Automating Triage with AI Agents
Key Points
- The nurse in the ER demonstrates classic triage by quickly distinguishing a minor paper cut from a serious rock‑climbing injury, prioritizing resources for the most critical cases.
- “Triage” originated in early 19th‑century military medicine and now appears in many fields—from emergency services to insurance, cybersecurity, and customer support—where tasks are sorted by urgency and risk.
- Triage AI agents aim to replicate human triage by automating intake, intelligent prioritization, and routing through a coordinated multi‑agent system.
- Each Triage AI solution comprises at least three core components: an intake agent that gathers data via LLM‑driven conversations, an assessment agent that researches and diagnoses the problem, and a routing/decision agent that directs the case to the appropriate resource.
Sections
- Nurse Triage Illustrates AI Potential - A vivid ER vignette shows how a nurse prioritizes patients, prompting the idea of automating triage with AI agents.
- Three Core Triage AI Agents - The speaker outlines the intake, assessment, and routing agents that together collect data, diagnose needs, and execute or forward actions via various APIs.
- Triage AI vs Conversational Agents - The passage explains that while conversational agents can be components of a triage AI system, true triage agents differ in function, workflow, adaptability, and decision‑making, delivering rapid, consistent prioritization and routing—making them a promising area for developers to explore with open‑source multi‑agent frameworks as AI becomes embedded across digital workflows.
Full Transcript
# Automating Triage with AI Agents **Source:** [https://www.youtube.com/watch?v=x2ghX_RyHG4](https://www.youtube.com/watch?v=x2ghX_RyHG4) **Duration:** 00:07:20 ## Summary - The nurse in the ER demonstrates classic triage by quickly distinguishing a minor paper cut from a serious rock‑climbing injury, prioritizing resources for the most critical cases. - “Triage” originated in early 19th‑century military medicine and now appears in many fields—from emergency services to insurance, cybersecurity, and customer support—where tasks are sorted by urgency and risk. - Triage AI agents aim to replicate human triage by automating intake, intelligent prioritization, and routing through a coordinated multi‑agent system. - Each Triage AI solution comprises at least three core components: an intake agent that gathers data via LLM‑driven conversations, an assessment agent that researches and diagnoses the problem, and a routing/decision agent that directs the case to the appropriate resource. ## Sections - [00:00:00](https://www.youtube.com/watch?v=x2ghX_RyHG4&t=0s) **Nurse Triage Illustrates AI Potential** - A vivid ER vignette shows how a nurse prioritizes patients, prompting the idea of automating triage with AI agents. - [00:03:14](https://www.youtube.com/watch?v=x2ghX_RyHG4&t=194s) **Three Core Triage AI Agents** - The speaker outlines the intake, assessment, and routing agents that together collect data, diagnose needs, and execute or forward actions via various APIs. - [00:06:26](https://www.youtube.com/watch?v=x2ghX_RyHG4&t=386s) **Triage AI vs Conversational Agents** - The passage explains that while conversational agents can be components of a triage AI system, true triage agents differ in function, workflow, adaptability, and decision‑making, delivering rapid, consistent prioritization and routing—making them a promising area for developers to explore with open‑source multi‑agent frameworks as AI becomes embedded across digital workflows. ## Full Transcript
Imagine someone walking into a hospital ER.
And they're doubled over in pain, tears flowing from their eyes, a nurse notices and she springs into action...
and she immediately starts asking them questions to fill out an intake questionnaire.
A brief intake questionnaire.
And then afterwards she starts asking more detailed questions to assess what exactly is the problem.
You know, where does it hurt?
What happened?
When did it start?
So the person gathers themselves and, you know, lifting up their hand and then lifting up their pinky finger, it reveals a very horrible paper cut.
So confused and very annoyed, the nurse quickly responds by providing a bandage.
Bandaid and then of course, routing them back home, telling them, get out of my ER.
Why would you go to an ER with a paper cut?
And then she turns her attention and rushes off to help somebody else, a victim of a very terrible rock climbing accident.
In this case, rock beats paper.
So, this nurse is doing triage.
What she's doing is she's figuring out who needs help as a priority
based on a mix of observed symptoms, patient history, and domain-specific knowledge.
And she's actually helping patients as much as she can in order to avoid using up valuable physician time and hospital resources.
So the question is, what if we could automate this process?
And this brings us to the world of Triage AI Agents.
So the word triage is actually a French word.
It means the action of picking or sorting.
And the modern concepts of the word started around 1800 in military medicine,
where the surgeon in chief of Napoleon's Imperial Guard introduced the concept of sorting battlefield injuries
so that the most critical cases got care first regardless of rank or class.
But triage isn't just a hospital thing.
If you've ever called 911 or gone to an auto mechanic or worked with customer service reps, you've been triaged.
Insurance companies and cybersecurity systems triage cases into different priority levels according to risk classification.
So the applications are innumerable.
Triage AI agents imitate the way we humans do triage by automating this process of intake, intelligent prioritization,
and routing using two calling agents working together in a multi-agentic system.
And every triage AI agent has three essential components.
Corresponding to the process our nurse followed earlier, intake.
The first one is our intake agent.
Here, the LLM is connected to knowledge.
So we have our LLM.
And typically through our MCP, we're connecting to various knowledge sources.
And these knowledge sources could include things like client data, or it could include ticket data, depending on the use case.
And for sure, it should include things like questionnaire templates.
The intake agent's goal is to converse and collect data that can be passed on to the subsequent agents.
Next, you have, typically, your assessment agent.
Now the assessment agent's goal is to do research and problem diagnosis
in order to generate a detailed needs assessment along with a priority.
And this agent consists of an LLM and he is typically similar to more like a search agent, where he's connected to various...
domain-specific knowledge or search APIs.
Or even web search.
And then finally, we have here...
last but not least, our routing agent.
Now, the goal of the routing and or action agent consists of an LLM
corresponding to any number of APIs or services for completing or routing a request.
It could be communication services like email.
APIs, and it could be numerous,
email, SMS, or maybe it could be APIs for updating resources.
It will also typically include access to the full list of triage cases in order to set and update the relative priorities of all the cases,
including the one that it received from the assessment agent.
Notice here that Triage AI agents aren't just chatbots or even conversational AI agents with RAG.
While conversational agents can form part of the Triage AI agent system, that's about as far as the similarities go.
They differ in their primary function, the workflow they follow, their adaptability,
their decision-making, and the use cases that they're best suited for.
Triage AI agents can bring speed,
consistency and scalability to the human art of intelligent prioritization and routing.
So if you're a developer or data scientist, this is a space worth exploring,
starting with your favorite multi-agentic open source framework like Langflow or Langchain or Crew AI.
Because as more systems go AI native, triage isn't just something nurses will do, it's something we'll see
integrated across the fabric of our digital workflows.