US-20260127460-A1 - Domain Specific Agents for an AI Agentic Architecture

Abstract

A method of selecting and interacting with agent types for an agentic AI system includes deploying an agent based on a template, with the agent assigned to a functional domain required by an enterprise, wherein the agent is selected to be one of a Prescriptive Conversational Agent; a Prescriptive Action Agent; a Q&A Retrieval Agent for private knowledge bases; a Q&A Retrieval Agent for public knowledge bases; a Dynamic Multi-task Action Agent; and a Personal Assistant Agent. The selected agents can be connected to a reasoning engine and orchestration module able to send and receive requests to the agent.

Inventors

• Antonio Nucci

Assignees

• Aisera, Inc.

Dates

Publication Date

20260507

Application Date

20251031

Claims (20)

1. 1 . A method of selecting and interacting with agent types for an agentic AI system, comprising: deploying an agent based on a template, with the agent assigned to a functional domain required by an enterprise, wherein the agent is selected to be one of a Prescriptive Conversational Agent; a Prescriptive Action Agent; a Q&A Retrieval Agent for private knowledge bases; a Q&A Retrieval Agent for public knowledge bases; a Dynamic Multi-task Action Agent; a Personal Assistant Agent; and connecting the agent to a reasoning engine and orchestration module able to send and receive requests to the agent.
2. 2 . The method of selecting and interacting with agent types for an agentic AI system of claim 1 , wherein the agent further comprises an external agent.
3. 3 . The method of selecting and interacting with agent types for an agentic AI system of claim 1 , wherein the functional domain further comprises at least one of IT, HR, Finance, Engineering, and Sales and Marketing.
4. 4 . The method of selecting and interacting with agent types for an agentic AI system of claim 1 , wherein the agent has an associated name and avatar.
5. 5 . The method of selecting and interacting with agent types for an agentic AI system of claim 1 , wherein the agent has an associated persona.
6. 6 . The method of selecting and interacting with agent types for an agentic AI system of claim 1 , wherein the template associated with the agent has a set of inputs, integrations, external services, channels, and business logic.
7. 7 . The method of selecting and interacting with agent types for an agentic AI system of claim 1 , wherein the agent has an associated agent scope.
8. 8 . The method of selecting and interacting with agent types for an agentic AI system of claim 1 , wherein the agent has an associated agent output.
9. 9 . The method of selecting and interacting with agent types for an agentic AI system of claim 1 , wherein functional domains further comprise at least one of IT, HR, Finance, Engineering, and Sales and Marketing.
10. 10 . An agentic AI system architecture able to respond to a user request, comprising: multiple agents, each agent being assigned to a functional domain required by an enterprise wherein each of the multiple agents is selected to be one of a Prescriptive Conversational Agent; a Prescriptive Action Agent; a Q&A Retrieval Agent for private knowledge bases; a Q&A Retrieval Agent for public knowledge bases; a Dynamic Multi-task Action Agent; a Personal Assistant Agent; a reasoning engine supported by at least one large language model (LLM), with the reasoning engine being able to interpret a user request and decompose the user request into one or more mini-tasks; and an orchestrator module connected to the reasoning engine and able to route those mini-tasks based on the user request to at least one of the multiple agents for further processing; and wherein the orchestrator module and reasoning engine can collect and reconcile processed results from the multiple agents and prepare a response to the user request.
11. 11 . The agentic AI system architecture of claim 10 , wherein multiple LLMs are used to assist in domain specific interpretation of the user request.
12. 12 . The agentic AI system architecture of claim 10 , wherein the reasoning engine can engage the user for clarification in response to the user request.
13. 13 . The agentic AI system architecture of claim 10 , wherein the reasoning engine supports reinforcement learning.
14. 14 . The agentic AI system architecture of claim 10 , wherein the multiple agents further comprise at least some external agents.
15. 15 . The agentic AI system architecture of claim 10 , wherein functional domains further comprise at least one of IT, HR, Finance, Engineering, and Sales and Marketing.
16. 16 . The agentic AI system architecture of claim 10 , wherein the user request is submitted through an engagement channel to the agentic AI System.
17. 17 . A method of building agents for an agentic AI system, comprising the steps of: having a user select an agent template that incorporates a guided set of input, integrations, external services, channels, and business logic; customizing the agent template to serve specific user needs; deploying the agent based on a template, with the agent assigned to a functional domain required by an enterprise; and connecting the agent to a reasoning engine and orchestration module able to send and receive requests to the agent.
18. 18 . The method of building agents for an agentic AI system of claim 17 , wherein the agent is selected to be one of a Prescriptive Conversational Agent; a Prescriptive Action Agent; a Q&A Retrieval Agent for private knowledge bases; a Q&A Retrieval Agent for public knowledge bases; a Dynamic Multi-task Action Agent; and a Personal Assistant Agent.
19. 19 . The method of building agents for an agentic AI system of claim 17 , wherein the agent selected is a Prescriptive Conversational Agent; and wherein the agent template allows for interactions with multi-agent flow aggregation using multiple branches, each branch having a different agent specialized in providing prescriptive answers for specific topics of expertise.
20. 20 . The method of building agents for an agentic AI system of claim 17 , wherein the agent selected is a Prescriptive Action Agent; and wherein the agent template allows a user to select the action workflow from a list of available action workflows or enter the required information to define and test needed integration.

Description

RELATED APPLICATION The present disclosure is part of a non-provisional patent application claiming the priority benefit of U.S. Patent Application No. 63/715,091, filed on Nov. 1, 2024, which is hereby incorporated by reference in its entirety. TECHNICAL FIELD The present disclosure generally relates to use of systems and processes in an enterprise support system that allow for dynamically selecting and utilizing multiple software agents of differing capability. In some embodiments, the agents are associated with various functional domains to improve decision making. BACKGROUND Agent based software systems that support and act on behalf of an enterprise have been used to enhance operational efficiency, improve decision-making, and allow for personalized user interactions. Unfortunately, many such systems rely on fixed workflows and predefined rules and experiences and have proven inadequate in addressing the dynamic needs of modern enterprises. What is needed are development and deployment of autonomous agents capable of dynamic learning, decision-making, and interaction within complex environments. Such systems can optimize workflows, reduce operational costs, and respond more effectively to changing business needs. This adaptability can drive higher productivity but enables organizations to stay competitive in an increasingly dynamic marketplace. SUMMARY In some embodiments, a method of selecting and interacting with agent types for an agentic AI system includes deploying an agent based on a template, with the agent assigned to a functional domain required by an enterprise, wherein the agent is selected to be one of a Prescriptive Conversational Agent; a Prescriptive Action Agent; a Q&A Retrieval Agent for private knowledge bases; a Q&A Retrieval Agent for public knowledge bases; a Dynamic Multi-task Action Agent; and a Personal Assistant Agent. The selected agents can be connected to a reasoning engine and orchestration module able to send and receive requests to the agent. In one embodiment, a method of operating an agentic AI system able to respond to a user request includes the steps selecting a set of multiple agents, each agent being assigned to a functional domain required by an enterprise, wherein the agent is selected to be one of a Prescriptive Conversational Agent; a Prescriptive Action Agent; a Q&A Retrieval Agent for private knowledge bases; a Q&A Retrieval Agent for public knowledge bases; a Dynamic Multi-task Action Agent; and a Personal Assistant Agent. A reasoning engine supported by at least one large language model (LLM) is provided, with the reasoning engine acting to interpret a user request and decompose the user request into one or more mini-tasks. An orchestrator module connected to the reasoning engine provides for routing of mini-tasks based on the user request to at least one of the multiple agents for further processing. The orchestrator module and reasoning engine can collect and reconcile processed results from the multiple agents and prepare a response to the user request. In some embodiments, the agents can be dynamically added or removed from the set of multiple agents. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 depicts an agentic AI system architecture able to respond to user requests in accordance with an embodiment. FIG. 2 depicts a method of operating an agentic AI system in accordance with an embodiment. FIG. 3 depicts the associate flow template for select branches of a Prescriptive Conversational Agent. FIG. 4 depicts an example of a prescriptive answer entered by the user for a Mental Wellness agent. FIG. 5 depicts a flow template for a Prescriptive Action Agent. FIG. 6 depicts a computer system capable of supporting or acting as a component of the agentic AI system in accordance with an embodiment. In the Figures, reference signs can be omitted as is consistent with accepted engineering practice; however, a skilled person will understand that the illustrated components are understood in the context of the Figures as a whole, of the accompanying writings about such Figures, and of the embodiments of the claimed inventions. DETAILED DESCRIPTION OF THE DRAWINGS FIG. 1 depicts an agentic AI system architecture 100 able to respond to user requests made through a communication omnichannel 102 in accordance with an embodiment for the purposes of the present technology. The system architecture 100 includes a set of multiple agents 104, each agent being assigned to various specific functional domains 106 required by an enterprise. In some embodiments, agents can be dynamically added or removed from the set of multiple agents. In some embodiments, the agents 104 can be external agents that can operate on distinct and separate enterprises with other system architectures and not be directly controlled by agentic AI system architecture 100. Interaction with agents 104 is provided by an AI system 110 that includes a reasoning engine 112 supported by at least one large language m