Architecting Agentic AI for Enterprise Tax Solutions
About this session
Tax systems impact every individual, making the integration of AI into this space both highly influential and deeply complex. In this session, we will dive into the architecture of the traditional Retrieval Augmented Generation(RAG) and Agentic RAG systems designed to tackle these intricate tax workflows. We will focus heavily on the critical methodologies required to ensure strict factual accuracy, model grounding, and safe reasoning when deploying Generative AI in such a high-stakes enterprise environment. To close, I will share how we leverage large-scale user feedback and model performance traces to establish a continuous improvement loop, ultimately shaping the future of tax research.
Speaker
Key takeaways
- An inside look at the technical framework required to build advanced, multi-agent Retrieval-Augmented Generation systems tailored for complex, domain-specific workflows like tax research and filing.
- Critical methodologies and best practices for ensuring strict factual accuracy, model grounding, and safe reasoning when deploying AI in enterprise sectors where errors carry significant consequences.
- Actionable methods for capturing and leveraging large-scale user feedback and model performance traces to iterate on product development and shape the future of domain-specific AI research.