Deep Dive: Functional Memory

This is the first in a five-part series diving into the Amigo Cognitive Architecture. In upcoming posts we will explore Context Graphs, Dynamic Behaviors, Actions, and the Agent Core.

Traditional AI memory systems are prone to breakdowns at the most inopportune times. They may forget essential context or misinterpret information. In healthcare, where accuracy and timeliness are critical to patient safety, this is unacceptable.

Amigo solves this problem with a twofold approach:

1. A functional memory system that enables hyper-optimization for different clinical use cases
2. A layered memory architecture to prevent information density explosion

Let’s take a deep dive into Amigo’s functional memory system and how it solves the challenges unique to healthcare.

Functional Memory: Remembering What Matters

To understand what this means, it’s helpful to describe the dimensional user model framework that serves as the blueprint for the functional memory system.

The user model is a set of dimensions that determines how to categorize information through a specific clinical lens – including what information to store forever (e.g., family history) vs. capture ephemerally (e.g., casual conversations) and how to preserve and decay each data type appropriately. What a patient’s PCP considers important to remember about them is different from what their orthopedic surgeon considers important, and a single piece of information may be interpreted in different ways. As a result, user model dimensions are designed to be unique to each specialty, service, and even clinic.

Through viewing each new piece of information against the holistic understanding of the patient, Amigo’s functional memory system can also recontextualize past data and arrive at novel insights that are missed via purely additive approaches. When a patient is diagnosed with ADHD, Amigo can re-evaluate a six-month-old complaint about concentration difficulties. This re-analysis can surface new patterns that were not apparent during the original interaction. As a result, the agent’s memory becomes more clinically relevant over time. Recontextualization also allows for temporal pattern recognition, allowing the agent to understand how a patient’s health evolves over time and distinguish between temporary pain and chronic condition progression.

Another crucial insight to consider is that even the definition of what matters is subject to change, as medical or regulatory stances evolve over time. Amigo’s functional memory system was built to allow for the continuously updating of user model dimensions without losing fidelity of past memories – the system can perform a retroactive backfill to reinterpret past information in a new way, ensuring a patient’s history stays relevant as the outside world changes.

Layered Memory: Managing Information Overload

One of the biggest challenges faced in AI memory design is how to handle the sheer volume of data accumulated over time – we need to prevent the loss of key insights while also ensuring that the system is not overwhelmed by large amounts of junk that make it impossible to reason. The human brain does a good job of this by intelligently prioritizing and organizing key insights to remember while systematically decaying information that doesn’t matter.

Amigo’s functional memory system relies on a layered architecture to emulate this intelligent approach. Let’s break down each layer.

L0: Raw Transcripts – Retains every word ever said, providing the foundation for historical recontextualization during live patient interactions and acting as the ground truth for future references. Simple memory systems stop here, but suffer from the information density explosion problem described above.

L1: Extracted Memories – After each conversation, the agent engages in post-processing (like dreaming, when the brain organizes relevant information from the events of the day for long-term storage). This process extracts new memories from L0, determines what is novel vs. redundant information, and decides what’s worth keeping long-term.

L2: Episodic User Insights – Organizes L1 notes into meaningful insights that are structured based on the dimensions of the user model (L3). The process of converting L1 memories into L2 insights forms temporal checkpoints that capture changes in our understanding of the patient over time. This step acts as a layer that prevents information density overload when ingesting insights into the user model.

L3: Global User Model – This represents the highest-level understanding of the patient as a whole, organized by dimensions that capture the most important information for a specific type of clinician to know about them. We can maintain an up-to-date understanding of the patient across time, providing a clear and complete picture that the agent can draw from during a conversation – this creates 90-95% efficiency gains in active memory retrieval.

When more niche information is required but missing, the agent writes its own targeted query to dig deeper and fill in the gap. Rather than broadly searching memory for phrases like “leg pain” to respond to a user query, Amigo’s system contextualizes the question against the user model to ask the question in a smarter way and efficiently extract deeper insights. The ability to flag information gaps also allows the agent to ask follow-up questions in real time.

Moving from Blueprint to Bedside

Healthcare conditions are complex, requiring a synthesis of patient health history, family history, medications, symptoms, age, and multiple additional factors. With the global user model at its core, Amigo agents are equipped with true functional clinical intelligence, acting like skilled clinicians who always have the most relevant information at their fingertips.

Let’s recap what we’ve covered:

During live sessions, Amigo’s functional memory system keeps the user model (L3) active at all times. This allows agents to reason instantly with the complete patient context, removing the latency-accuracy tradeoff that affects other AI memory systems.

It also creates multiple interconnected feedback loops between the global understanding of the patient and the immediate conversation, interpreting every detail based on a provider’s identity and service line. In other words, a cardiology agent interprets chest pain through a cardiovascular risk assessment framework, while a psychiatry agent will consider anxiety manifestation and somatic symptoms.

The functional memory system then identifies new information and evaluates it in the context of the patient’s medical history, merging relevant updates into the user model without losing clarity or becoming overwhelmed.

Because Amigo agents keep the whole patient’s picture in mind constantly, interpret new information in real time, know what’s truly important, and prevent information overload, they are able to reason like trained medical professionals instead of simply reciting facts.

Deliver Context-Aware Clinical Intelligence

Like a doctor who remembers all the important details of a patient’s past medical history and can put them in context based on the latest information, Amigo’s functional memory system remembers what matters when it matters most. As a result, organizations can deliver personalized care across hundreds of thousands of patients.

Have more questions about the Amigo memory system? Book a call and our team will be happy to answer them in detail.