Alation Ontologies
Your business rules, in a model agents can read.
An Alation Ontology is a deterministic model of how your business actually works, including the entities that matter, how they relate, and the constraints that govern them. It gives agents the meaning and rules behind your data, so they reason from fact instead of inference. Every concept binds to the real tables and columns that hold the data.
WHY IT MATTERS
Your agents are guessing at the rules.
An agent sounds just as confident when it's wrong as when it's right, and no risk function signs off on "sometimes right."
Grounded model
A Customer holds Accounts, an Account carries Transactions, and a Transaction can trigger an Investigation. The model states those relationships explicitly, with direction and cardinality, instead of leaving an agent to infer them from foreign keys. Every concept binds to the real tables and columns underneath, so the agent reasons over a knowledge graph of your data, not a picture of it.
One set of rules
Business logic lives in the ontology instead of being rewritten into every system prompt, so a policy change updates every agent at once, instead of breaking them silently. Publish the ontology as an MCP server and every agent reads the same rules, whether it runs in Alation, your own framework, or on a platform you don't control. Open W3C standards: RDF, OWL, SHACL.
Auto-drafted
Point it at your policies, manuals, or an existing ontology. Agents draft the concepts, relationships, and constraints on the glossary, lineage, and policies you already govern, so there is no parallel modeling project. Every facet is editable inline, so your experts correct a draft instead of authoring from nothing. Agent feedback keeps it accurate as the business changes.
Ontologies FAQ
What you need to know about ontologies.
Q1 —
What is the difference between an ontology and a knowledge graph?
An ontology is the model that holds the concepts, relationships, and rules defining how your business works. A knowledge graph is that model grounded in your real data, every concept mapped to the tables and columns in your catalog. An ontology on its own is a diagram. A knowledge graph is that diagram connected to what is really in your systems, so an agent reasons over real data. Most tools give you one or the other. Because AIOS starts from the governed catalog, you get both as one governed object.
Q2 —
Why not use the ontology my data platform vendor already offers?
Because theirs works inside their platform, and your business does not. An ontology is the authoritative model of how your business works, so it should not be locked to one vendor's runtime or perimeter. AIOS Ontologies are built on open W3C standards, govern meaning across your whole stack, not one corner of it, and are exposed over MCP so any agent can consume them. A platform vendor's ontology is built to make their agents better in their environment. This one is yours to take anywhere.
Q3 —
How do agents actually consume an Alation Ontology?
Over MCP. When you publish an ontology, it is exposed as an MCP server that any MCP-compatible agent can read, not just Alation's agents. The model you build feeds whatever you already run, wherever the work happens, without re-implementing it per tool. That is deliberate. The value of a governed model of your business drops the moment it is trapped behind one vendor's agents. Publish once, consume anywhere.
Q4 —
How do we know the extracted model is correct?
You review it. Extraction produces a candidate model of concepts, relationships, and constraints for your experts to correct and approve. It does not publish anything on its own. Every facet is editable inline, so the people who actually know the process refine a draft rather than authoring from nothing, and the model carries their authority when it ships. Reviewing a draft is far faster than building from a blank graph. Nothing goes into production because a model proposed it.
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