Agentic AI · The data foundation
Your agents can do more. With data they understand.
A data warehouse for agentic AI needs to provide more than tables. lavalake connects business data with its meaning and makes it available through tools for AI agents, using the permissions of the person they work for.
Find context. Query metrics. Put the results to work.
- Understand
- Query
- Act on results
Data warehouse for agentic AI
Metrics with meaning
Define revenue, margin and other metrics in a shared semantic layer. People and agents work with the same business definitions.
Tools for the next step
Through the built-in MCP server, agents explore data, query metrics and create dashboards. Changes follow the applicable approval steps.
Permissions that carry through
Agents use the user's delegated identity. Row and column policies apply to AI queries, and tool calls are recorded in the audit log.
Put it to work
For teams bringing agents into everyday work
A sales agent needs to investigate a revenue variance. Finance wants the same margin definition in chat and dashboards. An internal assistant needs access to data and documents. lavalake provides a shared data foundation for these tasks.
From a business question to a traceable result
Understand
The catalog, table descriptions and defined metrics provide business context.
Query
The agent selects MCP tools for SQL, metrics or hybrid search.
Act on results
Explore the findings and bring them together in a dashboard after approval.
Your questions, answered
What makes lavalake suitable for agentic AI?
lavalake combines an Iceberg lakehouse, defined business metrics, hybrid search and an MCP server. Agents get data access and context through named tools. This combination helps agents work on business tasks using traceable data.
Can I connect my own agents?
Yes. Compatible agents can connect through the MCP server. The MCP documentation covers authentication, available tools and approvals.
Do people stay involved in changes?
Tools are classified by their effects. Mutating and destructive actions follow the applicable confirmation steps and are recorded in the audit log.
Connect your agent to the data it needs.
Let's explore your data sources, your use case and the right starting point together.