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US-20260127200-A1 - AGENTIC TOOL MESH PLATFORM

US20260127200A1US 20260127200 A1US20260127200 A1US 20260127200A1US-20260127200-A1

Abstract

Systems and methods are provided for creation of project meshes by defining agentic tool instances that can be activated to access agentic tools. Examples include providing an interface for defining a mesh of agentic tools and receiving, via the interface, queries descriptive of a project. The queries can be provided to a reasoning engine that generates first parameters defining tasks and second parameters defining agentic tool instances. The agentic tool instances can be created in accordance with the first parameters and the second parameters and activated for accessing agentic tools located in a data store. The agentic tools can correspond to a parameter of the first parameters associated an agentic tool instance. Examples can configure the mesh comprising the agentic tool instances, and the mesh can be executed to cause the agentic tool instances to access and run one or more of the agentic tools.

Inventors

  • ANTONIO FIN
  • GLYN BOWDEN
  • Carlo Maria Eugenio Vaiti
  • Ignacio Aldama Perez

Assignees

  • HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Dates

Publication Date
20260507
Application Date
20241106

Claims (20)

  1. 1 . A method comprising: providing an interface for defining a mesh of agentic tools; receiving, via the interface, one or more queries descriptive of a project; providing the one or more queries to a large language model (LLM) that generates a plurality of first parameters defining tasks of the project and a plurality of second parameters defining a set of agentic tool instances for the tasks; creating the set of agentic tool instances in accordance with the plurality of first parameters and the plurality of second parameters; activating the set of agentic tool instances for accessing a plurality of agentic tools located in an agentic tool data store, wherein the plurality of agentic tools corresponds to a first parameter of the plurality of first parameters associated an agentic tool instance of the set of agentic tool instances; and configuring the mesh comprising the set of agentic tool instances, wherein executing the mesh causes the set of agentic tool instances to access and run one or more of the plurality of agentic tools.
  2. 2 . The method of claim 1 , wherein the plurality of agentic tools comprise domain-specific agentic tools.
  3. 3 . The method of claim 1 , wherein the plurality of agentic tools comprises at least a domain-specific LLM.
  4. 4 . The method of claim 1 , wherein the one or more queries are provided to the interface as a character string in human understandable natural language.
  5. 5 . The method of claim 1 , further comprising: populating a configuration file with a parameter responsive to a prompt corresponding to each of the one or more queries into one of a plurality of fields.
  6. 6 . The method of claim 1 , wherein the LLM comprises natural language processing.
  7. 7 . The method of claim 6 , wherein the LLM generates the plurality of first parameters and the plurality of second parameters by parsing the one or more queries using the natural language processing and populating a configuration file with the plurality of first parameters and the plurality of second parameters.
  8. 8 . A system, comprising: a memory storing instructions; and a processor communicably connected to the memory and configured to execute the instructions to: provide an interface for defining a mesh of agentic tools; receive, via the interface, one or more queries descriptive of a project; provide the one or more queries to a large language model (LLM) that generates a plurality of first parameters defining tasks of the project and a plurality of second parameters defining a set of agentic tool instances for the tasks; create the set of agentic tool instances in accordance with the plurality of first parameters and the plurality of second parameters; activate the set of agentic tool instances for accessing a plurality of agentic tools located in an agentic tool data store, wherein the plurality of agentic tools corresponds to a first parameter of the plurality of first parameters associated an agentic tool instance of the set of agentic tool instances; and configure the mesh comprising the set of agentic tool instances, wherein executing the mesh causes the set of agentic tool instances to access and run one or more of the plurality of agentic tools.
  9. 9 . The system of claim 7 , wherein the plurality of agentic tools comprise domain-specific agentic tools.
  10. 10 . The system of claim 7 , wherein the plurality of agentic tools comprises at least a domain-specific LLM.
  11. 11 . The system of claim 7 , wherein the one or more queries are provided to the interface as a character string in human understandable natural language.
  12. 12 . The system of claim 7 , wherein the processor is further configured to execute the instructions to: populate a configuration file with a prompt corresponding to each of the one or more queries into one of a plurality of fields.
  13. 13 . The system of claim 7 , wherein the LLM comprises natural language processing.
  14. 14 . The system of claim 13 , wherein the LLM generates the plurality of first parameters and the plurality of second parameters by parsing the one or more queries using the natural language processing and populating a configuration file with the plurality of first parameters and the plurality of second parameters.
  15. 15 . A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to: execute a large language model (LLM) to one or more queries received via a chatbot interface; transform, using the LLM, the one or more queries into a plurality parameters; construct a set of agentic tool instances based on the plurality of parameters, wherein the agentic tool instances of the set of agentic tool instances are assigned to tasks defined according to the plurality of parameters; allocate a plurality of agentic tools to the set of agentic tool instances, wherein the plurality of agentic tools are located in an agentic tool market place based on the plurality of parameters; and build a project mesh by configuring a mesh of the plurality of agentic tools, wherein the mesh comprises the set of agentic tool instances, wherein executing the project mesh causes the set of agentic tool instances to access and run a subset of the plurality of agentic tools.
  16. 16 . The non-transitory computer-readable storage medium of claim 15 , the plurality of parameters comprises sets of task parameters and sets of agentic tool instance parameters, wherein the non-transitory computer-readable storage medium stores further instructions that, when executed by a processor, cause the processor to: generate the tasks in accordance with the set of task parameters, wherein the set of agentic tool instances are constructed based on the set of agentic tool instance parameters; and assign the set of agentic tool instances to the tasks based on correspondence between the sets of task parameters and the sets of agentic tool instance parameters.
  17. 17 . The non-transitory computer-readable storage medium of claim 15 , wherein the plurality of agentic tools comprise domain-specific agentic tools.
  18. 18 . The non-transitory computer-readable storage medium of claim 15 , wherein the plurality of agentic tools comprises at least a domain-specific LLM.
  19. 19 . The non-transitory computer-readable storage medium of claim 15 , wherein the one or more queries are provided to the chatbot interface as a character string in human understandable natural language.
  20. 20 . The non-transitory computer-readable storage medium of claim 19 , wherein the LLM generates the plurality of parameters by parsing the one or more queries using natural language processing and populating a configuration file with the plurality parameters.

Description

BACKGROUND Generative artificial intelligence (AI) is AI capable of generating text, images, videos, or other data using generative models, in some cases based on prompts. Generative AI models can learn patterns and structure of input training data and use the learned patterns and structure to generate new data that has similar characteristics. Improvements in transformer-based deep neural networks, such as large language models (LLMs), have enabled the advancement of generative AI systems, such as chatbots (e.g., ChatGPT, Copilot, Gemini, LLaMA, and the like), text-to-image generation systems (e.g., Stable Diffusion, Midjourney, DALL-E, and the like), and text-to-video generation systems (e.g., Sora and the like). LLMs are computational models capable of language generation or other natural language processing tasks. As language models, LLMs acquire these abilities by learning statistical relationships from vast amounts of input data, such as text, during training. LLMs can provide predictive power regarding syntax, semantics, and ontologies contained in human language. BRIEF DESCRIPTION OF THE DRAWINGS The present disclosure, in accordance with one or more various examples, is described in detail with reference to the following figures. The figures are provided for purposes of illustration only and merely depict typical, non-limiting aspects of such examples. FIG. 1 is a schematic diagram of an example architecture of a project mesh creation system, in accordance with examples of the present disclosure. FIG. 2 illustrates a schematic diagram of a project mesh task that can be constructed by the system of FIG. 1, in accordance with an example disclosed herein. FIG. 3 depicts an illustrative example of project mesh that can be created by the system of FIG. 1, in accordance with an example disclosed herein. FIG. 4 illustrates an example process for creating a project mesh, in accordance with the examples disclosed herein. FIG. 5 is an example computing component that may be used to implement various features of creating a project mesh in accordance with the implementations disclosed herein. FIG. 6 is an example computing component that may be used to implement various features of the system of FIG. 1 in accordance with the implementations disclosed herein. FIG. 7 is a computing component that may be used to implement examples of the disclosed technology. The figures are not exhaustive and do not limit the present disclosure to the precise form disclosed. DETAILED DESCRIPTION Examples of the presently disclosed technology provide a platform (referred to herein as an agentic tool mesh platform) that supports creation of project meshes by defining a set of agentic tool instances, each of which can be activated (e.g., executed) to access an agentic tool. Examples herein can provide an interface for defining a project mesh as a mesh of agentic tools. The interface can receive queries descriptive of a target project and these queries can be used by the examples disclosed herein to create a project mesh for the project. In examples, the queries can be processed to generate parameters defining tasks of the project. Examples herein can discover agentic tools that can be utilized to perform the various tasks and create a set of agentic tool instances in accordance with the generated parameters. The agentic tools, in some examples, may themselves may be meshes designed for executing designed tasks. Examples herein can construct the project mesh by configuring the set of agentic tool instances to form a project mesh of the agentic tools assigned thereto. The project mesh can then be deployed as a standalone product that exposes its information and location for usage via an interface, such as but not limited to application programming interfaces (APIs) and/or user interfaces (UIs). Conventional, monolithic AI solutions may be inflexible and fail to address unique needs of different domains. This lack of adaptability can lead to inefficiencies that may hamper an ability to leverage AI's full potential. Monolithic AI solutions, such as larger domain agnostic LLMs (e.g., the GPT series of models, Gemini, the LLaMA family of models, Granite models, Claude models, and Mistral AI's models, to name a few examples) may rely on a corpus of training data from a multitude of sources across various different domains to represent the corpora of human language. Due to the reliance on training data from numerous domains, domain agnostic LLMs may inhere inaccuracies and biases present in the data from which they are trained. Furthermore, what is true to one domain may not be true to another domain, but monolithic AI solutions may be incapable of distinguishing between different domains and may adaptable to pivot between domains. Accordingly, examples herein leverage domain-specific agentic tools, each of which can be trained for a specific, domain centric task. Domain-specific agentic tools can provide for decentralized ownership of age