It’s never been easier to create a great AI agent demo – even in healthcare. But the speed from ideation to demo and the ease with which a demo can be created can hide a very important fact: One impressive AI interaction doesn’t mean you’ve found or built the right solution; it just means the demo worked. 

Or, to put it another way: A good AI agent demo is easy to fake. A great AI agent experience is hard to repeat.

Behind an AI agent that works autonomously, and is actually trustworthy and safe for use in healthcare, is a lot of work that’s impossible to confirm via a demo. Take, for example, everything that goes into keeping Infinitus AI agents safe – before, during, and after calls: 

None of that comes into play during a demo. That’s a big reason why we advise anyone considering an AI agent solution to thoroughly test it – and we mean thoroughly. To truly evaluate an AI agent, you need to go beyond the highlight reel. You need to ensure it works correctly, every time, even if it encounters the unexpected. 

As an example, in real-world testing at Infinitus, our AI agents have called the same payor multiple times and received different answers to the same question. What matters isn’t how an AI agent performs when everything goes right the first time. It’s proving that accuracy is repeatable, again and again, and that the AI agent can handle the unexpected. Only then can you understand whether the agent delivers consistent, reliable performance.

Or another example: We know from our conversations with payors that phone calls to their call centers are tracked in detail, and that every second counts. Yet it’s extremely challenging to add filtering to existing AI models – meaning if the demo has leveraged an existing model as-is, the demo might be easy to launch, but the real work required to streamline the AI agent’s conversation (and remove the “uh”’s and “um”’s becoming popular in the latest generation of voice models) won’t be. And that’s to say nothing of the need to follow SOPs and ensure safety in every conversational turn.

The concerns extend to calls between AI agents and patients, too. Appointment scheduling has become a popular use case in this realm, but it’s incredibly easy to fake confirming an appointment time or even to fake a warm handoff to a provider’s office receptionist. And what about situations where the stakes are extremely high – for example, a patient is having a medical emergency? Can you be sure the AI agent handles such a crisis appropriately every single time? 

All of this is to say that what happens in a demo may not be reality. The only way to understand what’s real is to test the solution. Then test it again. And again. And again. With that in mind, here are five practical tips to keep in mind for the AI agent testing process:

  • Don’t only compare to human work: Human error and variability lead to an almost 30% difference in answers between different calls, so if you’re only comparing the AI’s performance to humans, think again. Accuracy, overall, is the metric to track.
  • Include edge case scenarios: Make sure to test invalid, incomplete, and ambiguous inputs, repeatedly, to understand how the AI agent navigates “off the happy path” situations.
  • Test more than just the model: Simulate the full user journey from initial input to response, to ensure the UI/UX is serviceable, the AI agent can handle API integrations, and errors are appropriately handled.
  • Test in series of batches: Consistency is key. Consider testing in a series of batches to ensure quality metrics are always met.
  • Remember – AI agents aren’t magic: AI might feel like magic, but in reality, the AI needs access to the same information human teams have in order to succeed. Don’t hold back any data during testing.

A successful, thorough testing process is how you separate a good demo from a real solution. And only then can you understand whether the AI agent delivers consistent, reliable performance. 

That’s why we’re building Infinitus to earn trust through performance, over and over again. Want to experience repeatable, real-world performance? Let’s talk.