7 Essential Data Requirements for Building Agentic AI Solutions

Reliable agentic AI depends on a data layer that provides comprehensive access to relevant sources, high-quality labeled data and feedback loops, real-time or low-latency information where needed, unified schemas and semantic definitions, strong governance and security, observability with lineage and versioning, and elastic infrastructure with cost controls. The discussion argues that agents can make unreliable autonomous decisions when they lack access to current, trusted, consistently defined data, particularly across systems that use different meanings for common concepts such as customers. It contrasts batch data, which can become stale between scheduled updates, with streaming access for live operational decisions, and emphasizes source-level permissions, audit logging, privacy protections, and monitoring for drift, bias, and performance regressions. CData Connect AI is presented as a managed Model Context Protocol platform intended to address these requirements by connecting agents to hundreds of sources through live SQL-based access without replication, applying source permissions and authentication controls, translating schemas and semantic context, and logging queries for governance and auditing.

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