EPISODE · Nov 17, 2025 · 22 MIN
Agentic RAG Copilot: Stop Building Dumb Copilots and Start Using Agentic RAG
from M365.FM - Modern work, security, and productivity with Microsoft 365 · host Mirko Peters - Founder of m365.fm, m365.show and m365con.net
(00:00:00) The Limitations of AI Copilots (00:00:23) The Flaws of Retrieval-Augmented Generation (RAG) (00:02:05) The Linear Intelligence Fallacy (00:05:07) Introducing Agentic RAG: The Evolution of AI Assistants (00:09:48) Agentic RAG in Action: SharePoint Integration (00:13:26) Structured Data Meets Unstructured Knowledge (00:17:56) The Impact of Agentic RAG on Enterprise Decision-Making (00:20:51) The Future of AI in Enterprises (00:22:22) Subscribe and Enable Alerts In this episode of M365.fm, Mirko Peters explains why most enterprise copilots are just “well‑dressed autocomplete” — and how Agentic RAG, built on Azure AI Agent Service, Fabric Data Agents, and SharePoint retrievers, is the only realistic way to get verified, auditable answers instead of pretty guesses.WHAT YOU WILL LEARNWhy classic RAG (retrieve → prompt → generate → stop) fails for real enterprise decisionsHow a Planner, Retriever Agents, and a Verifier Agent work together as an agentic systemHow On‑Behalf‑Of auth, RLS/CLS, and Purview labels keep Agentic RAG inside your security and compliance guardrailsHow SharePoint retrievers turn “corporate archaeology” into searchable, security‑trimmed context with full audit logsHow Fabric Data Agents translate natural language into governed SQL over your semantic modelsHow verification loops, evidence‑linked insights, and provenance turn AI output into something auditors and GRC can live withA practical implementation checklist: Planner/Retriever/Verifier pattern, OBO auth, Fabric + SharePoint integration, and loggingTHE CORE INSIGHTRAG without agency is obsolete for enterprises. A single prompt over a single context window cannot join Fabric metrics, SharePoint documents, and external systems, let alone check itself for contradictions or stale data. Agentic RAG adds planning, multi‑agent retrieval, verification, and full governance so your copilot can reason across systems under the user’s identity and leave a complete audit trail behind every answer.WHO THIS EPISODE IS FORThis episode is ideal for CIOs, CDOs, Heads of AI, enterprise and data architects, BI leads, and security or GRC teams who need copilots that can actually be trusted in front of regulators, auditors, and executives. If your current copilots look great in demos but collapse on provenance, permissions, and verification, this conversation gives you a concrete blueprint for rebuilding them as Agentic RAG systems.ABOUT THE HOSTMirko Peters is a Microsoft 365 and data platform consultant focused on building auditable, governed AI systems on Azure, Microsoft Fabric, and SharePoint. Through M365.fm, Mirko shares practical copilot architectures, governance patterns, and implementation checklists that help organizations move from decorative AI to agentic systems that can explain every answer they give.Become a supporter of this podcast: https://www.spreaker.com/podcast/m365-fm-modern-work-security-and-productivity-with-microsoft-365--6704921/support.
What this episode covers
(00:00:00) The Limitations of AI Copilots (00:00:23) The Flaws of Retrieval-Augmented Generation (RAG) (00:02:05) The Linear Intelligence Fallacy (00:05:07) Introducing Agentic RAG: The Evolution of AI Assistants (00:09:48) Agentic RAG in Action: SharePoint Integration (00:13:26) Structured Data Meets Unstructured Knowledge (00:17:56) The Impact of Agentic RAG on Enterprise Decision-Making (00:20:51) The Future of AI in Enterprises (00:22:22) Subscribe and Enable Alerts In this episode of M365.fm, Mirko Peters explains why most enterprise copilots are just “well‑dressed autocomplete” — and how Agentic RAG, built on Azure AI Agent Service, Fabric Data Agents, and SharePoint retrievers, is the only realistic way to get verified, auditable answers instead of pretty guesses.WHAT YOU WILL LEARNWhy classic RAG (retrieve → prompt → generate → stop) fails for real enterprise decisionsHow a Planner, Retriever Agents, and a Verifier Agent work together as an agentic systemHow On‑Behalf‑Of auth, RLS/CLS, and Purview labels keep Agentic RAG inside your security and compliance guardrailsHow SharePoint retrievers turn “corporate archaeology” into searchable, security‑trimmed context with full audit logsHow Fabric Data Agents translate natural language into governed SQL over your semantic modelsHow verification loops, evidence‑linked insights, and provenance turn AI output into something auditors and GRC can live withA practical implementation checklist: Planner/Retriever/Verifier pattern, OBO auth, Fabric + SharePoint integration, and loggingTHE CORE INSIGHTRAG without agency is obsolete for enterprises. A single prompt over a single context window cannot join Fabric metrics, SharePoint documents, and external systems, let alone check itself for contradictions or stale data. Agentic RAG adds planning, multi‑agent retrieval, verification, and full governance so your copilot can reason across systems under the user’s identity and leave a complete audit trail behind every answer.WHO THIS EPISODE IS FORThis episode is ideal for CIOs, CDOs, Heads of AI, enterprise and data architects, BI leads, and security or GRC teams who need copilots that can actually be trusted in front of regulators, auditors, and executives. If your current copilots look great in demos but collapse on provenance, permissions, and verification, this conversation gives you a concrete blueprint for rebuilding them as Agentic RAG systems.<a...
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Agentic RAG Copilot: Stop Building Dumb Copilots and Start Using Agentic RAG
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