Agentic RAG: Architectural Patterns for Knowledge-Driven AI Systems

Retrieval-Augmented Generation (RAG) has become the foundation of modern AI applications, enabling large language models to access external knowledge and enterprise data. Yet many organizations are discovering that traditional RAG architectures struggle when faced with complex business workflows, multi-step reasoning, fragmented knowledge sources, and the reliability requirements of production environments.

As AI systems become increasingly autonomous, retrieval can no longer remain a static pipeline. It must become an intelligent architectural capability that can plan information gathering strategies, orchestrate multiple knowledge sources, evaluate evidence, and adapt dynamically to changing information needs.

In this webinar, we'll explore the architectural evolution from conventional RAG pipelines to Agentic RAG systems and examine the design patterns that enable more trustworthy, scalable, and enterprise-ready AI applications. Through practical examples and real-world architectural scenarios, attendees will learn how intelligent agents can orchestrate retrieval workflows, perform multi-hop reasoning across knowledge sources, and implement validation mechanisms that improve reliability and reduce hallucinations.

Whether you're designing internal AI copilots, enterprise search platforms, customer support systems, or knowledge-intensive AI applications, this session will provide a framework for building retrieval architectures that can scale beyond simple document lookup and support complex reasoning workflows.

What You'll Learn

• Why traditional RAG architectures fail in complex enterprise scenarios
• The architectural principles behind Agentic RAG systems
• Designing retrieval workflows that support planning, reasoning, and decision-making
• Query decomposition and multi-step retrieval strategies for complex information needs
• Dynamic source selection and orchestration across structured and unstructured knowledge systems
• Multi-hop retrieval patterns for reasoning across distributed information sources
• Query rewriting, corrective retrieval, and self-reflection mechanisms
• Provenance, grounding, and validation patterns for trustworthy AI outputs
• Reliability, observability, and governance considerations for production deployments
• Common architectural anti-patterns and failure modes
• Evaluating Agentic RAG systems for accuracy, performance, and business impact
• Reference architectures and implementation patterns for enterprise AI applications
  

Who Should Attend?

This webinar is designed for:

• Software Architects building AI-powered applications
• AI Architects designing enterprise GenAI platforms
• Solution Architects evaluating knowledge and retrieval architectures
• Enterprise Architects responsible for AI governance and scalability
• Technical Leaders and Principal Engineers building production AI systems
• Engineering Managers leading GenAI initiatives

Key Takeaway

You'll leave with a practical architectural framework for designing Agentic RAG systems, along with proven patterns, trade-offs, and implementation strategies for building scalable, reliable, and trustworthy AI applications that depend on external knowledge and enterprise data.