This guide explains how to build enterprise-grade AI and analytics infrastructure that is secure, explainable, and scalable. It details a 7-layer architecture: data ingestion, semantic modeling, prompt engineering, model orchestration, interaction layer, governance, and deployment. Key principles include composability, privacy, and alignment with business logic. Governance spans lifecycle logging, auditing, and risk calibration. The paper highlights evolving from reactive assistants to proactive agentic systems with planners, memory, and evaluators. A modular design prepares teams to scale AI safely, maintain trust, and adapt to future autonomy.

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