Source | China Industrial Internet Research Institute
2025 is widely defined in the industry as the “Year of the Intelligent Agent.” In this year, intelligent agent technology has experienced explosive growth, rapidly transitioning from cutting-edge concepts in laboratories to various industries, gradually emerging.
Unlike general large models, intelligent agents can not only understand human instructions but also perceive the environment, plan actions, and utilize tools to autonomously adapt to application scenarios. Through continuous interaction with real scenarios, intelligent agents accumulate experience and optimize strategies, ultimately achieving a leap from “passive response” to “active execution.” From office process automation to deep interactions in intelligent customer service, and to autonomous operation and maintenance in complex industrial environments, intelligent agents demonstrate extraordinary potential across various fields.
What is an AI Agent Based on Large Models?
An intelligent agent (AI Agent) isa type of AI system that possesses autonomy, reactivity, proactivity, and social capabilities, driven by large models, and includes key components such as Planning, Memory, and Tools.
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Planning: Responsible for breaking down large tasks into sub-tasks, optimizing solution steps through self-reflection and thinking chains, thereby improving the quality of output content.
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Memory: Responsible for storing information; short-term memory refers to learning related content in context, while long-term memory is used to retrieve external databases to complete complex tasks.
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Tools: Responsible for calling external tools, such as application programming interfaces (APIs), to obtain additional information.
Overview of AI Agent Structure
Application Scenarios
In the wave of technological iteration, intelligent agents based on large models have initiated a wave of process reconstruction and model innovation across multiple industries. We will focus on data governance and intelligent operation and maintenance, exploring how intelligent agents use technological power to break industry deadlocks.
1Platform Solution One:Building Intelligent Operation Solutions for Data Assets
Industry Pain Points: In industries such as manufacturing and finance, which are highly dependent on data, data governance has long faced constraints from “manual shackles.” In traditional models, data analysis, metric calculation, and report generation rely entirely on manual operations, which are time-consuming and labor-intensive; the complex structure of data assets makes it difficult to support rapid decision-making, becoming a “stumbling block” for enterprises to respond to market changes.
Intelligent Agent’s Breakthrough: In response to these pain points, intelligent agents serving data governance have emerged, covering processes such as data production management, retrieval analysis, and value delivery, creating a self-operating “data knowledge base” for enterprises.
In the data production management phase, the intelligent agent acts as a “data asset manager”, autonomously constructing an enterprise data knowledge repository based on the metric system. It utilizes large models and retrieval-augmented generation (RAG) technology to project relationships between business metadata and technical metadata, weaving data without the need to rebuild existing data warehouses, accurately identifying effective data assets and completing semantic encapsulation. The woven data includes core elements such as metrics, field names, tags, dimension values, and physical tables, as well as their historical relationships, laying the foundation for constructing a data knowledge graph and achieving a self-closed loop of “data production-governance-sedimentation.”
Data Production Management and Retrieval Analysis
In the data retrieval analysis phase, the intelligent agent transforms into a “decision analysis expert,” allowing data queries and analyses to bid farewell to “technical barriers.” Users only need to input their requirements in natural language, and the intelligent agent can complete semantic understanding and instruction decomposition through large models, accurately finding matching asset projections in the data knowledge graph using RAG technology. Subsequently, the intelligent agent automatically generates multiple structured query statements (SQL), constructing a complete analytical thinking chain, providing clear analytical advice like a “professional consultant.”
In the data value delivery phase, the intelligent agent transforms into a “data salesperson,” enabling the business model to shift from “passive querying” to “active pushing.” Relying on large model technology, it organizes the contextual logic of data assets through data catalogs, standardization, and contextualization, subsequently intelligently sensing the user’s specific industry scenario. Without the need for user inquiries, the intelligent agent can insightfully understand user needs like a “personal advisor,” creating real-time analysis tasks and actively pushing core information to users, allowing business personnel to obtain immediate decision-making basis without mastering SQL or industry knowledge.
Implementation Results: The intelligent agent, by automatically generating SQL and reports through large models, significantly reduces manual intervention in the data governance process, shortens decision-making cycles, and noticeably improves business response speed. In terms of data retrieval analysis, the intelligent agent has served over 8,000 users, with total usage exceeding 320,000 times; in terms of data value delivery, the market business valuation of related intelligent agents has approached 100 billion yuan, proving the value of intelligent agent technology in digital governance.
2Platform Solution Two:Establishing a Digital Operation Team with Multi-Agent Collaboration
Industry Pain Points: As digital transformation deepens, the IT environment of enterprises is becoming increasingly complex, with explosive growth in operation and maintenance demands, and continuous pressure on human resources. The traditional “manual monitoring and passive response” operation and maintenance model is no longer sustainable, and how to use technology to liberate manpower and improve efficiency has become a challenge faced by enterprises in digital transformation.
Intelligent Agent’s Breakthrough: In the face of the wave of digital reform, a digital operation team based on multi-agent collaboration has emerged. The core of this team consists of job-specific intelligent agents and tool intelligent agents, which quickly generate various solutions through multi-agent collaboration, empowering and enhancing the efficiency of actual operation and maintenance personnel, reshaping operational processes, and helping enterprises maintain a competitive edge in the wave of digital transformation.
Overall Framework of Operation and Maintenance Intelligent Agents
Job-Specific Intelligent Agents are the “professional backbone” of the team, trained using deep learning and specialized knowledge graph technology to simulate the professional roles of operation and maintenance positions. Whether it is network monitoring, system optimization, or fault diagnosis, each job-specific intelligent agent is equipped with knowledge and experience in the corresponding field, capable of making professional judgments in complex scenarios that rival human experts.
Tool Intelligent Agents serve as the “bridge” connecting job-specific intelligent agents and actual operation and maintenance tools. This intelligent agent possesses basic functions for using tools, while also understanding higher-level task objectives, autonomously deciding how to apply tools based on context, and proactively providing analytical suggestions, allowing technical tools to truly serve operational goals.
Implementation Results: Through multi-agent collaboration, operation and maintenance intelligent agents can autonomously complete most workflow processes, allowing operation and maintenance personnel to efficiently handle massive amounts of operational information by focusing only on key aspects. Currently, this intelligent agent has successfully empowered 120,000 engineers, providing professional operation and maintenance support for various manufacturing industries, helping enterprises establish a foothold in the digital wave.
Although there are still many issues in current application scenarios, such as data silos, lack of reliability verification, and incomplete standard systems, the trend of intelligent agent technology taking root in various industries has already formed. For enterprises and practitioners, grasping the evolution logic of intelligent agents and laying out core process intelligent transformation in advance will be key to seizing future competitive advantages.
As one of the most noteworthy technological trends today, AI Agents are rapidly evolving and changing enterprise operational models. In the face of a complex situation with both opportunities and challenges, the Zhihui Standard Center, as the drafting organization, advocates and initiates the“Enterprise-Level AI Intelligent Agent Application Effectiveness Evaluation Specification” group standard drafting work.
As thefirst national group standard focusing on AI intelligent agent applications, this standard will provide a consistent and credible evaluation basis for various enterprises and service organizations by constructing a set of evaluation foundations that support the large-scale and high-quality application of intelligent agents, filling the current core gap of quantifying intelligent agent application effects and lacking standards for cross-system comparisons.
The drafting organization/drafters are currently being recruited, and we welcomecloud computing service providers, large language model developers, AI intelligent agent application enterprises, third-party evaluation and certification agencies, AI security and compliance service providers as well asall professional forces concerned with AI intelligent agent application evaluation to join us. If you are interested in becoming a drafting organization/drafter for the “Standard”
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