Urban super intelligent agents achieve intelligent upgrading of urban governance and social services, and medical intelligent agents efficiently handle complex medical and health business work... Since the beginning of this year, intelligent agents have accelerated their industrial application and gradually become an important driving force leading the transformation of industrial intelligence. The interviewed experts believe that with the gradual establishment of industry norms and standardization systems, intelligent agents will accelerate their landing and empower industrial upgrading, which is expected to promote the deep integration of artificial intelligence and the real economy. Technological empowerment enhances efficiency. Intelligent agents, with their ability to perceive the environment, schedule tasks flexibly, and automate complex tasks, combined with technologies such as cloud computing and big data, have shown broad application prospects in multiple fields. Li Wei, Deputy Director of the Cloud Computing and Big Data Research Institute at the China Academy of Information and Communications Technology, analyzed that on the one hand, tasks in general scenarios such as customer service, marketing, office assistants, business intelligence, code assistants, and knowledge assistants have high repeatability and strong process regularity. In these fields, intelligent agents can achieve low-cost automation and precise processing of tasks; On the other hand, in industries with high levels of digitization such as finance, retail, education, healthcare, etc., the process of implementing intelligent agents will be more rapid. Han Jian, Executive Deputy Director of the Artificial Intelligence Research Center and Director of the Information Software Institute at the China Academy of Electronic Information Industry Development, believes that intelligent agent technology is not only a simple upgrade of existing technology, but also a paradigm shift. Intelligent agents enable machines to evolve from simple "script executors" to "task executors" capable of independent thinking and problem-solving, completing a crucial transition from tools to partners. Intelligent agents have incorporated unstructured tasks that were previously only achievable by humans and require understanding and judgment into the realm of machine execution, greatly expanding the boundaries of machine capabilities. At the same time, intelligent agents integrate isolated systems and tasks inside and outside the enterprise into an efficient, collaborative, and self regulating "digital organism", greatly improving process efficiency. Traditional enterprises have a large number of independent software systems, and the data and processes between them are often fragmented, requiring manual switching and operation as "middleware". Intelligent agents can act as' super connectors', interacting with all systems through tool calls, aggregating previously dispersed functions into a unified, intent driven operating layer. Multiple intelligent agents can negotiate, divide labor, collaborate, and even compete like a team, and the overall intelligence exhibited by the system will far exceed the sum of individual intelligent agents. Intelligent agents, as "digital employees" joining organizations, liberate enterprise employees from repetitive and tedious work, achieving exponential improvement in organizational operational efficiency. Artificial intelligence is transitioning from the stage of large-scale models to a new stage of intelligent agents. Although traditional large models have strong knowledge reserves and language abilities, they cannot decompose and plan tasks, and cannot use tools. In order to compensate for the shortcomings of large models, intelligent agents have emerged. Intelligent agents have the ability to decompose and plan tasks, the ability to remember and inherit knowledge, the ability to use tools, and the ability to divide labor and collaborate. When all four abilities are possessed, the intelligent agent can complete a complex task from start to finish like a human. ”360 Group founder Zhou Hongyi said. The gradual expansion of application scenarios is regarded as the first year of intelligent agent industrialization this year. Currently, industry enterprises are continuously increasing their efforts in the application of intelligent agents, and the application scenarios in various fields are gradually expanding. At the Clinical Skills Training Center of Qilu Hospital, Shandong University, dozens of virtualized "digital patients" were welcomed in the annual assessment of 87 resident physicians. The digital patient intelligent agent created by Inspur Enterprise Cloud breaks the temporal and spatial limitations of physical cases and offline teaching by constructing a multimodal interactive training and artificial intelligence assisted assessment system, bringing medical students a more flexible learning experience; It can also fully retain training process data and intelligently analyze students' knowledge weaknesses, providing customized training plans. The digital patient intelligent agent has to some extent alleviated the shortage of medical practical resources and rare cases, allowing medical students to conduct practical training anytime and anywhere, and also facilitating the assessment of medical schools, "said Wang Siying, product manager of Yunfan Digital Patient Intelligent Agent in Inspur Enterprise Cloud. In Hongkou, Shanghai, Lenovo's urban super intelligent agent achieves full domain intelligent coverage and deeply intervenes in the entire process of urban management; In Yichang, Hubei, Lenovo promotes refined urban operation and sustainable development by integrating comprehensive data resources such as transportation, energy, and government affairs. In addition, Lenovo has also reached strategic cooperation with Mount Wuyi, Fujian, Hohhot, Inner Mongolia and other cities, enabling cultural tourism, transportation, medical care, energy and other fields. International Data Corporation (IDC) predicts that by 2026, approximately 50% of China's top 500 data teams will use intelligent agents for data preparation and analysis. Han Jian stated that some cross industry general intelligent agent products have begun to be applied on a large scale, focusing on areas such as customer support, software development, sales, and general enterprise workflows. The commercialization of vertical professional intelligent agent products has begun and is expected to become an important lever for the digital transformation of the industry. The explosive development of intelligent agents is the result of a series of technological, market, and ecological factors resonating at the same frequency. The powerful logical reasoning, task decomposition, long-term memory, and code generation capabilities of large models provide a technological foundation for the development of intelligent agents; The reduction of development and application barriers provides ecological guarantees for the rapid development of intelligent agents; Clear business needs and the driving force of the capital market provide fertile ground for the development of intelligent agents. The large-scale implementation still faces some bottlenecks in the industrial application of intelligent agents. According to Han Jian's analysis, from a technical perspective, model performance and high-quality datasets have become key bottlenecks that constrain the improvement of intelligent agent performance. The modeling ability still needs to be improved. From an application perspective, uncertain decision quality and insufficient cross scenario collaboration capabilities are still the main factors hindering large-scale implementation. From an ecological perspective, there are risks and challenges such as security protection, unfair competition, technological ethics, and maturity evaluation. The standard specifications for intelligent agent interconnection need to be unified. ”Li Wei stated that currently, there are challenges in the field of multi-agent applications, such as poor tool calling and inflexible cloud and computing power calling. To promote intelligent agents from "experimental products" to "products" and then to "commodities", efforts need to be made simultaneously at the three levels of technology, trust, and ecology. Han Jian suggests focusing on improving the reliability and synergy of intelligent agents at the technical level. Accelerate the construction of a human-machine collaborative "safety belt" at the level of trust and governance. At the ecological level, continuously lowering the development threshold. Zhou Hongyi believes that enterprises should use intelligent agents as humans and clarify their role positioning. Priority should be given to pilot positions with clear processes and labor-intensive tasks, such as contract review and market research. Build a virtual team to collaborate and handle complex tasks through multi-agent division of labor. Designed by the backbone who understands the business process, with technical support provided by the artificial intelligence team. Key decisions are subject to human supervision to ensure they are controllable and manageable. We should continue to improve the open-source framework for intelligent agent development, and have cloud vendors provide a one-stop development, hosting, and management platform to enable developers to create and deploy intelligent agents more conveniently. Build industry level benchmarks and datasets. Establish standard testing platforms and high-quality industry datasets for training and evaluating the professional capabilities of industry agents, accelerating their application in vertical fields. ”Han Jian said. (New Society)