Presentation: Architecting the Data Layer for AI Agents: From Transactional Systems to MCP and Semantic Models | SpinGraph: Efficiency framing
SpinGraph analysis of InfoQ AI / ML / Data Engineering's Presentation: Architecting the Data Layer for AI Agents: From Transactional Systems to MCP and Semanti…
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Presentation: Architecting the Data Layer for AI Agents: From Transactional Systems to MCP and Semantic Models
data mesh, semantic ontology, MCP, token overhead, AI agents
TOTVS is rearchitecting its data layer to serve AI agents efficiently Focus is on reducing token overhead while maintaining precision, security, and cost control Techniques include data mesh, low-latency databases, semantic ontologies, and dynamic MCP selection
Emphasizes optimization and readiness while minimizing uncertainty about agent reliability, real-world latency tolerances, or whether these patterns scale beyond TOTVS’s internal use cases.
TOTVS as a pragmatic, forward-looking enterprise architect — balancing innovation with operational discipline.
TOTVS uses data mesh and semantic ontologies to reduce token overhead for AI agents.
No mention of implementation timeline, rollout scope, or failure modes encountered during integration
Combines practitioner authority (Fabiane Nardon), concrete technical terms (data mesh, MCP, semantic ontologies), and problem-solution framing ('token-hungry' → 'optimize') to make architectural complexity feel like disciplined efficiency — even though no evidence confirms the claimed optimization actually occurred or generalizes beyond this one implementation.
TOTVS uses data mesh, low-latency database architectures, semantic ontologies, and dynamic MCP tool selection to optimize context windows and reduce token overhead in transactional systems.
Nardon details using data mesh, low-latency database architectures, semantic ontologies, and dynamic MCP tool selection to optimize context windows and reduce token overhead in transactional systems.
Extracted statistics from the source narrative
Describes AI agents' high computational demand for context tokens