This article outlines the need for an "agent contract" to ensure reliable AI systems by defining clear expectations and standards among data, AI, and IT teams. It emphasizes the importance of communication and formal agreements to prevent failures caused by changing inputs and outputs.

AI reliability issues extend beyond hallucinations to include poor data quality, drift in embedding space, confused context, output sensitivity, and the balance of human involvement in processes. Ensuring the reliability of AI applications requires meticulous attention to data integrity, retrieval systems, and evaluation methods, rather than solely focusing on the model's performance. Building trust in AI involves comprehensive monitoring across all layers of the AI system.