Ontologies: The Missing Guardrail for Agentic AI

Frank Coyle argues that agentic workflows, while unlocking Turing-complete capabilities via loops, introduce serious operational risks: drift, infinite loops, and soaring token costs. The root cause is that LLMs are probabilistic and lack innate constraints. His solution is to pair them with formal ontologies—graph-based domain models—that act as a validation layer.

Using RDFS and OWL inference rules, ontologies can catch logical errors (e.g., double refunds, impossible statuses) that raw text generation would miss. Combined with Pydantic type checking, this creates a two-tier guardrail: structural validation at the interface, semantic validation against domain rules.

The tradeoff is upfront investment in ontology design and governance, which clashes with the fast iteration culture of many AI teams. Coyle acknowledges the burden but points to existing taxonomies like schema.org to shortcut the effort. Without this layer, teams will spend more on debugging, human-in-the-loop escalations, and unpredictable runaway costs.

This neuro-symbolic approach revisits lessons from the expert systems era but adapts them for modern agentic pipelines. It frames reliability not as a model quality problem, but as an architectural deficiency that engineering leaders must address before scaling agents into production.