# Why Agentic Systems Need Ontologies - Frank Coyle, UC Berkeley

> Agentic AI systems need ontologies as a guardrail layer: symbolic knowledge graphs validate probabilistic LLM tool outputs before agents take real-world actions.

Published: 2026-08-01
URL: https://daniliants.com/insights/why-agentic-systems-need-ontologies-frank-coyle-uc-berkeley/
Tags: ai-agents, knowledge-graph, ontologies, ai-safety

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## Summary

Frank Coyle (UC Berkeley) argues agentic AI systems need ontologies, formal graph representations of entities, relationships, and properties, as a guardrail layer, since LLMs are inherently probabilistic and hallucination is "a feature, not a bug." He frames this as neuro-symbolic AI: pairing LLM-driven agent loops with symbolic reasoning and knowledge graphs to validate outputs before they trigger real actions.

## Key Insight

- **Core thesis:** agent loops (tool call -> check stop reason -> act) have no built-in correctness check. An ontology or reasoner sits outside the LLM and validates the tool's output against domain rules before the agent is allowed to act on it.
- **Proposed pattern, "Pydantic at the door, ontology at the ledger":** use Pydantic to type-check tool call parameters going in, then check the semantic correctness of results against a domain ontology before committing any side-effecting action.
- **Building an ontology, two approaches:** top-down (domain experts define entities and relationships up front, the same method that drove 1980s expert systems, which failed to scale) or bottom-up (derive entities and relationships from real user and customer interaction data as it accumulates).
- **Don't reinvent taxonomies.** Reuse established ones: schema.org (general entity and relationship vocabulary), FOAF ("Friend of a Friend", for social network modeling), Dublin Core (bibliographic and document metadata), DBpedia (the graph that underlies Wikipedia's own search).
- **RDFS and OWL give free inference and constraints** that live outside the graph itself. *Domain and range*: if "teaches" has domain=teacher and range=student, then "Bob teaches Scooter" lets you infer Bob is a teacher and Scooter is a student. *Transitive properties*: "ancestor of" chains automatically, so Sue to Mary to Ann implies Sue is Ann's ancestor. *Functional properties*: "has father" can only have one value, so if two different names both claim to be the same person's father, that is a signal they are duplicate entities for the same individual.
- **Concrete failure modes an ontology catches that plain LLM text reasoning misses:** a second refund issued against the same order; a payout routed to the support rep instead of the buyer (a disjoint-property violation between "customer" and "support rep" roles); an invented status value like "probably shipped" instead of a constrained enum of paid, shipped, refunded.
- **Design rule:** keep agents side-effect-free until validated. Run tool outputs through the ontology check before they are allowed to write to a database, send a payout, or change a record.
- **Historical framing:** expert systems (1980s symbolic AI) couldn't scale; neural nets existed since the 1960s but couldn't scale until GPUs arrived. Agent loops only became Turing-complete once they got iteration (sequence plus conditionals plus loops, per Bohm and Jacopini, 1966), and that same loop capability is what now needs symbolic guardrails to stay safe.