As AI-driven applications evolve, architects must adopt robust design patterns to ensure scalability, efficiency, and reliability. This session explores AI architecture patterns, from foundational Retrieval-Augmented Generation (RAG) to advanced agentic systems with autonomous reasoning and reflection. Key topics include AI building blocks, RAG optimizations, agentic workflows, and enterprise considerations such as performance, security, and governance. To design AI-driven applications that are scalable, efficient, and future-ready, architects must adopt well-defined patterns that balance performance, reliability, and adaptability. As AI capabilities evolve—from simple retrieval-augmented generation (RAG) to sophisticated agentic systems—understanding the underlying architectural principles becomes crucial. Key Takeaways from This Session: Structured AI Architecture Patterns – Learn how AI-powered applications progress from basic RAG implementations to advanced agentic AI systems with autonomous reasoning. Optimizing RAG for Scalability – Discover techniques for improving retrieval accuracy, reducing latency, and ensuring efficient knowledge integration. Agentic AI Design Principles – Understand how to build AI systems that incorporate memory, reflection, and iterative decision-making. Enterprise AI Considerations – Explore critical factors such as security, governance, cost optimization, and strategies to mitigate model drift. Future-Proofing AI Architectures – Gain insights into emerging trends, including real-time reasoning, multimodal AI, and evolving AI infrastructure. By the end of this session, attendees will have a clear framework for designing AI architectures that align with both current enterprise needs and the next wave of AI advancements.