Connecting enterprise data to the age of AI agents

As AI models become more capable and widely available, enterprise success is increasingly expected to depend on reliable, well-governed data rather than model selection alone. Fragmented systems, inconsistent definitions, and unclear authoritative sources can limit an agent’s usefulness even when its reasoning is strong, making high-quality, relevant semantic context more valuable than simply supplying more data. The discussion advocates keeping AI close to structured and unstructured data in integrated platforms, where SQL, vector search, and open formats such as Apache Iceberg can reduce duplication, improve retrieval, and preserve flexibility across clouds and tools. It also presents agent autonomy as a risk-based spectrum, with organizations gradually expanding an agent’s authority according to the reversibility, financial impact, and auditability of its actions. Fivetran, dbt, and Google Cloud are positioned as complementary tools for ingesting source data, building tested business models, and combining data management with AI capabilities.

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