In the rapidly evolving landscape of AI integration, the Model Context Protocol (MCP) has solved the connectivity challenge—but connectivity alone isn't enough. This talk dives into the critical yet overlooked realm of API design for AI agent consumption, revealing why traditional human-oriented APIs often fail in agent-based systems. Drawing from practical case studies, we'll explore how semantic, intent-based architectures dramatically outperform technical endpoints, how progressive detail patterns reduce hallucination risks, and why context-rich responses are transforming agent effectiveness. Attendees will leave with actionable frameworks for evaluating existing APIs and designing new ones specifically engineered for the unique requirements of large language models. As organizations race to implement agent strategies, this presentation provides the blueprint for APIs that don't just connect to AI—but actively enhance its capabilities. The difference between mere connectivity and true usability isn't incremental—it's existential for enterprise AI success.