How RAG Is Powering the Future of AI Agents

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🎙️ FULL SHOW NOTES
https://www.microsoftinnovationpodcast.com/717

What if your AI agent could not only retrieve facts but reason with them—just like a human? In this episode, Farzad Sunavala, Principal Product Manager at Microsoft, takes us inside the world of Retrieval-Augmented Generation (RAG), the architecture powering the next wave of intelligent agents. From solving hallucinations to building memory into AI systems, Farzad shares practical insights for professionals looking to build scalable, high-quality AI solutions that actually work in the real world.
 
🔑 KEY TAKEAWAYS
- RAG is foundational for AI agents: Retrieval-Augmented Generation solves key limitations of LLMs by grounding them in real-time, private data.
- Metadata is your best friend: Rich metadata and filtering techniques dramatically improve retrieval quality and reduce noise in enterprise AI systems.
- Memory is the next frontier: Embedding memory into agents—via tools like Semantic Kernel—enables learning, unlearning, and contextual recall.
- AI engineering is evolving fast: Developers must move beyond conventional software practices and embrace ML Ops, vector databases, and open-source frameworks.
- Start small, iterate smart: Building ground-truth datasets and synthetic Q&A pairs is a high-ROI strategy for evaluating and improving AI agent performance. 

🧰 RESOURCES MENTIONED:
👉 Azure AI - https://ai.azure.com/
👉 Azure AI Search – https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search or https://azure.microsoft.com/en-us/products/ai-services/ai-search/
👉 Microsoft Fabric – https://www.microsoft.com/en-us/microsoft-fabric
👉 Semantic Kernel (Open Source) – https://github.com/microsoft/semantic-kernel
👉 LangChain – https://www.langchain.com/
👉 LlamaIndex – https://www.llamaindex.ai/
👉 CrewAI – https://www.crewai.com/ 

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Thanks for listening 🚀 - Mark Smith