup spent six months building an AI agent for patient triage. They used the latest GPT-4 model. They hired experienced ML engineers. They built a beautiful interface. The demo impressed investors. Then they launched to real clinics. Within days, nurses stopped using it. The agent asked irrelevant questions. It missed critical symptoms. It provided inconsistent advice. Sometimes it was too cautious, sending patients with minor issues to emergency rooms. Other times it was too aggressive, dismissing serious conditions. The problem was not the model. The problem was not the code. The problem was foundation. They skipped the critical first steps. They never properly defined the purpose. They rushed through prompt design. They chose the wrong model configuration. Three months and significant rework later, they got it right.