CN-121996316-A - Agent reasoning platform capable of being rapidly deployed

Abstract

Systems, methods, or techniques are provided for reasoning and response generation across a composite data grid. In various embodiments, a system may include a memory storing computer-executable components and a processor executing the computer-executable components stored in the memory, wherein the computer-executable components include a plurality of inference components, wherein a first inference component of the plurality of inference components is operatively coupled to other inference components of the plurality of inference components, and the first inference component is configured to select one or more of the other inference components to facilitate responding to a prompt.

Inventors

• L. J. Nintmann III
• J. D. Bonevich

Assignees

• 费舍尔科学有限责任公司

Dates

Publication Date

20260508

Application Date

20251107

Priority Date

20251024

Claims (20)

1. 1. A system for response generation across a network of distributed computing nodes, the system comprising: A memory storing computer-executable components, and A processor executing the computer-executable components stored in the memory, wherein the computer-executable components comprise: a plurality of inference components, wherein a first inference component of the plurality of inference components is operatively coupled to other inference components of the plurality of inference components, and the first inference component is configured to select one or more of the other inference components to invoke in response to a prompt.
2. 2. The system of claim 1, wherein the first inference component is further configured to select one or more of a plurality of artificial intelligence agents to invoke in response to the prompt.
3. 3. The system of claim 2, wherein the first inference component is configured to invoke in response to the prompt by selecting one or more other inference components of the plurality of inference components by calculating a relevance metric of a capability list of the plurality of inference components to the prompt.
4. 4. The system of claim 1, wherein one or more of the plurality of inference components is configured to select one or more additional inference components of the plurality of inference components based on one or more additional hints.
5. 5. The system of claim 3, wherein the first inference component is further configured to generate a response to the prompt based on data provided by the selected one or more artificial intelligence agents and the selected one or more other inference components.
6. 6. The system of claim 5, wherein the first inference component is configured to generate the response to the prompt by: generating one or more data structures from the data provided by the selected one or more artificial intelligence agents and the selected one or more other inference components; Generating one or more outputs based on the hint and the one or more data structures, and The response is synthesized from conclusions of two or more resistant artificial intelligence agents, wherein the two or more resistant artificial intelligence agents generate the conclusion based on the one or more outputs.
7. 7. The system of claim 6, wherein the first inference component is further configured to generate the response to the hint by dimensionally reducing the one or more data structures using singular value decomposition.
8. 8. The system of claim 5, wherein a second inference component of the selected one or more other inference components is configured to, in response to being selected: checking at least one of role-based control permissions or attribute-based access control permissions of the first inference component; Approving data from one or more data sources managed by the second inference component based on the at least one of the role-based control permissions or attribute-based access control permissions of the first inference component; Aggregating relevant data of the approved data based on the prompt, and The aggregated relevant data is transmitted to the first inference component.
9. 9. A computer-implemented method for response generation across a network of distributed computing nodes, the computer-implemented method comprising: selecting, by a device operatively coupled to the processor, one or more of the plurality of operatively coupled inference components based on the hint; Selecting, by the appliance, one or more of the plurality of artificial intelligence agents based on the hint, and A response to the prompt is generated by the device based on the data generated by the selected one or more inference components and the data generated by the selected one or more artificial intelligence agents.
10. 10. The computer-implemented method of claim 9, wherein the one or more selected inference components further select one or more additional inference components of the plurality of operatively coupled inference components.
11. 11. The computer-implemented method of claim 9, wherein selecting the one or more of the plurality of artificial intelligence agents comprises calculating, by the device, a relevance metric of a capability list of the plurality of artificial intelligence agents to the hint.
12. 12. The computer-implemented method of claim 9, wherein selecting the one or more inference components comprises computing, by the device, a relevance metric of a list of data sources managed by the plurality of inference components to the hint.
13. 13. The computer-implemented method of claim 10, wherein generating the response to the prompt comprises: generating, by the device, data from one or more data sources managed by the selected one or more inference components using the selected one or more inference components; Populating, by the appliance, one or more knowledge graphs with data generated by the selected one or more artificial intelligence agents and data generated by the selected one or more inference components; generating, by the device, one or more data transformations based on the hints and the one or more knowledge graphs using the one or more analysis modules of the selected one or more inference components, and The response is synthesized by the device from disputes of a plurality of resistant artificial intelligence agents using natural language processing, wherein the plurality of resistant artificial intelligence agents generate the dispute based on the one or more data transformations.
14. 14. A computer program product for response generation across a network of distributed computing nodes, the computer program product comprising a non-transitory computer readable memory having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to: generating, by the processor, one or more subtasks based on the received hints using a first inference component of a plurality of operatively coupled inference components; Selecting, by the processor, one or more additional ones of the plurality of inference components to perform the one or more subtasks using the first inference component; executing, by the processor, the one or more subtasks using the one or more additional reasoning components; Transmitting, by the processor, results of the one or more subtasks to the first inference component using the one or more additional inference components, and A response to the received prompt is generated by the processor based on the results of the one or more subtasks using the first inference component.
15. 15. The computer program product of claim 14, wherein transmitting the results of the one or more subtasks comprises a publish/subscribe communication protocol between the first reasoning component and the selected one or more additional reasoning components.
16. 16. The computer program product of claim 14, wherein the program instructions are further executable to cause the processor to perform a subtask of the one or more subtasks in response to an inference component of the one or more additional inference components being selected: Determining, by the processor, whether the subtask complies with a security policy of the inference component using the inference component, and The subtasks are performed by the processor using the inference component in response to determining that the subtasks conform to the one or more security policies of the inference component.
17. 17. The computer program product of claim 14, wherein the program instructions are further executable to cause the processor to: Determining, by the processor, whether the one or more subtasks conform to a security policy of the first inference component using the first inference component, and In response to determining that the one or more subtasks are in compliance with the security policy, selecting, by the processor, the one or more additional ones of the plurality of inference components using the first inference component to perform the one or more subtasks.
18. 18. The computer program product of claim 14, wherein the program instructions are further executable to cause the processor to: receiving, by the processor, the received hint using the first inference component; Determining, by the processor, whether the received hint meets a security policy of the first inference component using the first inference component, and In response to determining that the received hint meets the security policy of the first inference component, the one or more subtasks are generated by the processor based on the received hint using the first inference component.
19. 19. The computer program product of claim 14, wherein processing instructions are further executable by the processor to cause the processor to: Determining, by the processor, whether the response to the prompt meets a security policy of the first inference component using the first inference component, and Responsive to determining that the response to the prompt meets the security policy of the first inference component, the response to the received prompt is displayed by the processor on a graphical user interface.
20. 20. The computer program product of claim 14, wherein the selection of the one or more additional reasoning components to perform the subtask causes the processor to: determining, by the processor, a database including data related to sub-tasks of the one or more sub-tasks using the first inference component; Identifying, by the processor, an inference component of the plurality of inference components that manages the database using the first inference component, and The subtasks are transmitted by the processor to the identified inference components using the first inference component.

Description

Agent reasoning platform capable of being rapidly deployed Cross Reference to Related Applications The present application claims the priority and benefits of U.S. provisional application No. 63/718,094, entitled "RAPIDLY DEPLOYABLE AGENTIC REASONING PLATFORM (quick deployable agent inference platform)" filed on 8, 11, 2024. The foregoing application is hereby incorporated by reference in its entirety. Background Many industries and enterprises are rapidly introducing Artificial Intelligence (AI) into their workflows and processes. However, integrating various data sources and providing enterprise-wide access to AI processes may lead to significant expansion problems. Disclosure of Invention The following presents a simplified summary in order to provide a basic understanding of one or more embodiments. This summary is not intended to identify key or critical elements or to delineate any scope of the particular embodiments or any scope of the claims. Its sole purpose is to present the concepts in a simplified form as a prelude to the more detailed description that is presented later. In one or more embodiments described herein, an apparatus, system, method, or device is provided that facilitates inference and response generation across a composite data grid. In accordance with one or more embodiments, a system is provided. The system may include a memory storing computer-executable components and a processor executing the computer-executable components stored in the memory, wherein the computer-executable components include a plurality of inference components, wherein a first inference component of the plurality of inference components is operatively coupled to other inference components of the plurality of inference components, and the first inference component is configured to select one or more of the other inference components to facilitate responding to a prompt. An advantage of the system and/or corresponding method may be a quick and easy expansion of the system by using new reasoning components and adding them to multiple reasoning components. Drawings The embodiments will be readily understood by the following detailed description in conjunction with the accompanying drawings. For ease of description, like reference numerals designate like structural elements. Embodiments are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings. FIG. 1 is a block diagram of an example scientific instrument module for performing reasoning and response generation across a composite data grid, according to one or more embodiments described herein. FIG. 2 is a flowchart of an example method of performing reasoning and response generation across a composite data grid in accordance with one or more embodiments described herein. FIG. 3 illustrates a block diagram of an example system that can facilitate reasoning and response generation across a composite data grid in accordance with one or more embodiments described herein. Fig. 4 illustrates a block diagram of an example inference component in accordance with one or more embodiments described herein. Fig. 5 illustrates a block diagram of an example inference engine in accordance with one or more embodiments described herein. FIG. 6 illustrates a block diagram of an example knowledge engine, in accordance with one or more embodiments described herein. FIG. 7 illustrates a block diagram of an example analysis module in accordance with one or more embodiments described herein. Fig. 8 illustrates a flow diagram of an example method that can facilitate inference and response generation across multiple inference components in accordance with one or more embodiments described herein. Fig. 9 illustrates a flow diagram of an example method that can facilitate response generation across multiple inference components in accordance with one or more embodiments described herein. Fig. 10 illustrates a flow diagram of an example method that can facilitate information transfer across multiple inference components in accordance with one or more embodiments described herein. FIG. 11 illustrates a block diagram of an example operating environment in which one or more embodiments described herein can be implemented. Detailed Description The following detailed description is merely exemplary in nature and is not intended to limit the embodiments and/or the application or uses of the embodiments. One or more embodiments are now described with reference to the drawings. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a more thorough understanding of one or more embodiments. It may be evident, however, that one or more embodiments may be practiced without these specific details. AI is increasingly being applied across large networks and to large existing data sources in various fields and enterprises. Typically, these AI applications are centrally hosted, with the user rem