Overview:

The first wave of Generative AI was about "asking and finding." The next wave is about "thinking and doing." While Naive RAG was a great starting point, such linear pipelines hit a performance ceiling when faced with real-world business logic. If the first retrieval step misses, the system has no way to recover, leading to dead ends or confident hallucinations.

To deliver actual ROI, we must move to Agentic RAG—shifting from a simple search bar to an autonomous cognitive control loop. By transforming the LLM into an orchestrator, you enable AI that can decompose messy problems, select the right tools, and iterate until it finds a validated answer. This webinar provides an architectural crash course to build data-connected AI that doesn't just summarize documents and answer simple questions, but actually solves complex business problems.

Technical Details:

In this session, we walked through:

• Moving from linear RAG pipelines to iterative loops to solve "one-shot" retrieval failures.
• Navigating agentic RAG design patterns from intelligent routers to high-autonomy swarms.
• A practical crash course on query decomposition, tool integration, and the ReAct pattern.
• How to automate the extraction of agent-ready knowledge from messy data with Unstructured.