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Grounded Agents: Annotating Ontologies with Tool Definitions
Knowledge → Semantics → Action
In knowledge-driven systems, we often talk about ontologies as if they were the end of the story: formal classes, relationships, and taxonomies that encode domain concepts. But if your goal is to build intelligent systems that interact with the world, that make decisions, call APIs, and execute tasks: then an ontology needs to be more than static knowledge. It needs semantics that map cleanly to executable behavior.
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In this post I’ll walk through a pattern for annotating an ontology with tool definitions, so that an AI agent doesn’t just understand concepts, but also knows what it can do with them. The technical stack we’ll use is:
- Neo4j — for the semantic knowledge graph
- LangChain — for tool definition and execution
- LangGraph — for orchestrated reasoning and multi-step workflows
The end result is an architecture where your agent reasons over structured knowledge and then acts in grounded, verifiable ways — no more hallucinated APIs.
The goal is not clever prompting.
The goal is grounded, semantic execution.
