Retrieval-Augmented Generation (RAG) has become the standard approach in developing enterprise AI applications. However, as real-world tasks become more complex, classical RAG systems are performing adequately but are now encountering clear limitations. They retrieve information well, yet struggle with planning, multi-step reasoning, tool use, and integrating knowledge across diverse enterprise data sources. The result is a system that can answer simple questions but cannot execute workflows. This talk introduces Agentic-RAG, an emerging paradigm that combines retrieval with autonomous agents capable of reasoning, iterating, and making decisions. We will break down the core architectural components—intent interpretation, planning, multi-hop retrieval orchestration, tool-augmented reasoning, and context synthesis—and show how they transform RAG from passive fetch-and-generate into an active, adaptive problem-solving pipeline.
Porimol is an MLOps Engineer at Roche, currently focused on architecting advanced Agentic-RAG workflows for healthcare. He has been working in the cross-disciplinary (software engineering, data engineering, machine learning, and MLOps) domain for more than a decade. He's passionate about turning complex AI ideas into systems that actually work in production. Porimol specializes in the intersection of compliance and efficiency, applying a strong FinOps mindset to ensure that high-performance AI is also financially sustainable for the enterprise. With an MSc in Data Science and Business Analytics from the University of Warsaw and a research background in trustworthy AI, he is dedicated to building infrastructure that is scalable, interpretable, and production-ready.
