Your data was documented for humans. Agents need something else.
Dashboards got a gold layer built for analysts. Agents need one built for machines — that's the control plane.
Warehouses were built for analysts, not agents.
Tribal knowledge, not documented
Which revenue column is real, why customer data lives in three tables, what the status codes mean — a human analyst carries it, an AI agent doesn't.
Guessed SQL, confident wrong answers
Connecting a chatbot straight to raw schemas doesn't create insight — it creates hallucination with a SQL accent.
The control plane, built for machines.
Denormalized and unambiguous
Flattened and explicitly documented, built for how machines read — not how analysts click through a BI tool.
Built from Agent Views
Served over MCP, consumed by Claude, Copilot, or any MCP-compatible agent.
Better prompts can't fix hostile data.
Text-to-SQL guesses at query time
Dagen removes the guessing before the question is ever asked.
The model needs to read, not be clever
That's the difference between a demo that impresses and an answer you'd put in front of a CFO.
From raw schema to agent-ready in four steps.
Understand the data
Dagen's agents study your schemas, query history, and existing models to understand what your data means, not just what it's called.
Generate Agent Views
Dagen generates the pipelines that produce your Agent Views: flat, documented, and ready for machine consumption. Your engineers review and approve what gets built.
Connect over MCP
Agent Views are exposed through the Dagen MCP server. Claude connects and asks questions in plain language — so does any MCP-compatible agent your team runs.
Stay fresh and healed
The agentic runtime keeps Agent Views fresh as sources change, recording every question pattern, change, and fix with full lineage from source to answer.
Questions will build the pipelines.
Today, when an agent asks a question your data can't answer, that's the end of the conversation. Soon, it'll be the start of one — Dagen will detect the gap, propose the pipeline that closes it, and build it once your engineer approves. The question creates the pipeline. That's what Dagen is built for.
Connecting AI to your data, answered.
How do I connect Claude to Snowflake or my data warehouse?+
Through MCP. Dagen builds Agent Views on top of your warehouse and serves them through its MCP server. Claude connects to Dagen and queries agent-ready data instead of raw schemas.
What is an MCP server for a data warehouse?+
MCP is the open protocol AI agents use to reach external systems and data. Dagen's MCP server exposes your Agent Views so any MCP-compatible agent can query them with governance and lineage intact.
Does this work only with Claude?+
No. Claude is the flagship example, and any MCP-compatible agent can consume Agent Views.
Is this safe to expose to an AI agent?+
Agents see Agent Views, not your raw estate. Your engineers approve what gets built and what gets served, and every access is recorded with full lineage.
Watch Claude answer from your data.
Book a technical demo and bring a question your data should be able to answer. We'll show you what it takes to make that answer trustworthy.