AI Agent Architecture: Trust, Not Accuracy

This post dissects the architecture of AI agents, arguing that user experience trumps raw accuracy. Using a customer support agent as an example, it outlines four architectural layers: memory (session, customer, behavioral, contextual), connectivity (system integrations), capabilities (skill depth), and trust (confidence scores, reasoning transparency, graceful handoffs). Four architectural approaches are compared: single agent, router + skills, predefined workflows, and multi-agent collaboration. The author recommends starting simple and adding complexity only when needed. Counterintuitively, users trust agents more when they're honest about their limitations, not when they're always right.