Intuit faced significant challenges in developing its agentic AI, requiring two major architectural overhauls in a short period. Initially, they moved from independent specialist agents to a central orchestration layer to simplify customer interaction. However, this orchestrator failed due to complexity, as natural language handoffs between agents led to compounding errors and loss of context. The system broke down because each agent had to infer previous steps, degrading accuracy with more agents in a chain.Consequently, Intuit reverted to a skills and tools based architecture, completing a rebuild in 60 days. Convincing leadership involved demonstrating the new system's superior performance on real customer queries. Gaining engineering buy-in focused on the scalability benefits of shared skills and tools over isolated agents. This shift also redefined team responsibilities towards evaluation rather than agent creation.The rebuild yielded customer-facing features like seamless integration of human support within AI conversations, allowing direct connection with professionals. Intuit's system prioritizes explicit permission for financial data actions, building trust over time with an audit log for accountability. Feedback collection transformed from sparse, polarized responses to nearly every conversation serving as data. Nhung Ho is personally re-engaging with coding to develop models that systematically analyze this vast amount of direct customer feedback, even when it's critical, to drive system improvements.