What Is Cube? The Agentic Analytics Platform Built on a Semantic Layer
Cube is an agentic analytics platform built on a semantic layer. It is designed to serve both AI agents and the humans who work alongside them, giving them governed, reliable business data through MCP and APIs. The core idea is that one semantic model keeps every answer consistent and governed across every surface — whether that surface is a dashboard your data team uses, an AI agent running an operation, or an analytics feature embedded inside your own product.
If you are evaluating Cube, the deciding question is usually whether you need consistent, governed metrics delivered to multiple consumers (people, agents, and customers) without rebuilding your BI stack for each one. Cube is aimed at that problem.
What problem Cube solves
As AI agents become more autonomous, they run more operations and make more decisions without waiting for a person. That creates a data problem: an agent acting on ungoverned or inconsistent numbers can make confidently wrong decisions at machine speed.
Cube's answer is to put a semantic layer underneath everything. The semantic model holds business context, definitions, governance, and permissions. Both humans and agents query through it, so a metric means the same thing whether it appears in a chart, an API response, or an agent's next operation.
The platform describes this as "BI for both humans and AI agents — for your team and your customers."
How the agentic loop works
Cube describes an autonomous loop that runs when a request or trigger arrives. A request can come from a human (a question or instruction) or from an agent (Claude, Codex, or a custom agent), and it can also be triggered by a schedule or a data change.
The loop has four stages:
- Understand request — interpret the question or instruction.
- Work on data model — operate against the semantic model rather than raw tables.
- Run analysis — execute the query or computation.
- Produce result — return output shaped for the consumer.
Results are delivered differently depending on who asked. For humans, Cube produces charts, reports, and dashboards — analytics content ready to use. For agents, it returns governed data and structured results that feed the next operation.
The semantic model, business context, and governance plus permissions sit underneath this loop, which is what keeps agent output grounded in trusted context.
Core capabilities
Semantic layer and data modeling
The semantic layer is the foundation. It keeps definitions consistent across every surface, so your data team governs metrics in one place instead of reconciling conflicting numbers across tools. Data modeling is a first-class capability, along with self-serve analytics and caching and query performance.
AI context layer
Cube provides an AI context layer that supplies agents with governed business data through MCP and APIs. This is the mechanism that lets an agent act on the same definitions your analysts use, rather than on whatever it can infer from raw data.
Embedded analytics
Cube lets you ship AI-powered analytics inside your product without rebuilding your BI stack. It is described as multi-tenant, governed, and built around the semantic layer. There are several integration paths depending on how much control you want:
| Option | What it gives you |
|---|---|
| Chat API | A fully custom AI analytics experience, agent-to-agent capable via MCP |
| Embedded iframes | Analytics Chat and Dashboard iframes — the fastest drop-in path |
| Creator Mode | Full workbook and dashboard creation embedded in your app, so your customers build their own |
| Core Data APIs | Maximum control at the data layer; build any UI on top |
Two properties matter for product teams. First, multi-tenancy is described as "by construction," and governance flows from your model through to your customers' permissions. Second, the embedded surfaces are designed to disappear into your product — your colors, your branding, your agent name.
Who it is for
Cube names two broad audiences:
- Data teams that need governed, AI-native BI with definitions kept consistent across every surface.
- Product teams that want to embed analytics and AI-driven answers inside their own application for their customers.
It also lists industry coverage including financial services, technology, healthcare, and media and advertising, and department coverage including finance and accounting, human resources, marketing, and IT.
Integrations and ecosystem
Cube lists integrations plus partnerships with Snowflake, Databricks, and BigQuery, along with consulting partners. The site also points to documentation, guides, demos, a changelog, customer stories, and community channels on GitHub and Slack.
One customer signal worth noting: the site states that "Brex chose Cube over dbt Semantic Layer and LookML." If you are comparing semantic layer options, that is a concrete data point to follow up on in the customer stories section rather than a general claim about superiority.
How to decide whether to look further
Cube is likely a fit if you recognize this combination:
- You need one governed definition of metrics that holds across BI, embedded analytics, and AI agents.
- You want agents to act on trusted business context rather than raw data.
- You are embedding analytics into a multi-tenant product and do not want to rebuild your BI stack to do it.
- You want to choose your integration depth, from drop-in iframes to full control at the data layer.
It is likely not the right starting point if you only need a single internal dashboard, or if you have no semantic model and no intention of building one — the semantic layer is the premise, not an add-on.
To evaluate it concretely, the useful next steps are the pricing page, the demos, and the documentation, since the integration path you pick (Chat API, iframes, Creator Mode, or Core Data APIs) determines most of the implementation work.