Ontologies for AI Agents

Agents fail quietly when they do not share a definition of the world with the systems they act on. This guide shows how to write an ontology for an agent: the classes it needs, the relationships it can traverse, the constraints that keep it inside the lines, and the formats that let a model consume all of it without a semantic web degree.

What you will be able to do

• Scope an ontology to the decisions an agent actually makes.
• Model entities, properties, and relationships in a form a language model can use.
• Express constraints and permissions as part of the ontology instead of prompt text.
• Serialize the ontology into tool schemas, system context, and retrieval filters.
• Version an ontology as the product and the agent evolve.

Inside the guide

1. 01

   What an agent needs to know

   The difference between knowledge an agent retrieves and knowledge it must be given up front.

2. 02

   From domain to classes

   Identifying entities, drawing boundaries, and resisting the urge to model everything.

3. 03

   Relationships and traversal

   Defining the relationships an agent is allowed to follow and the ones it must not infer.

4. 04

   Constraints, rules, and permissions

   Encoding invariants and authorization as ontology, not as scattered prompt instructions.

5. 05

   Serialization for models

   JSON Schema, tool definitions, compact text summaries, and when to use each.

6. 06

   Runtime patterns

   Loading the ontology into context, filtering retrieval with it, and validating actions against it.

7. 07

   Versioning and governance

   Changing an ontology without breaking agents already in production.

From the guide

An ontology for an agent is not a philosophy project. It is the smallest set of definitions that lets the agent tell the difference between the things it may act on, the things it may only read, and the things it should not know exist. Start from the actions, work backwards to the entities those actions touch, and stop when every action is fully described.

Who it is for

• Engineers building agents that act on business systems.
• Product and data leads who need a shared vocabulary between teams and models.
• Architects deciding where domain knowledge should live.