Ontology Layer
Defining what the agent is to the user
Defining what the agent is to the user
Without a clear ontological definition, agents behave unpredictably. Users either over-trust or under-trust the AI, leading to friction, surprise, or total abandonment.
Agentic Use Case Examples
Category
Healthcare & Life Sciences
Example
Clinical assistant defines its role as a supportive recommender, not a diagnostician.
Industry
Transportation & Logistics
Example
Routing agent balances autonomy vs. dispatch control for trust in critical logistics.
Industry
AI & Automation Infra
Example
Agent mesh defines boundaries between copilots to avoid overlapping behaviors.
Industry
Digital Identity Systems
Example
Credential agent clarifies whether it’s verifier, controller, or recommender in identity flow.
Applying the Ontology Layer in Product Development
1
Solves for: Undefined agent role creates stakeholder misalignment.
Solves for: Users misunderstand whether the agent is a tool or a partner.
Solves for: Early design decisions ignore user expectations of autonomy.
Project Kickoffs
1
Solves for: Undefined agent role creates stakeholder misalignment.
Solves for: Users misunderstand whether the agent is a tool or a partner.
Solves for: Early design decisions ignore user expectations of autonomy.
Project Kickoffs
1
Solves for: Undefined agent role creates stakeholder misalignment.
Solves for: Users misunderstand whether the agent is a tool or a partner.
Solves for: Early design decisions ignore user expectations of autonomy.
Project Kickoffs
2
Solves for: No shared definition of the agent’s identity across teams.
Solves for: Agent behavior surprises or confuses users.
Solves for: Misalignment between agent UI cues and its actual autonomy.
Audits
2
Solves for: No shared definition of the agent’s identity across teams.
Solves for: Agent behavior surprises or confuses users.
Solves for: Misalignment between agent UI cues and its actual autonomy.
Audits
2
Solves for: No shared definition of the agent’s identity across teams.
Solves for: Agent behavior surprises or confuses users.
Solves for: Misalignment between agent UI cues and its actual autonomy.
Audits
3
Solves for: New features conflict with the agent’s established role.
Solves for: Added functionality shifts perceived identity without clarity.
Solves for: Behavior creep leads to broken mental models of control.
Feature Reviews
3
Solves for: New features conflict with the agent’s established role.
Solves for: Added functionality shifts perceived identity without clarity.
Solves for: Behavior creep leads to broken mental models of control.
Feature Reviews
3
Solves for: New features conflict with the agent’s established role.
Solves for: Added functionality shifts perceived identity without clarity.
Solves for: Behavior creep leads to broken mental models of control.
Feature Reviews
4
Solves for: No framework to probe users’ mental models of the agent.
Solves for: Users report discomfort with how proactive the AI is.
Solves for: Interviews reveal a mismatch between intended and perceived agent roles.
User Research
4
Solves for: No framework to probe users’ mental models of the agent.
Solves for: Users report discomfort with how proactive the AI is.
Solves for: Interviews reveal a mismatch between intended and perceived agent roles.
User Research
4
Solves for: No framework to probe users’ mental models of the agent.
Solves for: Users report discomfort with how proactive the AI is.
Solves for: Interviews reveal a mismatch between intended and perceived agent roles.
User Research
Real-World Use of the Ontology Layer
Spotify and Netflix use ontologies, structured knowledge graphs linking genres, moods, artists, and themes, to personalize recommendations. This enables the AI to move beyond simple pattern matching, surfacing content that aligns with a user’s deeper preferences.
Spotify and Netflix use ontologies, structured knowledge graphs linking genres, moods, artists, and themes, to personalize recommendations. This enables the AI to move beyond simple pattern matching, surfacing content that aligns with a user’s deeper preferences.
Spotify and Netflix use ontologies, structured knowledge graphs linking genres, moods, artists, and themes, to personalize recommendations. This enables the AI to move beyond simple pattern matching, surfacing content that aligns with a user’s deeper preferences.
Google Search uses ontologies to disambiguate entities (e.g., Jaguar the car vs. Jaguar the animal), enabling more accurate results by linking queries to real-world concepts and their relationships.
Google Search uses ontologies to disambiguate entities (e.g., Jaguar the car vs. Jaguar the animal), enabling more accurate results by linking queries to real-world concepts and their relationships.
Google Search uses ontologies to disambiguate entities (e.g., Jaguar the car vs. Jaguar the animal), enabling more accurate results by linking queries to real-world concepts and their relationships.
Stripe’s Agent Toolkit lets AI agents autonomously create payment links, issue cards, and handle billing using secure, pre-defined API roles, establishing clear authority.
Stripe’s Agent Toolkit lets AI agents autonomously create payment links, issue cards, and handle billing using secure, pre-defined API roles, establishing clear authority.
Stripe’s Agent Toolkit lets AI agents autonomously create payment links, issue cards, and handle billing using secure, pre-defined API roles, establishing clear authority.
IBM Watson applies domain-specific medical ontologies to structure diseases, treatments, and outcomes, ensuring clinical recommendations are based on validated medical knowledge.
IBM Watson applies domain-specific medical ontologies to structure diseases, treatments, and outcomes, ensuring clinical recommendations are based on validated medical knowledge.
IBM Watson applies domain-specific medical ontologies to structure diseases, treatments, and outcomes, ensuring clinical recommendations are based on validated medical knowledge.
Microsoft Bing’s Satori knowledge graph connects billions of entities, enabling its AI to answer factual questions with confidence and support conversational search with structured, contextual understanding.
Microsoft Bing’s Satori knowledge graph connects billions of entities, enabling its AI to answer factual questions with confidence and support conversational search with structured, contextual understanding.
Microsoft Bing’s Satori knowledge graph connects billions of entities, enabling its AI to answer factual questions with confidence and support conversational search with structured, contextual understanding.