Retrieval-Augmented Generation (RAG) adoption is growing rapidly, with major hyperscalers expected to launch native RAG agent solutions by 2025, leading stakeholders to face challenges in selecting the right platform. The text provides a framework for evaluating RAG agents based on five critical pillars: Accuracy, Observability, Adaptability, Time-to-Value, and Enterprise-Readiness. It emphasizes the importance of achieving trustworthy outputs, transparent workflows, and flexible adaptability to unique data and use cases, while also ensuring rapid deployment and enterprise-level security. The guide suggests asking vendors specific questions to assess these attributes and highlights AI21 Maestro as a solution that meets these criteria, offering automation, visual execution insights, adaptability, and enterprise-grade deployment. It concludes that a RAG agent should meet stringent requirements across these five areas to ensure successful implementation and value realization.