Why Most Healthcare AI Can't Handle Real Patient Conversations
Amigo’s Dynamic Behaviors allow AI agents to recognize what matters during clinical conversations and adapt in real time.
This is part three in a five-part series diving into the Amigo Cognitive Architecture. We’ve already covered Functional Memory and Context Graphs, and in upcoming posts, we will explore Actions and the Agent Core.
Why Can't Healthcare Agents Think Like Doctors?#
Consider the following exchange between a patient and an AI intake agent.
The patient says:
"My chest feels a bit tight.
The AI agent replies:
"I’m sorry to hear that—chest tightness can be commonly caused by anxiety, acid reflux, or muscle strain. Let’s continue with your intake. What is your date of birth?
A doctor would never respond this way. A good clinician listens to patients with a running list of hypotheses in mind, constantly reassessing new information to determine what matters and what the next step should be. When did the symptoms start? Has the patient started taking any new medications recently? Does their medical history make anything more likely? The conversation itself is diagnostic and each answer reshapes the questions that follow.
We call this clinical judgment - the ability to recognize when something is important and respond accordingly.
Most AI agents in healthcare today can’t do this. Some are built to execute a set of predefined workflows very well but aren’t able to change course when new information surfaces. Other agents are able to have more flexible conversations but lack the infrastructure to process new context in a structured way. As a result, they struggle to ask the right follow-up questions and determine the best path forward in real time.
Dynamic Behaviors are what allow Amigo agents to exercise clinical judgment like human clinicians. But building this kind of adaptability requires solving a fundamental problem in how AI models access memory and apply knowledge. Traditional models often struggle here due to what we call the latent space activation challenge.
The Latent Space Activation Challenge#
AI models can possess the right clinical knowledge and still fail to apply it correctly.
Inside every large language model (LLM) is a latent space, or a massive library where all its knowledge is stored. The library is vast, and success at knowledge retrieval depends on whether the model can locate and combine the right pieces of information for the problem at hand. When this happens without strong control architecture in place, the model is simply guessing. It may solve the wrong problem or prioritize incorrectly, often producing surface-level outputs that look right but lack real understanding. In healthcare, this can be dangerous.
Consider the patient experiencing chest pain. It could be caused by acid reflux. It could also be a sign of a heart attack. A physician knows this, but that knowledge is only useful if they're able to ask the right questions.
AI models face the same issue. All the foundational medical knowledge is there, but accessing it incorrectly produces inconsistent, irrelevant, and unsafe results. Dynamic Behaviors are an architectural innovation that solves this problem by ensuring that Amigo agents activate the right clinical knowledge at the right moment.
How AI Agents Navigate Complex Conversations#
In our previous Deep Dive, we discussed how Context Graphs give Amigo agents a structured yet flexible roadmap for clinical conversations. You can think of Context Graphs as the scaffolding that breaks down complex clinical workflows into connected states, giving agents reliable options to intelligently navigate through intake questions, treatment discussions, or care planning. This solves the problem of agents getting lost or going completely off-script.
But structure alone isn’t enough. Clinical conversations can be unpredictable, and patients will sometimes have unusual queries. An agent might be halfway through a medication review when the patient mentions something unexpected or concerning.
Dynamic Behaviors allow Amigo agents to recognize these critical moments and override the conversation to deliver nuanced responses without abandoning the underlying structure underneath. This keeps patient interactions flexible and natural, instead of feeling like rigid, predetermined conversational pathways.
How Dynamic Behaviors Work#
From an architecture perspective, Dynamic Behaviors act as a trigger-and-response mechanism relying on two components: triggers and instructions.
Triggers detect patterns that signal when a behavior should activate. These can range from explicit keywords (“I have chest pain”) to subtle contextual cues (a patient repeatedly deflecting questions about medication adherence).
Amigo’s trigger system doesn’t rely on a single signal to decide when a behavior should activate. Instead, it draws on multiple cues, such as the agent’s internal reasoning, past responses, patient context, and tool usage. When these signals combine, each new behavior is informed by the full history of the patient relationship rather than just the last message. As a result, Amigo agents are able to recognize patterns that would be invisible to simpler systems.
To illustrate, consider a patient who consistently reports low energy, sleep disruption, and loss of interest in activities they previously enjoyed. Even if they don’t explicitly report feeling depressed, a trigger can activate to prompt the agent to explore this possibility further.
Instructions define how the agent responds once triggered. They can be broad, giving the agent discretion to adapt, or highly detailed, requiring strict adherence to a defined protocol. A safety escalation might have rigid instructions, such as immediately advising the patient to call 911, whereas a trigger for exploring lifestyle factors might give the agent more flexibility in how it approaches the conversation. These instructions can also tell the agent to perform external actions, like placing a call to a specialist’s office or writing to an EHR.
Why Dynamic Behaviors Matter for Healthcare AI#
Dynamic Behaviors change what’s possible in clinical AI.
- Increased safety: Rather than relying on patients to explicitly state their concerns, Amigo agents can detect risk based on implied patterns or contextual triggers, then control the response based on custom-defined safety protocols.
- Improved patient experience: Conversations feel natural rather than scripted because Amigo agents respond to what patients are actually saying rather than what the workflow anticipated. Patients rarely take the happy path, and a robust AI experience must be built for edge cases.
- Execute complex workflows: Dynamic Behaviors can trigger tools, connect with external systems, or pull live data mid-conversation.
Deliver Responsive, High-Quality Care with Amigo#
Dynamic Behaviors allow Amigo agents to recognize critical moments in clinical conversations and respond appropriately, whether that means asking different questions or escalating to a clinician. The result is healthcare AI that can deliver responsive, high-quality care at scale.
Ready to build healthcare agents that do more than follow a script? Book a call with us today.
