Bas Geerdink

The initial wave of Generative AI focused on conversational interfaces and basic Retrieval-Augmented Generation (RAG). However, as enterprises move toward the next frontier—Agentic AI Architecture—the limitations of simple vector search are becoming clear. To transition from an AI that merely answers questions to one that autonomously executes complex business processes, we must solve the "Context Gap." An agent is only as effective as its understanding of the environment it operates in; without a structured framework for reasoning, even the most advanced models struggle with hallucination and logic errors when faced with fragmented enterprise data. This session explores how to design a high-performance agentic ecosystem that transcends the boundaries of traditional RAG. We will examine why a Semantic Layer and a robust Ontology are no longer academic concepts but are essential features of a production-ready AI stack. These components act as the "connective tissue" between the reasoning capabilities of Large Language Models and the physical reality of your data storage. Whether your organization relies on a centralized Data Warehouse, a scalable Data Lake, or a decentralized Data Mesh, the success of an agent depends on its ability to navigate these environments with a unified

The rapid evolution of AI, fueled by powerful Large Language Models (LLMs) and autonomous agents, is reshaping how we build, deploy, and manage AI systems. This presentation explores the critical intersection of MLOps and AI architecture, highlighting the paradigm shifts required to integrate LLMs and agents into production. We will address key architectural challenges, including scalability, observability, and security, while examining emerging MLOps practices such as robust data pipelines, model monitoring, and continuous optimization. Attendees will gain practical insights and actionable strategies to navigate the complexities of modern AI deployments, unlocking the full potential of LLMs and agents while ensuring operational excellence. As AI evolves with powerful Large Language Models (LLMs) and autonomous agents, deploying and managing these systems requires new approaches. This presentation explores the crucial intersection of MLOps and AI architecture, highlighting the shift toward scalable, observable, and secure AI deployments. We’ll examine key architectural considerations for integrating LLMs and agents into production, alongside evolving MLOps practices such as robust data pipelines, model monitoring, and continuous optimization.