“Common Sense🧠” for AI Agents

Ontologies are Transforming AI Agents from Blind to Brilliant.

AI Generated Image (ChatGPT)

AI agents are built to know millions of facts, but do they understand how those facts relate to one another? Can they distinguish between a supplier and a customer? Can they understand that an employee belongs to a department, which belongs to an organization?

AI agents are great at pattern matching. They’ve seen thousands of examples and can guess what comes next. But guessing isn’t understanding. Understanding means knowing why things matter and how they connect.

Ontologies can teach AI agents to actually understand.

Ontologies are like brains. To put it simply.

It’s like a blueprint or map that shows how things connect to each other. It won’t just list or store data. It’s like saying “Here’s how our business actually works. Here are the rules, the relationships, and constraints.”

When you give an AI agent an ontology, you give it the structure of your world and a context that it can use to think.

To illustrate this simply, imagine an AI agent built for clinical workflow assistance. Let’s call it Ned🤖.

Ned’s job is to help organize patient care. From tracking appointments and patients diagnostic history to prescriptions, current treatments, and anything else that needs attention.

The goal here is to help doctors make smarter safer decisions quickly by keeping the whole picture visible.

If Josh comes into the hospital with chest pain and shortness of breath (SOB), Ned can help the doctor figure things out faster, like what’s going on and what to do next.

On the back side of things, Josh has a patient history, current medications, allergies, past conditions and other stuff that are stored in his hospital record.

Without an ontology, Ned🤖could reason like: Patient presenting with chest pain and SOB; Query the symptom database; return probable diagnoses; could be cardiac. Ned🤖might also suggest drugs to use, like aspirin, nitroglycerin, or something.

Ned🤖 doesn’t see that Josh is allergic to aspirin, he’s currently on blood thinners (which interact dangerously with nitroglycerin), and his last appointment note mentions anxiety disorder.

Ned🤖 is flying blind, and the result is a recommendation that could harm Josh.

With an Ontology, Ned🤖 will understand the full clinical structure.

The ontology will establish that a patient has appointments with doctors 👉🏻 patients have diagnoses 👉🏻 diagnoses have prescriptions for treatments 👉🏻patient has a history 👉🏻doctors have expertise 👉🏻prescriptions have constraints (who shouldn’t take and what it interacts with).

So instead of a simple recommendation, Ned🤖 could reason like this :

“Patient presenting with chest pain and SOB. Could be cardiac, but could also be anxiety given history. This doctor specializes in cardiology, but I still need to flag before recommending aspirin/nitroglycerin: Patient is allergic to aspirin and is currently on anticoagulants (blood thinners) that interact negatively with nitroglycerin.

Recommendation: Order EKG first. Involve pharmacist to verify any safe alternatives. Consider psychological assessment given anxiety history. Let the doctor decide the best path with this full context.”

Disclaimer: The above probably isn’t correct health advise!!!

The scenarios with and without an ontology is the same, but the outcomes are different. Except that in the latter, Ned🤖 isn’t just retrieving information, It’s reasoning with context 💊.

What’s the point of all these? 🤝

The point is that even though AI systems can make predictions based on patterns in historical data, there’s also the gnawing fact that most complex businesses aren’t just patterns. They’re a system of relationships, protocols, constraints, and risks.

A diagnosis, for example, doesn’t exist in isolation. It connects to treatments, which connects to patient history, which connects to a doctor, and so on. If one relationship is missed, the whole picture is missed as well.

Businesses and decision makers are starting to care about answering questions like “Why did the AI agent recommed this?” when implementing AI in thier environments. That’s the backbone question for explainable and trustworthy AI implementation which an ontology is likely to provide.

Without an ontology? Good luck.

The AI agent makes a recommendation, but the logic behind the recommendation can’t be traced. Is it based on good evidence? Or is it based on vibes and hallucinations?

🌟Ontologies aren’t new technology. But AI agents are changing their importance. Prospective AI agents across industries, are likely to be judged not only by how well they talk, but also by how well they think.

And for organizations building the next generation of autonomous systems, especially in healthcare, finance, and critical infrastructure, an ontology might just be the difference between a liability and a lifesaver.

Ontologies are how you make trust happen. They’re how you teach an AI agent to grasp not just what happens, but why it happened and why it matters.

And in the age of AI agents, understanding may be what truly defines the “intelligence” in “artificial intelligence”. 🚀

AI Generated Image (Copilot)

Want to see ontologies in action? Microsoft’s Ontology Playground lets you explore real-world examples of ontology designs like the healthcare system, Fourth Coffee, and others. Check it out: Microsoft Ontology Playground.