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Many RAG initiatives stall after early demos because they hallucinate, break under orchestration, or fail to show measurable business value. This session introduces Agentic RAG, where retrieval decisions, evaluation, and improvement are handled by agents embedded in enterprise workflows. The session explains how Salesforce Agentforce, Salesforce Data Cloud, and LangGraph can be combined to build grounded, self correcting RAG systems. It also covers how to apply evaluation frameworks such as the RAG Triad, TruLens, and Ragas to measure accuracy, fairness, and ROI. The session concludes with a live Smart Checkout Helper scenario that demonstrates grounded retrieval, agent driven correction, and quantified business impact.
What you will Learn
How to design Agentic RAG systems that coordinate retrieval, reasoning, and decision making across enterprise workflows
How to evaluate RAG quality using context, groundedness, relevance, and continuous validation frameworks
How to connect RAG metrics to business outcomes such as conversion lift, call deflection, and AHT reduction
Who Should Attend
Software Developers building AI enabled applications
Architects designing production grade RAG and agent systems
Data and AI Engineers responsible for grounding and evaluation
Technical Leads and Engineering Managers focused on reliability, governance, and ROI
