Generic AI systems break down quickly in specialized industries because the problem isn't just model quality; it's context quality. In healthcare, legal, and other regulated or workflow-heavy domains, agents need to know which body of knowledge to use, when to switch contexts, and how to avoid blending unrelated data into one mushy answer. This session shows why vertical APIs matter in the AI era. They give agentic systems a clean way to route requests into the right domain stack, whether that means a subject-specific small model, a separate RAG corpus, or structured knowledge represented as a graph. The core idea is practical: domain intelligence works better when the system knows which corpus belongs to which workflow.

This session will show how to expose that intelligence through APIs so it can be used across applications, assistants, and agent frameworks. We will cover patterns for packaging vertical context behind traditional APIs as well as newer agent-facing interfaces such as MCP, with a focus on how systems decide which domain endpoint, corpus, or knowledge layer to call at runtime. Attendees will leave with a clearer design model for building APIs that do more than return data. They can act as routing and control layers for domain-specific AI, helping teams build agents that are more accurate, more explainable, and better aligned to the industries they serve.