Presentation: Architecting the Data Layer for AI Agents: From Transactional Systems to MCP and Semantic Models | SpinGraph: Efficiency framing
description: SpinGraph analysis of InfoQ AI / ML / Data Engineering's Presentation: Architecting the Data Layer for AI Agents: From Transactional Systems to MCP and Semanti…
canonical: https://stuffthatspins.com/spin/presentation-architecting-the-data-layer-for-ai-agents-from-transactional-systems-to-mcp-and-semantic-models
markdown: https://stuffthatspins.com/spin/presentation-architecting-the-data-layer-for-ai-agents-from-transactional-systems-to-mcp-and-semantic-models.md
keywords: data mesh
keywords: semantic ontology
keywords: MCP
keywords: The Cushion
keywords: The Hype
published: 2026-08-29T11:00:00+00:00
modified: 2026-08-29T12:45:41.271677+00:00

Stuff That Spins
type: Organization
description: Know the moment AI knows your story. Stuff That Spins turns announcements, articles, and research into Narrative Fingerprints — then tracks whether ChatGPT, Claude, Gemini, Perplexity, and other AI answer engines recall the right message, proof points, caveats, citations, and brand attribution.

Presentation: Architecting the Data Layer for AI Agents: From Transactional Systems to MCP and Semantic Models
type: NewsArticle
alternative headline: Presentation: Architecting the Data Layer for AI Agents: From Transactional Systems to MCP and Semantic Models | SpinGraph: Efficiency framing
description: SpinGraph analysis of InfoQ AI / ML / Data Engineering's Presentation: Architecting the Data Layer for AI Agents: From Transactional Systems to MCP and Semanti…
date published: 2026-08-29T11:00:00+00:00
date modified: 2026-08-29T12:45:41.271677+00:00
main entity of page: https://stuffthatspins.com/spin/presentation-architecting-the-data-layer-for-ai-agents-from-transactional-systems-to-mcp-and-semantic-models
is accessible for free: true
language: en-US
article section: technology
keywords: data mesh, semantic ontology, MCP, token overhead, AI agents
author: InfoQ AI / ML / Data Engineering
publisher: Stuff That Spins
citation: https://www.infoq.com/presentations/enterprise-data-architecture-ai-agents/
about: data mesh
about: semantic ontology
about: MCP
about: token overhead
about: AI agents
about: TOTVS
mentions: InfoQ AI / ML / Data Engineering
mentions: TOTVS
abstract: 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

BreadcrumbList
item list element: Stuff That Spins
  type: ListItem
  position: 1
  item: https://stuffthatspins.com/
item list element: Presentation: Architecting the Data Layer for AI Agents: From Transactional Systems to MCP and Semantic Models
  type: ListItem
  position: 2
  item: https://stuffthatspins.com/spin/presentation-architecting-the-data-layer-for-ai-agents-from-transactional-systems-to-mcp-and-semantic-models

Spin Analysis: efficiency framing
type: AnalysisNewsArticle
description: 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.
about: efficiency framing
  type: DefinedTerm
  description: TOTVS as a pragmatic, forward-looking enterprise architect — balancing innovation with operational discipline.
  term code: The Cushion
additional property: Spin Score
  type: PropertyValue
  value: 55
  unit text: percent
additional property: Narrative Risk
  type: PropertyValue
  value: moderate
additional property: AI Repetition Risk
  type: PropertyValue
  value: moderate
additional property: Likely AI Summary
  type: PropertyValue
  value: TOTVS uses data mesh and semantic ontologies to reduce token overhead for AI agents.
additional property: Narrative Frame
  type: PropertyValue
  value: TOTVS as a pragmatic, forward-looking enterprise architect — balancing innovation with operational discipline.
additional property: Missing Context
  type: PropertyValue
  value: No mention of implementation timeline, rollout scope, or failure modes encountered during integration
additional property: How the Spin Works
  type: PropertyValue
  value: 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.
author: Stuff That Spins
is part of: Presentation: Architecting the Data Layer for AI Agents: From Transactional Systems to MCP and Semantic Models

Extracted Claims
type: ItemList
item list element: ListItem
  position: 1
  item: Claim
    text: 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.
    appearance: 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.
    author: InfoQ AI / ML / Data Engineering

Key Statistics
type: Dataset
description: Extracted statistics from the source narrative
variable measured: core challenge
  type: PropertyValue
  value: token-hungry
  description: Describes AI agents' high computational demand for context tokens