This case study provides an in-depth look into the real-world challenges and decisions involved in building an AI agent for financial advisors, highlighting practical engineering insights and lessons learned that are crucial for anyone working with AI tooling in enterprise settings.

• — The team eliminated RAG and MCP due to complexity and performance issues, opting for one-shot LLM calls that improved latency and stability.
• — The initial approach with multi-index RAG led to overengineering and slow queries; simplifying back to fundamentals resolved major performance bottlenecks.
• — Emphasize strategic decision-making on when to use agentic RAG, as it can significantly increase costs and response times for complex queries.