An AI agent ecosystem and a method for constructing the same
By building an AI intelligent agent ecosystem, the problems of adaptability and high cost of enterprise-level AI agents in enterprise scenarios have been solved. It has enabled the standardized decomposition and personalized response of business needs, reduced the cost of enterprise AI applications, and improved the platform's flexibility and scalability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Filing Date
- 2026-05-25
- Publication Date
- 2026-07-14
AI Technical Summary
Existing AI Agent technologies in enterprise scenarios suffer from several problems: inability to adapt to large-scale and hierarchical business needs, insufficient security, high usage costs, lack of effective monitoring, difficulty for enterprises to customize AI tools, and lack of channels to publish tasks.
The system constructs an AI intelligent agent ecosystem, including a central platform, an intelligent agent market module, a bounty module, and a data monitoring and analysis module. It generates office spaces by standardizing the breakdown of business needs, matches them with AI intelligent agents, provides trial interfaces, and offers bounties to the developer community when no match is found, thereby achieving full-dimensional data monitoring and analysis.
It enables the standardization and visual breakdown of enterprise business needs, reduces the cost of enterprise AI applications, improves the platform's flexibility and scalability, provides full-dimensional data monitoring and personalized response capabilities, and lowers the operational threshold.
Smart Images
Figure CN122390426A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of artificial intelligence, specifically to an AI intelligent agent ecosystem and its construction method. Background Technology
[0002] In recent years, AI Agent technology has seen breakthrough development. The emergence of the phenomenal product OpenClaw has become an important turning point for the industry, making the market and the public realize that the core value of AI Agent is no longer limited to natural language dialogue, but can actually undertake and complete various specific tasks, promoting the upgrade of AI Agent from a consumer-grade dialogue tool to a practical task processing tool.
[0003] Driven by this trend, major technology companies are referencing the OpenClaw technical architecture to develop product systems that provide end-users with customized AI assistants, and the commercial application of AI agents is entering a rapid exploration phase. However, current products and industry application models based on the OpenClaw architecture still have many significant shortcomings: 1. From a perspective perspective, existing products are still designed with individual end users at the core, failing to achieve enterprise-level collaboration centered on needs, and are unable to adapt to the large-scale and hierarchical business needs of enterprises.
[0004] 2. At the product level, OpenClaw's high openness and lack of systematic design in the early stages have led to significant security risks. Even though leading companies have solved the usability issues through encapsulation and optimization, users still need to have certain professional technical skills to achieve efficient tuning of OpenClaw, which is a high barrier to entry. At the same time, its token consumption is large and there is a lack of effective token consumption monitoring mechanisms, making it difficult to control usage costs.
[0005] From a business perspective, the current application of AI Agent technology in enterprise scenarios still faces a series of core issues that urgently need to be addressed: 1. Enterprises struggle to clearly define the application boundaries of AI and cannot accurately determine which business problems are suitable for AI to handle; 2. The actual cost and business benefits of AI-assisted work lack effective management and monitoring methods, making it difficult to quantify the return on investment; 3. Business needs are often unique and highly customized, making it difficult to find suitable AI tools and lacking channels to publish tasks and find professional solution providers; 4. Enterprises define their business differently at different stages of development, requiring sufficient compatibility in terms of UI scalability. However, there are currently no enterprise-level products on the market that can effectively solve this problem while also allowing for scalability. Summary of the Invention
[0006] The technical problem to be solved by this invention is to overcome the above-mentioned technical defects and provide an AI intelligent body ecosystem and its construction method that takes into account both extended applications and the creation of enterprise-level virtual offices.
[0007] To solve the above-mentioned technical problems, the technical solution provided by the present invention is: an AI intelligent agent ecosystem, including a central platform, an intelligent agent market module, a bounty module, a developer module, and a data monitoring and analysis module; The central platform includes constructing core interactive scenarios, standardizing and visually decomposing user business needs, and generating business tasks; The AI agent market module matches AI agents with corresponding capabilities according to business tasks, and provides capability description information and trial interfaces for the corresponding AI agents. When the agent market module fails to match an AI agent that meets the requirements of the preset business task, the bounty module will release the business task to the developer community in the form of a bounty. The developer module allows developers to build new AI agents to the agent marketplace module; The data monitoring and analysis module performs full-dimensional data collection, monitoring, and analysis for each task.
[0008] Preferably, the central platform includes multiple independent workstations generated based on business needs, with each workstation corresponding to a business task; The AI agent performs business tasks at its workstation in either a fully managed mode or a co-pilot collaborative mode.
[0009] Preferably, the workstation is also equipped with a unique identifier and associated with task objectives, input / output specifications, execution constraints, and expected value indicators; The central platform uses the MRG standardized interaction format for information interaction between office workstations and AI agents, as well as between AI agents themselves.
[0010] Preferably, the AI agents in the intelligent agent market module are modularly packaged based on an EMS architecture, and each AI agent includes: Ego Context components: Store the basic understanding of intelligent agents' roles, core data dependencies, and essential model components; Mission Context component: Based on the MRG standardized format, it defines the task boundaries, execution constraints, and output standards of the agent; Skill components include multiple sets of standardized business operation tools that can be called independently and have a unified input / output interface.
[0011] Preferably, each office space generated by the central platform is defined in the MRG format, which is completely consistent with the Mission Context component. The AI agent is bound to the corresponding business requirements through the format alignment and semantic matching of the two.
[0012] Preferably, the bounty module includes setting bounty amounts, delivery deadlines, and acceptance criteria for unfulfilled business tasks, and pushing targeted invitations or public recruitment information to registered developers.
[0013] Preferably, the developer module also includes standardized development templates and a test sandbox; After developers build, debug, and verify the functionality and compatibility of AI agents locally or in the cloud, they submit them to the agent marketplace module for review and listing.
[0014] Preferably, the data monitoring and analysis module tracks, assesses credibility, and quantifies benefits of the task execution process at each workstation in real time based on data flow, trust chain, and value anchor.
[0015] Another aspect of this invention discloses a method for constructing an AI intelligent agent ecosystem, comprising the following steps: S1: The central platform constructs the core interactive scenarios of the virtual office, standardizes and visualizes the enterprise's business needs, and generates independent workstations for corresponding business tasks; S2: The central platform sends the business tasks of the office space to the intelligent agent market module in a standardized format. The intelligent agent market module matches AI agents according to the task capability requirements and provides AI agent information and trial interface. S3: If the intelligent agent market module does not match an AI intelligent agent that meets the task requirements, the bounty module will publish the business task to the developer community in the form of a bounty to recruit custom development. S4: Developers can build or customize AI agents in a modular way through the developer module, and submit them to the agent marketplace module for listing after testing and verification. S5: The data monitoring and analysis module performs full-dimensional data collection, monitoring, and quantitative analysis of task execution, cost, efficiency, and revenue at each workstation.
[0016] The advantages of this invention compared to the prior art are: This invention achieves standardized and visual breakdown of enterprise business needs through the scenario-based definition of "office + workstation". Based on the EMS modular architecture and MRG standardized protocol, enterprises can quickly adjust the workstation definition and intelligent agent configuration according to business development needs without large-scale system transformation, which greatly improves the platform's flexibility and scalability and reduces the long-term AI application costs for enterprises. Build an agent hiring marketplace that automatically locates and matches target agents based on business needs, and provides resume and trial functions; Build a comprehensive data system, trust chain, and value anchors to monitor task progress, cost, and efficiency in real time, providing data support for Agent renewal and replacement; This invention uses a bounty center to broadcast difficult-to-match business needs across the entire network, responding promptly to personalized needs and expanding ecosystem capabilities; This invention employs a visual and simulated interactive approach, lowering the barrier to understanding and operation, breaking free from the constraints of traditional GUIs, and enabling rapid iteration and flexible expansion of platform functions. Attached Figure Description
[0017] Figure 1 This is a schematic diagram of the framework structure of the present invention.
[0018] Figure 2 This is a flowchart illustrating the present invention.
[0019] Figure 3 This is a schematic diagram of a workstation management framework based on DTV. Detailed Implementation
[0020] The present invention will now be described in further detail with reference to the accompanying drawings.
[0021] This invention provides an AI intelligent agent ecosystem, including a central platform, an intelligent agent market module, a bounty module, a developer module, and a data monitoring and analysis module.
[0022] The central platform serves as the system's core, constructing a virtual office as the central interactive scenario. It presents business owners with a highly realistic workspace and virtual employee models, reducing the learning and usage costs for businesses. The central platform standardizes and visually breaks down user business needs, generating business tasks.
[0023] Specifically, the central platform generates multiple independent workstations based on business needs, with each workstation corresponding to an independent business need, thereby achieving standardized and visual breakdown of enterprise business needs.
[0024] The AI Agent Marketplace module matches AI agents with corresponding capabilities based on business tasks, providing capability descriptions and trial interfaces for each AI agent. Enterprises can independently search for and hire matching AI agents within the platform. The AI agents in the AI Agent Marketplace module are packaged in a modular capability manner, with each AI agent including a task context, an individual knowledge base, and a structured component containing a set of callable skills.
[0025] When the AI agent marketplace module fails to match an AI agent that meets the requirements of a preset business task, the bounty module will release the business task to the developer community in the form of a bounty. The bounty module includes setting the bounty amount, delivery deadline and acceptance criteria for the unmet business task, and pushing targeted invitations or public recruitment information to registered developers to respond to the personalized needs of enterprises in a timely manner, while expanding the overall AI agent ecosystem capabilities of the platform.
[0026] The developer module allows developers to build new AI agents and submit them to the agent marketplace module. The developer module also includes standardized development templates and a testing sandbox. After developers build, debug, and verify the functionality and compatibility of their AI agents locally or in the cloud, they can submit them to the agent marketplace module for review and listing. Developers use a modular approach to build intelligent agents, breaking down intelligent agent development into three main modules: task context, individual knowledge base, and callable skill set. This allows people without professional development skills to quickly build usable intelligent agents by defining tasks, selecting individual knowledge bases, and configuring skills.
[0027] The data monitoring and analysis module collects, monitors, and analyzes data from all dimensions for each task. Based on data flow, trust chain, and value anchors, it tracks the task execution process of each workstation in real time, assesses credibility, and quantifies benefits. This helps enterprise managers monitor task processing progress, results, costs, and efficiency in real time, providing data basis for the continued use and replacement of intelligent agents.
[0028] In practical applications, combine with the appendix Figure 1-3 As shown: This invention constructs an open ecosystem platform for requirement building, requirement diffusion, and requirement resolution from two dimensions: product design and implementation methods. This platform enables efficient connection between discrete business requirements of enterprises and developers. The specific technical solution is as follows: Using the virtual office as the core interaction scenario, a full-process enterprise agent hiring, management, collaboration, and ecosystem expansion system is created. The core design includes: Core Interaction Scenarios: Using virtual offices as the main interaction strategy, it presents business owners with highly realistic office spaces and virtual employee forms, reducing the cost of understanding and use for businesses; Standardized definition of requirements: Each workstation in the office is linked to the company's independent business needs. Companies can quickly identify their own business needs by building their own or by choosing a ready-made workstation template. Virtual employee (Agent) management: Virtual employees at workstations are AI agents that undertake corresponding business needs. It supports a fully managed mode or a co-pilot collaborative management mode. Enterprises can independently find and hire matching agents within the platform. Personalized Demand Bounty: The bounty center broadcasts workstation requirements that cannot be matched with a suitable agent across the entire network, attracting developers (OPCs) to participate in customized development and meet the personalized needs of enterprises; Low-code Agent Development: A developer center is set up, breaking down Agent development into three main modules: Ego Context (including user profiles, enterprise profiles, professional knowledge bases, etc.), Skills (Agent core capabilities), and Mission Context (Agent goals and pending tasks definition). This allows even beginners without professional development skills to quickly build a usable Agent by defining tasks, selecting Ego Context, and configuring Skills.
[0029] This invention relies on three core technology systems: EMS, MRG, and DTV, combined with modular management and intelligent search and matching mechanisms, to achieve standardized, efficient, and scalable operation of the platform. The core implementation methods include: Agent construction logic based on EMS (Multi-Context Virtual Employee Intelligent Agent): The basic logic for building AI Agent is based on EMS. The core is to decompose the intelligent agent into three independent and composable modules: Ego Context, Mission Context, and Skill. The Mission Context fully adopts the standardized MRG (Task Relevance Graph) format, which is completely isomorphic to the definition format of the workstation in the central platform. The system automatically clarifies specific business problems and execution boundaries by aligning the workstation MRG with the agent's Mission Context format and semantic matching. After successful matching, the system automatically filters and loads the corresponding Ego Context knowledge base (including job knowledge, industry data, enterprise-specific information, etc.) and Skills capability module based on business requirements, and finally generates a dedicated Agent for the workstation with one click, achieving precise adaptation between the Agent and business requirements.
[0030] As the only standardized interaction protocol on the entire platform, MRG not only supports the initial matching of workstations and intelligent agents, but also runs through the entire process of intelligent agent task execution, multi-agent collaboration, and result acceptance, ensuring seamless interaction among all entities within the platform.
[0031] Specifically, the functions and roles of the three main modules of EMS are as follows: Ego Context: As the "identity and cognitive foundation" of an intelligent agent, it defines the agent's core cognition, data dependencies, and essential model components for the corresponding role.
[0032] For example, the Ego Context of a finance-related AI agent includes financial regulations, corporate accounting rules, and tax knowledge; while the Ego Context of a sales-related AI agent includes product information, customer profiles, and sales scripts.
[0033] Mission Context: As the "task execution framework" of the intelligent agent, it is stored in MRG format and clearly defines the core objectives, execution scope, constraints (time, permissions, cost, etc.) and acceptance criteria of the task.
[0034] Completely isomorphic to the MRG definition of an office space, it is the core foundation for achieving precise matching between workstations and intelligent agents.
[0035] Skill (Tool Skill Set): As the "action execution unit" of the intelligent agent, each Skill corresponds to a specific business operation capability (such as data statistics, document generation, email sending, etc.), has standardized input and output interfaces, and can be flexibly combined and called according to task requirements.
[0036] The modular architecture in this invention enables the intelligent agent to have strong scalability: when the enterprise's business needs change, only the MRG definition of the corresponding workstation needs to be adjusted or the Mission Context module of the intelligent agent needs to be replaced, without having to redevelop the entire intelligent agent, which greatly reduces the maintenance cost of the enterprise's AI applications.
[0037] Information communication model based on MRG: MRG (Mission Related Graph, which defines a formal expression of a reference task (Mission)) is used for defining business requirements for office workstations, defining the task context of AI agents, and transmitting information between agents.
[0038] By using a unified MRG format, seamless interaction between workstations and agents, and between agents themselves, is achieved, fundamentally solving the compatibility issues of intelligent agents from different sources and of different types, and standardizing the entire open platform ecosystem.
[0039] Its specific application is divided into three stages: Screening phase: The workstation task description is input into the "numerical intelligent agent engine" in MRG format. The system standardizes the index and retrieval expression in MRG format, connects the intelligent agent to conduct rigorous agent selection, and generates a "set of available agents" to recommend to the enterprise through recall, screening, testing and other processes.
[0040] Usage phase: Since both the workstation and the Agent interface are expressed in MRG format and have undergone prior adaptation testing, it is ensured that the Agent and the workstation requirements are perfectly matched, and the business objectives set by the workstation are efficiently achieved. Collaboration Phase: If a single workstation requires the collaboration of multiple agents, each agent interacts with information through the MRG data structure; the agent engine is responsible for determining the collaboration compatibility between agents, that is, verifying whether the request issued by Agent-i matches the capabilities defined by Agent-j, thereby achieving efficient collaboration between agents.
[0041] like Figure 3 As shown, the workstation management system based on DTV (Data flow, Trustlink, Value point) is centered on DTV, defining the data flow path, permission authorization relationship, and business acceptance indicators for each workstation; with the workstation as the basic unit, it realizes refined analysis of the cost, benefits, data, and behavior collection of all workstations in the office, while subdividing permission management to the workstation level to meet the hierarchical business management needs within the enterprise; This invention also includes modular management of Agents and Skills: at the system level, the management and construction of Agents and Skills are separated. Non-business-specific Skill development results can be connected to the Agent development platform through the Skill developer community, providing capability support for actual business needs of enterprises, while realizing the commercialization of Skill development. In the Agent developer center, based on MRG, a unified input and output specification is set to reduce the platform access barrier for developers. Developers can use any development tools and modes to complete Agent development for specific business needs of enterprises and integrate them into the platform ecosystem.
[0042] The contents not described in detail in this specification are existing technologies known to those skilled in the art.
[0043] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.
[0044] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.
Claims
1. An AI intelligent agent ecosystem, characterized in that: It includes a central platform, an intelligent agent market module, a bounty module, a developer module, and a data monitoring and analysis module; The central platform includes constructing core interactive scenarios, standardizing and visually decomposing user business needs, and generating business tasks; The AI agent market module matches AI agents with corresponding capabilities according to business tasks, and provides capability description information and trial interfaces for the corresponding AI agents. When the agent market module fails to match an AI agent that meets the requirements of the preset business task, the bounty module will release the business task to the developer community in the form of a bounty. The developer module allows developers to build new AI agents to the agent marketplace module; The data monitoring and analysis module performs full-dimensional data collection, monitoring, and analysis for each task.
2. The AI intelligent agent ecosystem according to claim 1, characterized in that: The central platform includes multiple independent workstations generated based on business needs, with each workstation corresponding to a business task. The AI agent performs business tasks at its workstation in either a fully managed mode or a co-pilot collaborative mode.
3. The AI intelligent agent ecosystem according to claim 2, characterized in that: Each workstation is also equipped with a unique identifier and associated with task objectives, input / output specifications, execution constraints, and expected value indicators. The central platform uses the MRG standardized interaction format for information interaction between office workstations and AI agents, as well as between AI agents themselves.
4. The AI intelligent agent ecosystem according to claim 1, characterized in that: The AI agents in the intelligent agent market module are modularly packaged based on the EMS architecture, and each AI agent includes: Ego Context components: Store the basic understanding of intelligent agents' roles, core data dependencies, and essential model components; Mission Context component: Based on the MRG standardized format, it defines the task boundaries, execution constraints, and output standards of the agent; Skill components include multiple sets of standardized business operation tools that can be called independently and have a unified input / output interface.
5. An AI intelligent agent ecosystem according to claim 4, characterized in that: Each workstation generated by the central platform is defined in the MRG format, which is completely consistent with the Mission Context component. The AI agent is bound to the corresponding business requirements through the format alignment and semantic matching of the two.
6. An AI intelligent agent ecosystem according to claim 1, characterized in that: The bounty module includes setting bounty amounts, delivery deadlines, and acceptance criteria for unfulfilled business tasks, and pushing targeted invitations or public recruitment information to registered developers.
7. An AI intelligent agent ecosystem according to claim 1, characterized in that: The developer module also includes standardized development templates and a test sandbox; After developers build, debug, and verify the functionality and compatibility of AI agents locally or in the cloud, they submit them to the agent marketplace module for review and listing.
8. An AI intelligent agent ecosystem according to claim 3, characterized in that: The MRG standardized interaction includes: Business tasks are retrieved and filtered in MRG format to generate a set of usable AI agents; Implement interface adaptation between office workstations and AI agents using the MRG format; Multiple AI agents can collaborate and interact using the MRG format.
9. An AI intelligent agent ecosystem according to claim 1, characterized in that: The data monitoring and analysis module tracks, assesses credibility, and quantifies benefits of the task execution process at each workstation in real time based on data flow, trust chain, and value anchor.
10. A method for constructing an AI agent ecosystem as described in any one of claims 1-9, characterized in that: Includes the following steps: S1: The central platform constructs the core interactive scenarios of the virtual office, standardizes and visualizes the enterprise's business needs, and generates independent workstations for corresponding business tasks; S2: The central platform sends the business tasks of the office space to the intelligent agent market module in a standardized format. The intelligent agent market module matches AI agents according to the task capability requirements and provides AI agent information and trial interface. S3: If the intelligent agent market module does not match an AI intelligent agent that meets the task requirements, the bounty module will publish the business task to the developer community in the form of a bounty to recruit custom development. S4: Developers can build or customize AI agents in a modular way through the developer module, and submit them to the agent marketplace module for listing after testing and verification. S5: The data monitoring and analysis module performs full-dimensional data collection, monitoring, and quantitative analysis of task execution, cost, efficiency, and revenue at each workstation.