AI & ML

From RAG to Agents: Productionizing Generative AI

Elena Rossi·April 30, 2026·8 min read

Retrieval-augmented generation is the on-ramp. Agentic workflows are where the value compounds — if you can govern them.

Most enterprises start their Generative AI journey with RAG: ground a model in your own documents and answer questions. It works, it's safe, and it builds organizational confidence.

The next step is agents — systems that don't just answer but act, calling tools and chaining steps. With Mosaic AI and a governed feature and function layer, those tool calls can be audited and permissioned just like any other data access.

The teams that win treat agents as software: versioned, evaluated against a test set, and observable in production. The model is the easy part; the platform discipline is the moat.

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