Retrieval-Augmented Generation (RAG) has transformed how we build Q&A systems with Large Language Models (LLMs) by combining the strengths of search and generation. However, traditional RAG workflows are static and often struggle to handle the dynamic and complex demands of real-world applications, such as answering multi-step queries, integrating external APIs, or gracefully recovering from retrieval failures. Agentic behavior addresses these challenges by extending RAG pipelines, enabling LLMs to make decisions, integrate tools, and dynamically adapt workflows.

In this talk, we’ll explore how agentic behavior enhances pipelines. We’ll define what it means for a system to act as an “agent” and cover core concepts like routing, tool calling, and reasoning. Using hands-on examples implemented in Python, we’ll walk through practical use cases, such as integrating external APIs and solving multi-step problems. Finally, we’ll tackle challenges like transparency in complex systems and share how graph-based approaches can make these workflows more interpretable.