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

Fabiane Nardon: My name is Fabiane. I work for TOTVS. TOTVS is probably the biggest tech company you never heard of. This company has been building enterprise systems for the last 40 years in Brazil: SaaS, on-premise, everything in between. About a quarter of Brazilian GDP runs through our systems. Brazil is not a small market, it's the 10th largest economy in the world. When we started building AI agents, our problem was not lack of data, it was the opposite. We had too much data and none of it was prepared to be accessed by agents. We evolved a lot in agent building in the last few years. We have now several agent frameworks, agent orchestrations, or evals. Is our data prepared to be accessed by a token-hungry, latency-sensitive reasoning loop that can fire hundreds of unpredictable queries in a few minutes? The data we have in transactional systems was optimized to be accessed by applications. The data we have in data lake was optimized to be accessed by data analysts or dashboards. None of this data was optimized to be accessed by AI agents. Then we have another problem, because when we talk about enterprise-grade agents, we are…