AI agents addresses the challenge of complex scenario that not only generates text but also grounds its responses in real data and takes action. This session empowers you to leverage retrieval-augmented generation (RAG), knowledge graphs, and agent based architectures to engineer truly intelligent behavior. By combining large language models (LLMs) with up to date information retrieval and structured knowledge, you'll create AI agents capable of deeper reasoning and more reliable problem-solving. Inside, you'll find a practical roadmap from concept to implementation. You’ll discover how to connect language models with external data via RAG pipelines for increasing factual accuracy and incorporate knowledge graphs for context-rich reasoning.