There Is No Such Thing as Close Enough: Why AI Agents Need Complete Access to Enterprise Data

Enterprise AI agents can produce confident but inaccurate answers when connectors provide only partial access to source systems, as illustrated by Workday compensation data that is unavailable through REST but accessible through WQL and SOAP services. The passage argues that reliable enterprise connectivity requires more than endpoint coverage, encompassing access to all relevant APIs, custom data models, read-and-write operations, source-side query execution, system-specific context, and ongoing maintenance as APIs evolve. Query pushdown is presented as essential for reducing token use and improving accuracy by performing filtering, joins, and aggregations in the source system rather than in an LLM’s context window, while secure write-back requires user-level permissions and audit trails. It positions CData Connect AI, a managed MCP server, as a solution offering broad API access for systems such as Workday, NetSuite, and Salesforce, dynamic schema discovery, source-executed queries, read/write actions, per-user credentials, logging, and managed connector updates. The piece cites public and internal benchmarks to support claims that deeper connectivity and source-level execution improve agent accuracy and efficiency compared with shallow connectors and model-routing gateways.

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