Architecting the Data Layer for AI Agents: From Transactional Systems to MCP and Semantic Models

Most conversations about production AI agents focus on the agent itself — the prompts, the orchestration, the framework. But the moment you put an agent in front of real enterprise data, a different problem dominates: the data layer wasn’t designed for this consumer. Data lakes were built for analysts and dashboards. Transactional systems were built for applications. Neither was built for a non-deterministic, token-hungry, latency-sensitive reasoning loop that may issue thousands of unpredictable queries per minute.

This talk takes the data architect’s view of agentic systems. We’ll walk through the architectural decisions that determine whether your agents are reliable and affordable in production — or quietly bankrupting your team.

You’ll learn how to draw the line between deterministic and non-deterministic computation, and why getting that boundary right is the single biggest reliability lever you have. We’ll cover when to route an agent to a transactional system versus a data lake, and what each choice costs in latency and consistency. You’ll see how to add low-latency serving layers to a traditional data lake to make it agent-ready, how to design MCP servers that scale and stay secure under agent-driven traffic, and how a semantic layer can dramatically reduce hallucinations by translating raw schemas into agent-native concepts.

We’ll also tackle the topic that quietly kills most agent projects: token economics. We’ll share concrete patterns to keep agent workloads financially viable as they scale.

If you’re moving agents from prototype to production, you’ll leave with a decision framework — and a clear mental model for the data stack that has to exist beneath every reliable AI system.