Table of Contents

Triage AI Agents – Automating Intelligent Prioritization

Triage AI Agents: Automating Intelligent Prioritization and Routing

Triage AI Agents – Automating Intelligent Prioritization

I. Overview

This briefing details the concept of Triage AI Agents, a sophisticated application of AI designed to automate the process of

"triage"

– the act of picking or sorting to prioritize needs. Drawing parallels from human-centric triage in fields like medicine, customer service, and cybersecurity, Triage AI Agents aim to bring speed, consistency, and scalability to intelligent prioritization and routing. Unlike simple chatbots, these multi-agentic systems integrate three core components (Intake, Assessment, and Routing Agents) to collect data, diagnose problems, and execute or escalate actions, leveraging Large Language Models (LLMs) connected to various knowledge sources and APIs.

II. What is Triage?

Triage is defined as

"the action of picking or sorting."

Its modern concept emerged around 1800 in military medicine, introduced by Napoleon's Imperial Guard's surgeon-in-chief, who prioritized care for the

"most critical cases...regardless of rank or class."

The core purpose of triage is to

"figure out who needs help as a priority based on a mix of observed symptoms, patient history, and domain-specific knowledge,"

while also

"helping patients as much as she can in order to avoid using up valuable physician time and hospital resources."

Examples of human-driven triage include:

Hospitals/ERs: Prioritizing patients based on the severity of their condition (e.g., a
"very horrible paper cut"
versus a
"very terrible rock climbing accident"
).
911 Services: Classifying emergency calls by urgency.
Auto Mechanics: Diagnosing vehicle issues and prioritizing repairs.
Customer Service: Sorting customer inquiries by urgency or complexity.
Insurance Companies & Cybersecurity Systems: Classifying cases into
"different priority levels according to risk classification."

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III. The Concept of Triage AI Agents

Triage AI Agents are designed to

"imitate the way we humans do triage by automating this process of intake, intelligent prioritization, and routing."

They are built as

"two calling agents working together in a multi-agentic system."

Key Differentiators from Chatbots: It is crucial to understand that

"Triage AI agents aren't just chatbots or even conversational AI agents with RAG."

While conversational agents can be part of the system,

"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."

Benefits of Triage AI Agents: They

"can bring speed, consistency and scalability to the human art of intelligent prioritization and routing."

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IV. Essential Components of a Triage AI Agent System

Every Triage AI Agent system comprises three main components, mirroring the human triage process:

Intake Agent:

Goal:
"To converse and collect data that can be passed on to the subsequent agents."
Technology: An LLM
"connected to knowledge"
via an
"MCP"
(presumably a Master Control Program or similar connector).
Knowledge Sources: These can include
"client data," "ticket data,"
and
"questionnaire templates."

Assessment Agent:

Goal:
"To do research and problem diagnosis in order to generate a detailed needs assessment along with a priority."
Technology: An LLM,
"typically similar to more like a search agent,"
connected to
"domain-specific knowledge or search APIs"
or
"web search."

Routing and/or Action Agent:

Goal: To complete or route a request based on the assessment.
Technology: An LLM connected to
"any number of APIs or services."
Functions:
- Communication Services:
  "email, SMS."
- Resource Updates:
  "APIs for updating resources."
- Priority Management: Access to
  "the full list of triage cases in order to set and update the relative priorities of all the cases."

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V. Implications and Future Outlook

Triage AI Agents represent a significant advancement in AI applications, moving beyond simple conversational interfaces to intelligent workflow automation. They are anticipated to be

"integrated across the fabric of our digital workflows"

"more systems go AI native."

Developers and data scientists are encouraged to explore this space, leveraging

"multi-agentic open source framework like Langflow or Langchain or Crew AI."

Triage AI Agents: FAQ

Triage AI Agents: Automating Intelligent Prioritization and Routing - FAQ

What is triage and how does it relate to AI agents?

Triage is the process of sorting and prioritizing cases based on urgency and need, aiming to allocate resources effectively. It originated in military medicine to prioritize battlefield injuries and is now common in various fields like healthcare, customer service, and cybersecurity. Triage AI agents automate this human process of intelligent prioritization and routing, bringing speed, consistency, and scalability to decision-making.

What are the core components of a Triage AI Agent system?

A Triage AI Agent system typically consists of three essential components, each corresponding to a step in the triage process:

Intake Agent: This agent, powered by an LLM, converses with users to collect initial data. It connects to various knowledge sources like client data, ticket information, and questionnaire templates to gather relevant information.
Assessment Agent: Also powered by an LLM, this agent's goal is to research and diagnose the problem. It connects to domain-specific knowledge, search APIs, or web search to generate a detailed needs assessment and assign a priority level.
Routing Agent: This agent, an LLM as well, is responsible for completing or routing the request. It connects to various APIs or services (e.g., email, SMS, resource updating) and has access to the full list of triage cases to set and update their relative priorities.

How do Triage AI Agents differ from typical chatbots or conversational AI agents with RAG?

While conversational agents can be part of a Triage AI Agent system, they are not the same. Triage AI Agents have a distinct primary function, follow a specific workflow, exhibit greater adaptability, make more complex decisions, and are suited for different use cases. Their core purpose goes beyond simple conversation; it's about intelligent prioritization and routing, often involving multiple interacting agents.

What problem do Triage AI Agents aim to solve?

Triage AI Agents aim to automate the process of intake, intelligent prioritization, and routing. This automation helps avoid using up valuable human time and resources on less critical issues, allowing human experts to focus on the most severe or complex cases. By streamlining this process, Triage AI Agents can bring significant improvements in efficiency and resource allocation.

Can you provide examples of where triage is currently used?

Triage is widely applied in various sectors. Beyond the well-known hospital emergency rooms (ERs), it's used when you call 911, visit an auto mechanic, or interact with customer service representatives. Insurance companies use triage to classify cases by risk, and cybersecurity systems prioritize incidents based on threat levels.

What is the role of a Large Language Model (LLM) within a Triage AI Agent system?

LLMs are central to each of the three core components of a Triage AI Agent system. They power the Intake Agent for conversation and data collection, the Assessment Agent for research and problem diagnosis, and the Routing Agent for completing or directing requests. Their ability to process and generate human-like text is crucial for understanding user input, accessing knowledge, and making informed decisions.

What benefits do Triage AI Agents offer?

Triage AI Agents bring several key benefits to organizations. They enhance speed in processing and prioritizing requests, ensure consistency in decision-making by following predefined logic and accessing comprehensive knowledge, and provide scalability, allowing for efficient handling of a large volume of cases without a proportional increase in human effort.

For whom is the development of Triage AI Agents particularly relevant?

The development of Triage AI Agents is particularly relevant for developers and data scientists. The growing trend of systems becoming "AI native" suggests that triage will be increasingly integrated into digital workflows. Exploring multi-agentic open-source frameworks like Langflow, Langchain, or Crew AI is recommended for those interested in this emerging field.

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