Enterprise Data Access for AI Agents: What Model Routing Doesn't Cover

Model routing and enterprise data access are presented as separate layers of an AI application stack: routing gateways manage LLM selection, fallback, costs, rate limits, caching, and observability, while data-access layers connect agents to live business systems such as Salesforce, Snowflake, SAP HANA, and PostgreSQL. The passage argues that routing has become relatively standardized through OpenAI-compatible APIs and increasingly competitive open-source models, whereas enterprise data access remains difficult because each source has distinct authentication, schemas, business definitions, permissions, and API limits. Model Context Protocol standardizes agent-to-tool communication but does not solve the integration logic within each tool, creating an N-by-M connector burden as organizations add agents and data sources. A dedicated data-access layer is described as providing semantic schema resolution, source-system permission passthrough, per-query audit records, and MCP-native access, helping agents return contextually meaningful results without exceeding users’ access rights. The discussion also links this layer to compliance needs under GDPR Article 30 and the EU AI Act’s logging provisions, since model-routing logs reveal model usage and cost but not what enterprise data an agent accessed. CData Connect AI is positioned as a managed implementation intended to replace many custom integrations with governed connections to enterprise data sources.

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