Why Agentic Systems Need Ontologies — Frank Coyle, UC Berkeley
Summary
The talk argues that while Large Language Models (LLMs) and agentic systems represent a powerful shift toward AI, their inherent probabilistic nature makes them unreliable for mission-critical tasks. To mitigate failures—such as incorrect order statuses or double refunds—the solution is 'Neurosymbolic AI': coupling the LLM's generative power with external, formal ontologies. An ontology acts as a logical guardrail, enforcing constraints (e.g., payment status must be one of three values) that are impossible to reliably enforce using prompt engineering alone.
Key takeaways
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The Problem with Probabilistic Agents
LLMs reason probabilistically over domains they only half understand, leading to failures in brittle tools and fragile handoffs. These errors cannot be reliably stopped by mere instructions or prompt engineering.
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Neurosymbolic AI: Guardrails for LLMs
7:04
This approach ties together neural networks (LLMs) with symbolic AI (rule-based systems and ontologies). The ontology provides the necessary guardrails to keep the probabilistic model constrained and honest.
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The Role of Ontologies
8:43
An ontology is a formal specification of a shared conceptualization, defining typed entities, their relationships, and constraints. It allows systems to validate proposed actions (e.g., ensuring an order can only be refunded once).
Technical details
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Ontology Structure and Standards
984s
Ontologies are represented using standards like RDFS (Resource Description Framework Schema) and OWL (Web Ontology Language). These technologies allow for defining constraints, such as: 1. **Domain/Range:** Inferring entity types based on relationships (e.g., if 'teaches' has a domain of 'teacher', the subject must be a teacher). 2. **Functional Properties:** Enforcing uniqueness (e.g., an individual can only have one father).
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Agent Execution Flow and Validation
A robust agent loop must validate LLM outputs at multiple stages: First, use **Pydantic** to check the types of proposed tool parameters. Second, pass the resulting data through an ontology validator (the 'ledger') to ensure logical consistency against domain constraints before allowing any action.
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Agent Limitations
LLMs can only predict the next word with high probability; they cannot execute actions. Therefore, external tools and validation layers are mandatory to translate LLM intent into reliable system calls.
Mentioned resources
- RDFS
- OWL (Web Ontology Language)
- Pydantic
- Schema.org / DBPedia
Channel & topics
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