Agentic RAG extends traditional retrieval-augmented generation by enabling AI systems to plan, use tools, evaluate intermediate results, and refine responses through multi-step workflows. In this session, I will demonstrate how Kubeflow can orchestrate an Agentic RAG system as a production-oriented LLMOps pipeline.

The talk will cover a practical architecture using Kubeflow Pipelines for document ingestion, embedding generation, vector index updates, retrieval evaluation, agent testing, and deployment. It will also show how agent workflows can connect with tools and MCP-style services while Kubeflow provides reproducibility, versioning, experiment tracking, and continuous evaluation.

Attendees will learn how to move from a RAG prototype to an observable, testable, cloud-native Agentic RAG workflow using open source MLOps practices