Analysis of the impact of Agentic AI on the ICT ecosystem and industry digital transformation

The definition and origin of Agentic AI

Definition and concept: "Agentic AI" generally refers to an AI system that is autonomous, active, and can accomplish complex goals with limited human intervention . Such systems consist of one or more AI agents, each of which can perceive the environment, make decisions, and perform actions, similar to how human agents solve problems autonomously . Unlike traditional AI that only responds passively to preset instructions , Agentic AI has the ability to set goals and take actions autonomously , showing a high degree of autonomy, goal-oriented behavior, and adaptability . For this reason, the industry also calls it " agency-based " AI , which means that the machine is given a certain degree of agency and can act independently and purposefully . For example, a traditional generative AI model (such as ChatGPT ) can generate text or code based on input, but an Agentic AI system can use these generated content to call external tools to automatically complete complex multi-step tasks . In other words, Agentic AI will not only answer " the best time to climb Mount Everest " , but will also help users book air tickets and hotels autonomously, and " put the answer into action " .

Academic and technological origins: The concept of Agentic AI can be traced back to the early research on agent theory and multi-agent systems. In the 1990s , AI researchers proposed the " agent " model , emphasizing that AI should perceive , think and act like a rational agent. Classic AI textbooks define agents as systems that " can autonomously perceive the environment and take actions to achieve goals " , which laid the theoretical foundation for Agentic AI . IBM 's Deep Blue chess program in the 1990s was also regarded as the prototype of an early autonomous agent, which made autonomous decisions to play chess in a specific environment . Subsequently, the academic community developed reinforcement learning , distributed multi-agent systems and other directions, enabling AI to gradually acquire decision-making and collaboration capabilities in a dynamic environment. However, most of the agents at that time were specialized and narrow in scope, lacking the general intelligence and language understanding capabilities brought by today's large models.

The Agentic AI wave that really attracted public attention appeared in 2023 , thanks to the rise of large-scale pre-trained models (LLM) and the emergence of autonomous agent applications . OpenAI and other institutions conducted systematic research on Agentic AI in 2023 : OpenAI researcher Lilian Weng proposed the AI ​​Agent technology framework and released a white paper in the same year to formally define the "Agentic AI system " . The white paper defines Agentic AI systems as a new type of AI that " can adaptively pursue complex goals and requires only limited direct supervision . " At the same time, AI expert Andrew Ng demonstrated the potential of Agentic AI in his speeches at the Sequoia Capital AI Summit and Snowflake Developer Day , and proposed the concept and design paradigm of "Agentic Workflow", which further ignited the enthusiasm of the technology community . It can be said that the combination of large language models + autonomous agents has made Agentic AI a new frontier in the field of AI , leading the transformation of AI from a " tool " to an " autonomous manager " .

Difference from traditional AI : Compared with traditional AI systems that passively respond according to rules or training patterns, Agentic AI represents a qualitative leap - from " reactive " to " active " . Traditional AI usually operates within predefined constraints and requires human intervention and correction in the loop; Agentic AI is more like an autonomous intelligent partner that can autonomously plan, make decisions and perform tasks in a complex and changing environment . For example, in the field of customer service, traditional customer service robots can only answer questions according to scripts, while Agentic AI customer service can autonomously understand customer intentions, check balances, match solutions, and even wait for users to make decisions before continuing to complete transactions, providing autonomous services throughout the entire process. This autonomy comes from the fact that Agentic AI integrates advanced reasoning and iterative planning capabilities, which can solve multi-step problems in a coherent manner. In addition, Agentic AI has adaptability and learning capabilities: it can continuously adjust strategies based on feedback and learn to optimize behavior from experience, rather than being limited to the information already in the training set . In short, Agentic AI breaks through the limitations of traditional AI as a passive tool , allowing AI systems to begin to have human-like initiative, which brings both huge opportunities and new technical and ethical challenges .

The similarities, differences and relationships between Agentic AI and AI Agent

Concept Analysis: "AI Agent" ( AI agent or intelligent body) and "Agentic AI" are only one word apart, but they have different focuses. [ AI Agent ] usually refers to a specific intelligent entity that can perceive the environment, make autonomous decisions and perform actions, and is often used for automated tasks in a certain field . For example, an RPA robot combined with AI capabilities can read emails and automatically enter data into the system. This is an AI Agent . AI Agent is more like a tactical executor , autonomously completing a single task within a limited range, and is the basic unit of AI application . In contrast, [ Agentic AI ] emphasizes a system capability and paradigm , which is an overall intelligent system composed of a series of highly autonomous and collaborative AI Agents . Agentic AI focuses on the " active " characteristics of the AI ​​system - the system can actively think, plan and perform a series of tasks in a changing environment, and move towards complex long-term goals . In short, AI Agent is a specific " intelligent body " , while Agentic AI refers to an autonomous intelligent system with a " big picture view " . It may be composed of multiple AI Agents working together and has a higher level of autonomous decision-making and adaptability.

Overlap and difference: There are overlaps between the two: Agentic AI systems are often composed of one or more AI agents . Without AI agents , there is no Agentic AI . At the same time, many AI agents have a certain degree of autonomy, which initially reflects the characteristics of Agentic AI . However, the difference between them lies in the scope of capabilities and positioning . Traditional AI agents focus on the automation of a single field or task . For example, the digital employee who automatically fills out reports mentioned above is an AI agent focusing on office automation . Such agents usually follow preset processes or limited strategies. Even if they have learning capabilities, their autonomy is limited. Agentic AI is a comprehensive upgrade and expansion of this concept: it requires the system to not only complete preset tasks, but also autonomously seek solutions in unknown situations and dynamically plan new tasks . Agentic AI emphasizes autonomy at the strategic level : the system can face an open environment, operate autonomously for a long time, discover problems and solve them by itself, and even generate new sub-goals. This high degree of initiative and adaptability far exceeds the capabilities of general AI agents .

Relationship between system construction and application: In practical applications, Agentic AI and AI Agent are often in a hierarchical collaborative relationship. Agentic AI system can be regarded as a group of AI Agents organized according to a certain architecture to achieve " group intelligence " . Among them, Agentic AI acts as a strategic layer: determining the overall goal of the system, assigning tasks and adaptive strategies in complex situations; AI Agent is at the tactical layer : each performs its duties, performs specific perception, analysis and operation tasks, and implements strategic intentions . For example, in an Agentic AI system of an intelligent customer service center, it may contain multiple AI Agents : one is responsible for voice recognition to listen to customer questions, the second Agent queries the knowledge base to find answers, and the third Agent performs subsequent operations (such as handling business or recording feedback). The Agentic AI layer of the entire system will autonomously plan the customer service process according to the type of customer problem (first greeting, then identifying the problem, and then deciding on the solution), coordinate the division of labor and cooperation among various Agents , and finally complete the service. The two complement each other: Agentic AI provides macro autonomy and decision-making framework, and AI Agent provides micro execution capabilities . As one analysis points out: "AI Agent is more like an executor in the ecosystem, completing various tasks and goals under the strategic guidance of Agentic AI . " Therefore, we can regard Agentic AI as a future-oriented AI system design concept , and AI Agent is the specific implementation unit under this concept . The combination of the two not only ensures the depth of technology implementation, but also expands the breadth of application, and jointly promotes the evolution of artificial intelligence from single-point intelligence to autonomous and collaborative intelligent ecology .

The Impact of Agentic AI on the ICT Technology Stack

The rise of Agentic AI will have a profound impact on the technology stack in the ICT field, especially in the six major sectors of infrastructure, data, platform, application, service and security. Since Agentic AI introduces autonomous intelligent agents and complex workflows, all layers of technology need to be adjusted to support this new paradigm. The following analyzes the impact of each layer and the possible emerging technologies and architecture evolution:

Infrastructure​​

At the infrastructure level, Agentic AI brings new requirements for computing architecture and resource layout . First, computing demand surges and runs continuously : autonomous agents usually need to repeatedly reason and respond to the environment in real time, which means that the proportion of AI inference load in the overall AI workload will increase significantly. A report pointed out that with the popularization of agents, the AI ​​inference load will be on par with training in 2025 , and will soon surpass training to become the main AI workload . This requires the infrastructure to provide sufficient real-time computing power to support long-running agents . To this end, chip manufacturers and cloud service providers need to launch more efficient inference accelerators and computing optimization architectures to meet the 7x24- hour operation of autonomous agents . At present, some large companies have even begun to invest in self-built energy facilities (such as small nuclear power plants) to ensure the power supply of future computing power, which shows the challenges to infrastructure . Secondly, the architecture extends to distributed and edge : many Agentic AI applications (such as industrial robots, unmanned vehicles, etc.) need to make real-time decisions close to the data source, which prompts computing to sink from the cloud to the edge. Deploying lightweight and efficient agent models at the edge to achieve local autonomous decision-making can reduce latency and improve reliability. This has driven the development of edge computing architecture , enabling devices to have local AI reasoning and collaboration capabilities. Again, real-time communication and collaboration are key: multiple AI agents working together require a high-speed, low-latency network communication foundation. Therefore, communication technologies such as 5G/6G will play an important role in the Agentic system to ensure real-time synchronization of information between agents and the cloud, and between agents. Within the data center, in order to cope with large-scale deployment of agents , high-bandwidth interconnection and memory architecture also need to be upgraded to support agents to quickly access massive knowledge bases and environmental data. Finally, infrastructure vendors have begun to integrate support for Agentic AI in their products, such as providing an "Agent as a Service " operating environment, containerized deployment solutions, and scheduling systems to facilitate users to flexibly deploy and manage agents on their infrastructure . In short, at the basic level, Agentic AI drives computing hardware from pursuing peak computing power to pursuing long-term stable reasoning performance and wide-area collaboration capabilities , and the traditional cloud architecture has evolved into a cloud - edge - end collaborative agent operation platform.

Data​​

Agentic AI places higher requirements on the data layer for integration and governance . First, the intelligent agents of Agentic AI need to be able to obtain multi-source heterogeneous data to perceive the environment and make decisions . This means that the enterprise's data platform must connect various data islands and build a unified data access and sharing mechanism . For example, in the financial field, an agent may need to access the transaction database, customer relationship system, and market API at the same time to make a comprehensive decision. Therefore, the data architecture will evolve from traditional data warehouses and data lakes to knowledge bases that support real-time retrieval and online queries . The vector database and retrieval augmentation generation (RAG) technology that have emerged in recent years are the embodiment of this trend: by vectorizing unstructured knowledge, agents can quickly retrieve relevant information to assist reasoning . This enables Agentic AI systems to break through the limitations of training data, use external knowledge sources to complete tasks, and improve decision-making accuracy . Secondly, data organization and semantic modeling are becoming increasingly important. In order to autonomously " understand " data, agents need perfect metadata and knowledge graph support to convert raw data into semantic information that can be understood by intelligent agents. This drives enterprises to strengthen data standardization and knowledge graph construction, and provide agents with contextual understanding capabilities. Third, Agentic AI strengthens the demand for data quality and governance . Because agents make key decisions autonomously, data errors may directly lead to action errors. Therefore, enterprises must ensure that the data input to agents is accurate, up-to-date and secure . This includes real-time verification of data, conflict detection, and immediate notification of relevant agents to adjust strategies when data is updated . In terms of data governance, it is also necessary to set the data scope and permissions that agents can access to prevent them from accessing unauthorized or sensitive data and causing compliance risks. Finally, closed-loop management of feedback data : Agentic AI systems will continuously generate interaction and result data. These new data need to be collected and analyzed for training and improving models or strategies (to achieve self-learning). Therefore, the data platform must be able to record agent behavior logs , environmental changes and result feedback, forming a closed loop of data - decision - feedback to support continuous optimization of agents . Overall, Agentic AI promotes the transformation of the data layer from passive data storage to active knowledge supply and management : that is, by strengthening data fusion, real-time retrieval and quality governance, it provides reliable " perception nutrients " for autonomous intelligent bodies to ensure that they make decisions in complex environments.

Platform​​

The platform layer involves AI middleware, development frameworks, and cloud platform services. The rise of Agentic AI is leading the iterative upgrade of AI application development platforms . On the one hand, major cloud vendors and open source communities have launched development frameworks and middleware for Agentic AI . These platforms help developers build, manage, and monitor AI agents more easily. For example, a number of frameworks for autonomous agent orchestration have emerged, such as LangChain , AutoGPT , MiniChain , etc., which are used to simplify the implementation of multi-step tasks. In more complex cases, there are frameworks that support " agent teams " : such as LangGraph , CrewBot , etc., which can design the division of labor and collaboration processes of multiple agents . It is reported that emerging platforms such as LangGraph can support the design, deployment, and management of agents, helping teams build Agentic AI systems that can autonomously navigate complex environments and adapt to dynamic needs . Such platforms provide functions such as visual orchestration of agent workflows , tool call management, and memory storage, allowing developers to implement complex agent logic with less code. In addition, in order to standardize agent behavior, the platform layer also introduces " agent design patterns " and templates. The technical community has summarized a variety of agentic workflow patterns (such as chain thinking ReAct , tool call RAG mode, etc. ), and encapsulated them in the platform for developers to choose. This is similar to the design pattern of software architecture in the past, which helps to establish a reliable workflow structure in the field of agentic AI . On the other hand, traditional AI platforms (such as machine learning platforms and cloud AI services) are also integrating agentic capabilities. For example, many cloud vendors have integrated conversational interfaces, tool plug-ins, and process orchestration into their AI platforms , allowing developers to create their own AI Agents directly on the cloud . The plug-in ecosystem announced by Microsoft, Google, etc. allows third-party tools to access large models and enable them to have agent execution capabilities, which essentially turns the cloud platform into a sandbox for agentic AI operations . The platform layer also needs to focus on scalability and monitoring : when many agent instances are running in a production environment, the platform must provide the ability to monitor the status, decision-making process, and results of each agent , and detect anomalies in a timely manner. This has given rise to a new AgentOps toolchain, which is similar to MLOps but focuses on runtime behavior monitoring, playback, and security management. In summary, driven by Agentic AI , the platform layer is evolving towards low-code, modularization, and orchestration , providing one-stop support from development, debugging to deployment, and reshaping the AI ​​application development paradigm (it is said that there is an open source framework that claims to " build production-level intelligent agents with 100 lines of code " and aims to redefine the AI ​​development model ). This evolution will greatly reduce the threshold for building Agentic applications and accelerate the implementation of customized intelligent agents in various industries.

Application​​

At the application layer, Agentic AI will change the form of software applications and the way users interact, and promote the transformation of application architecture from static processes to dynamic intelligent agents . First, the implementation of application functions will be transformed : in traditional applications, developers pre-design the functional process, and users follow fixed steps. After the introduction of Agentic AI , applications can have one or more intelligent agents built in, which can actively perform operations within or across applications according to the user's high-level instructions . This means that applications are no longer just passive tool sets, but become more " live " intelligent assistants. For example, in office applications, users only need to use natural language to say " please help me organize this report and send it to Zhang San " , and the AI ​​agent can call sub-functions such as document editing and email sending to automatically complete the entire process. This " you say, the application does " model greatly improves the automation and intelligence of applications . Secondly, the user interface and interaction paradigm change : due to Agentic AI's strong language understanding and decision-making capabilities, the interface of future applications will be more inclined to conversational, intent-driven interactions rather than complex menus and forms. As IBM pointed out, users can interact with the intelligent system directly through natural language, " the many tabs, drop-down menus and other interface elements in the past SaaS applications may be simplified or even eliminated . " The user only needs to describe the goal, and the agent will call the application modules behind the scenes to achieve the goal. This intuitive interaction lowers the threshold for use and improves the user experience. Third, the application architecture is more open and integrated : Agentic AI often needs to collaborate across applications to achieve complex goals, prompting deep integration between applications through APIs and plug-ins. In the future, a single application may no longer be a closed system, but a " place " for agents to perform specific functions . For example, a sales management agent may interact with CRM software, email clients, and social media interfaces at the same time to complete business. Therefore, applications need to provide standardized interfaces for AI agents to call and achieve seamless connection between different software. This will accelerate the development of software products towards platformization and service-oriented ( API -oriented), the boundaries of applications will become blurred, and more functions will be connected through intelligent orchestration rather than manual operations. Fourth, the change in software function design thinking: When designing applications, developers need to consider how to enable AI agents to use application functions. In the past, the focus was on the user click process, but now it is necessary to design the application's functional modules and how data output is understood and used by AI . For example, provide machine-readable document structures, operation interfaces, and verifiable and rollback mechanisms for agents to call to prevent wrong decisions from causing irreversible consequences. This actually adds the dimension of " AI user- oriented " to application design . Finally, new application models emerge : Agentic AI will also give rise to new application categories, such as " digital human " applications - AI agents that simulate experts in specific fields , which can independently interact with users and provide services; and " adaptive business process " applications - dynamic workflows that adjust execution strategies in real time according to the environment. Traditional software vendors are also adding " collaborative intelligent body " modules to their applications (such as the Copilot smart assistant in office software ) to make them Agentic AI within the application . This series of changes will reshape the application ecosystem, and the software industry may shift from selling functions to selling results : because users are more concerned about what tasks the Agent has completed for them, rather than which specific functional modules have been used.

Service​​

The service field here refers to IT services, business process outsourcing, and enterprise operation services in the ICT industry. The arrival of Agentic AI will reshape the delivery model and content of digital services . First, in terms of IT operation and support services : Traditionally, monitoring, troubleshooting, and customer support work that rely heavily on manual labor can be highly automated by introducing Agentic AI . For example, operation and maintenance service providers can deploy intelligent agents to monitor system logs and network traffic in real time 24/7 , automatically analyze alarms, and try to self-repair common faults . Only in the case of complex problems will manual intervention be notified. The survey shows that 40.7% of enterprise users are satisfied with Agentic AI's process operation and data processing efficiency, and 51.5% are satisfied with its effect in risk analysis and alarm judgment . It can be seen that Agent is beginning to show its value in operation and maintenance services. In this way, IT service providers need to transform into "AI efficiency-enhancing services " : through intelligent agents to undertake repetitive tasks, engineers play more roles in training and supervising AI , and service delivery will be faster and more efficient. Secondly, in the field of business process outsourcing (BPO) and enterprise process services : Agentic AI is expected to become a " digital workforce " and perform many tasks that were originally completed by manual BPO personnel. For example, in financial shared center services, Agent can automatically handle invoice review and reimbursement processes; in human resources services, Agent can complete resume screening, interview arrangements, etc. In some government services, "AI digital employees " have been deployed to replace some manual operations. According to feedback, it can save more than 100 hours of work time for government staff each month . This efficiency improvement means that service providers can deliver more work with less manpower. However, this also forces service providers to adjust their business models: the past model of charging by man-hours may shift to charging by the number of automated processes or results . Service providers need to develop their own Agentic AI solutions or cooperate with AI companies to provide customers with " intelligent process automation " services. And customers will also expect service providers to provide business results (such as increased order processing volume and customer response speed) rather than just manpower input. Third, consulting and professional services will also be affected: Agentic AI can quickly retrieve knowledge and perform analysis, which allows some basic consulting work (such as data collection and report draft writing) to be completed by AI agents. Consulting companies should shift their focus to higher-level strategy formulation and AI empowerment, acting as "AI coaches " for customers - helping customers identify businesses that can be transformed with Agentic AI , customizing exclusive intelligent agents, and providing a governance framework to ensure the safe operation of AI . This requires consulting service personnel to master the technical principles and application methods of Agentic AI , and further integrate traditional business consulting with AI technology consulting. Fourth, for hosting services and cloud service providers: Agentic AI provides an opportunity for differentiated services. For example, cloud vendors can launch " Agent hosting " services, allowing enterprises to deploy customized AI agents in the cloud, and the vendors are responsible for operation, maintenance and security monitoring. This is similar to the application hosting in the past, but the object becomes an intelligent agent. Such a service model can help industry customers who lack AI operation and maintenance capabilities quickly enjoy the benefits of Agentic AI , and also bring new revenue growth points to service vendors. Finally, solution providers in different industries also need to incorporate Agentic AI elements into their services . For example, ERP implementation service providers can provide agent plug-ins to allow the ERP system to automatically execute some business approval processes; call center outsourcing providers can deploy intelligent customer service agents to improve 7×24 service capabilities. In general, Agentic AI will shift the service industry from " manual intensive " to " intelligence intensive " . Service providers must upgrade their capabilities by embracing AI agents, otherwise they will face the risk of being eliminated by innovators.

Security​​

The security field will be affected by Agentic AI in two ways: on the one hand, new security risks will emerge , and on the other hand , security defense capabilities will be improved . First, consider the risk challenges: Giving AI greater autonomy also brings the risk of loss of control and misuse . If the Agentic AI system is not properly constrained, it may perform inappropriate operations and cause actual damage. For example, if an enterprise authorizes an agent to manage financial transactions, how can it be held accountable and avoid losses in the event of an erroneous operation or overstepping of authority? For another example, the agent may be attacked and manipulated by criminals, thus acting as an " accomplice " of cyber attacks . OpenAI pointed out in its white paper that while Agentic AI brings efficiency improvements, it also introduces new potential hazards, requiring all parties to establish security responsibilities and best practices . Therefore, in terms of security architecture, strict control mechanisms must be added to Agentic AI . These include: limiting the scope of action of the agent (action space constraints) and requiring manual confirmation of high-risk operations ; setting up identity authentication and permission management to ensure that only trusted agents perform sensitive tasks and clarify their authorization boundaries ; real-time monitoring of agent behavior logs to establish auditability so as to trace the decision-making process and detect anomalies in a timely manner. It is worth noting that there are currently no specific laws and regulations at home and abroad to regulate the legal status and authority boundaries of Agents . When using Agentic AI , enterprises need to actively cooperate with regulators to explore compliance solutions . Secondly, data and privacy security need to be paid special attention: Agentic AI often needs to access a large amount of internal enterprise data, which requires ensuring that Agents do not leak or abuse data. Even if the system is deployed in a private environment, it is necessary to prevent the agent from outputting sensitive information to an unsafe environment . Security vendors need to develop data isolation and encryption technologies , such as labeling the data queried by the Agent , and sensitive data is only allowed to be inferred locally and not uploaded to the cloud. In addition, to prevent the model itself from leaking information, desensitization technology or federated learning and other protection measures should be applied to the large model of the Agent. Third, Agentic AI itself can also be used as a new weapon for security defense . Security companies are already exploring the use of AI Agents for active network defense and monitoring . Agents can continuously scan network traffic and system logs and respond immediately when suspicious patterns are found . For example, if a security agent detects an abnormal login attempt, it can take autonomous measures such as locking the account or strengthening verification. Its response speed and coverage far exceed those of the manual team, improving the real-time nature of the security system. At the same time, Agent can also undertake repetitive work in security analysis, such as automatically aggregating multi-channel threat intelligence, correlating security events, and providing preliminary research and judgment reports for security experts to review. This human-machine collaboration method improves the efficiency of the security operation center (SOC) . The survey shows that 63.0% of security practitioners believe that Agentic AI can be a beneficial supplement and enhancement to the traditional security system . Some cutting-edge applications even let multiple AI Agents simulate attackers and defenders to conduct confrontational drills to discover system vulnerabilities and improve countermeasures. Fourth, the role adjustment of security vendors : In the face of the Agentic AI wave, security products need to expand their functions in a timely manner to specifically protect against threats related to intelligent agents. For example, the "Agent Guardian " system was launched to provide unified behavior monitoring, policy management, and sandbox isolation for AI Agents deployed by enterprises . When the Agent attempts a high-risk operation, it should first be tested in the sandbox, or its decision safety should be verified through simulation before execution. In addition, security vendors should participate in the formulation of industry standards and regulations for Agentic AI , help establish a security compliance framework , and thus become trusted consultants and partners for enterprises. In general, Agentic AI represents " both opportunities and risks " in the security field : we must not only utilize its capabilities to enhance security protection, but we must also face up to and manage the new risks it brings, and ensure that autonomous intelligent bodies " stay on track and do not derail . "

The Impact of Agentic AI on the Software Industry

Agentic AI not only changes the way technology is implemented, but also has a profound impact on the software industry itself, including development paradigms, tool chains, team organization, and business models.

Development paradigm innovation: Software development is shifting from " writing code " to " assembling intelligence " . In the past, developers focused on how to implement business logic with programming; in the era of Agentic AI , developers need to think more about how to orchestrate AI agents to complete business . This is reflected in two changes: First, generative AI -assisted programming is becoming increasingly popular. AI can automatically generate some code or configuration to make development more efficient. Furthermore, developers can directly specify the target function, and multiple AI Agents collaborate to " self-develop " an implementation plan. For example, a team launched a product called "MGX" , claiming that multiple AI assistants collaborate to form an AI development team that works 24/7 non-stop and can automatically complete software development tasks . Another open source project, MetaGPT , attempts to let different GPT agents play the roles of product managers, architects, coders, testers, etc., and jointly develop complete applications . Although these are still in the early stages of exploration, they foreshadow a new paradigm in which software development may shift from people writing code to people describing intentions, AI generating code , or even AI self-organizing to complete coding . The role of developers will focus more on high-level design, intent calibration, and result verification , while the specific implementation details will be filled in by AI agents. This requires developers to have new skills to work with AI , such as prompt engineering (designing effective prompts to allow AI to produce the desired results) and intelligent agent behavior tuning.

Toolchain and framework evolution: To support the new paradigm, the toolchain of software engineering is also evolving. Traditional IDEs and CI/CD platforms are incorporating AI assistant functions, such as automatic code completion, intelligent debugging, and test generation. For Agentic AI applications, new tools are beginning to emerge, including: visual orchestration tools for designing intelligent agent workflows , supporting debugging of each step of the decision-making process; link tracking systems that monitor Agent calls to various tools and APIs , making it easier for developers to understand AI behavior ; and frameworks for managing the collaboration and status of multiple Agents to ensure the controllability and reliability of complex processes . For example, some open source frameworks provide the MCP ( Model Context Protocol ) protocol for multi- agent communication, and some provide graphical interfaces to display information exchange between Agents , allowing developers to debug intelligent agent interactions like debugging distributed systems. These new tools reduce the difficulty of Agent development and debugging. As one framework advertises, it redefines the AI ​​application development model with the concept of " building production-level intelligent agents with just a hundred lines of code . " It can be foreseen that traditional software engineering will incorporate more AI components to form an "AI-Native" development pipeline. For example, AI is used to automatically generate user stories from documents during the requirements analysis phase; AI is used to verify the architecture according to best practices during the design phase ; AI coding is used to assist in the implementation phase ; AI agents automatically generate and execute test cases during the testing phase; and AI behavior is monitored in real time during the deployment phase. AI is involved in the entire software life cycle , thereby improving efficiency and quality.

Changes in team organization and division of labor: As Agentic AI participates in the development process, the organizational structure and division of labor of the software team will also be adjusted. First, the team size may be reduced or streamlined because AI undertakes some coding and testing work. A small team with powerful AI tools may complete projects that used to require a large team. This brings about a new form of " human-machine fusion " team : the role of "AI development assistant " is added to the team members , and the development sub-team is composed of multiple AI agents, and human members serve more as supervisors and coordinators. For example, an architect may manage several AI coding assistants at the same time, reviewing the code and design submitted by them every day, thereby expanding personal production capacity. Secondly, new positions and skill requirements have emerged, such as prompt engineers and AI maintenance engineers . The former focuses on designing and optimizing prompts for interacting with large models /agents , and the latter is responsible for monitoring the performance of AI systems, adjusting model parameters and knowledge bases, and ensuring that the Agent output meets expectations. Third, the way teams collaborate has changed: in the past, engineers communicated through documents and meetings; in the future, teams may collaborate through shared AI assistants . For example, in project management, an agent is introduced to track task progress, integrate individual codes, and remind conflicts, so that members can collaborate indirectly by interacting with the agent . For cross-departmental teams, the agent can also act as a " translator " and " coordinator " , automatically summarizing the status of different modules and proposing interface modification suggestions. In general, human creativity and the efficient execution of AI will be deeply integrated . Organizations need to cultivate a culture of " working with AI " so that team members can trust and make good use of AI tools, while maintaining prudent control over AI output.

Changes in software products and business models: Agentic AI will also lead to changes in the business model and value proposition of the software industry. Traditional software companies sell software licenses or subscriptions, and their value lies in the functional modules themselves. As Agentic AI becomes more popular, customers are more concerned about what business results AI- driven can achieve , rather than the function buttons on the software interface. Therefore, software vendors may shift from selling products to selling solutions and results . For example, when an enterprise purchases financial software, it used to focus on its reporting functions, but now it focuses more on whether its built-in AI agent can automatically complete tasks such as invoice processing and anomaly detection and bring cost savings. In this way, software companies need to price around the intelligent automation effect of specific scenarios , such as charging by the number of transactions processed, rather than by the number of users. This is actually close to the " pay by results " or "AI as a service " model. We have seen some RPA software vendors start to charge by robot hours or task volume, and this model will be extended to the Agentic AI field in the future. At the same time, software products will appear more in the form of platform + ecology : vendors provide basic large model interfaces and agent platforms, allowing customers and third-party developers to customize intelligent agents on them to meet various long-tail needs. By cultivating a developer ecosystem, software vendors’ products become an incubation platform for intelligent agent applications . Their commercial value comes from controlling the ecosystem, not just a finished software. Another change is the evolution of the competitive landscape : Agentic AI may break down barriers in certain software fields. For example, users originally needed to purchase multiple professional software to complete different functions, but if a powerful agent can perform tasks across these software, users’ dependence on a single software will be reduced. This will force software vendors to be more open and cooperative, allowing intelligent agents to call their functions in an API manner, otherwise they may be replaced by more open competitors. Many enterprise software have launched open interfaces and plug-ins to adapt to the calls of AI agents. In terms of business models, “ digital employee ” rental services may also appear, that is, companies do not directly purchase software, but rent AI agents that can complete certain types of work on demand (equivalent to hiring digital employees). This service is provided by software companies with backend support and continuous training and upgrading. Agentic AI is not an incremental improvement of automation, but a disruptive capability that will become a competitive necessity ; forecasts show that by 2028 , 33% of enterprise software applications will be embedded with Agentic AI ( compared to less than 1% in 2024 ) . This indicates that the software industry must quickly embrace this trend and comprehensively transform from product design to business model in order to remain competitive in the next generation of intelligent software.

The impact of Agentic AI on the digital transformation of industry users

Agentic AI injects new impetus into the digital and intelligent transformation of all industries. The following uses the six major industries of finance, government, manufacturing, energy, retail, and healthcare as examples to analyze how Agentic AI enables intelligent upgrades, process automation, and service optimization.

Financial Industry

The financial industry is at the forefront of digital transformation, and Agentic AI has a wide range of uses in this field. Improving operational efficiency is the primary driving force for financial institutions to adopt Agentic AI . For example, in the back-end operations of banks, the introduction of intelligent agents can automatically perform tedious tasks such as reconciliation, cash flow auditing, and abnormal transaction monitoring, greatly reducing the time of manual intervention . Many banks have begun to deploy digital employees to handle long-tail, low-frequency office affairs, realizing the automated experience of " you say, AI does it " . According to statistics, some banking RPA+AI Agent solutions have been implemented in hundreds of scenarios, covering customer marketing, production operations, risk management, research and development, office assistance , etc. Optimizing customer service and experience: In front-end customer interactions, Agentic AI can provide personalized and proactive financial services. For example, the intelligent financial advisor Agent can provide customized investment advice based on customer account balances, investment preferences, and real-time market data ; the bank customer service Agent can not only answer inquiries, but also independently query the customer's financial status, recommend financial management or loan plans, and directly handle related business after the customer agrees . This service model has changed to " service finds people " , that is, the Agent actively discovers needs and provides solutions for customers, instead of waiting for customers to make requests . Ping An Bank's practice shows that by deeply integrating Agent into the enterprise customer system, Agent can perceive the enterprise's operating status in real time, such as changes in inventory turnover or cross-border transaction frequency , and then actively trigger corresponding financial services (for example, recommending supply chain financing when inventory turnover decreases, and optimizing exchange rate hedging solutions when cross-border transactions increase) . This intelligent service that is seamlessly embedded in the customer value chain has greatly improved customer experience and business stickiness.

Enhanced decision-making and risk control: Financial services are highly complex and risk-sensitive. Agentic AI can help financial institutions make better decisions and control risks in dynamic markets by integrating perception, reasoning, and action capabilities . For example, in the field of investment and trading, intelligent trading agents can autonomously monitor market conditions, make real-time trading decisions based on preset strategies or reinforcement learning models, and automatically execute orders and close positions, improving trading speed and accuracy. At the same time, it can timely warn or even suspend trading when the market fluctuates abnormally to control risks. For example, in anti-fraud, agents can scan massive transaction data 24/7 to detect suspicious patterns. Once suspected fraudulent transactions are found, they will be immediately intercepted and prompted for manual review. This continuous and high-speed risk control capability is difficult to achieve manually. Many financial institutions regard fraud detection as one of the high ROI scenarios for the implementation of Agentic AI . In addition, banks can deploy compliance checking agents to automatically track changes in new regulatory rules and review whether internal processes and reports meet requirements, so as to respond quickly to supervision. Similarly, in pre-loan approval, the intelligent approval agent can integrate multi-dimensional data (credit records, social behavior, business data, etc.) to make risk assessments and credit decisions, which not only improves lending efficiency but also reduces bad debt risks.

Business model innovation: Agentic AI may even reshape the model of financial services. Traditional banks allow third parties to access their services (open banking) through open APIs , but this is still a model of " customer systems calling bank services " . The introduction of agents changes the model to " bank services are actively executed by agents " . For example, after a certain enterprise's financial agent is authorized, it can automatically call the APIs of multiple banks to complete the entire process of fund allocation, payment settlement, etc. At this time, the bank no longer provides just an interface, but an active service driven by an agent . To adapt to this change, banks have begun to develop agent- native APIs that can support flexible calls by agents and dynamically adapt to changing business scenarios . Forward-looking banks such as Ping An Bank are building a comprehensive platform of Agent+ workflow + plug-in +RAG knowledge base , combining the capabilities of large models with banking business rules to create a dedicated intelligent cluster in the financial field . Their goal is to achieve a paradigm shift from " people looking for services " to " services looking for people " . This not only improves service efficiency, but may also give rise to new products, such as intelligent advisors that automatically provide treasury management solutions based on the real-time status of the enterprise, or AI stewards that help individual customers automatically optimize asset-liability allocations . It is conceivable that in the future, banks and insurance companies will launch various named AI assistants (investment advisors, insurance claims assistants, etc.) and provide them to customers as part of their products.

In short, in the financial industry, the intelligent upgrade driven by Agentic AI is reflected in: more process automation and cost reduction and efficiency improvement in operations, from passive response to active insight into customer needs in services, real-time prevention and control around the clock in risk management, and exploration of new intelligent service models in business innovation. Of course, the premise of all this is to solve the problem of security and compliance, and ensure that the decisions of AI agents are recognized, safe and controllable within the legal and regulatory framework . While actively embracing Agentic AI , financial institutions need to formulate strict governance and authorization strategies to define clear boundaries of responsibility for intelligent entities. Overall, Agentic AI is expected to enable the financial industry to move further into a new stage of " smart finance " - efficient, personalized, agile and secure.

Government and Public Services

Government departments and public service agencies are facing the challenges of insufficient manpower and diversified service needs. Agentic AI can become a powerful tool to promote the intelligent upgrade of government affairs . Improve administrative efficiency: A large number of repetitive and rule-defined tasks in government affairs can be handled by AI agents, thereby freeing up civil servants to devote themselves to higher-value work. According to feedback from actual applications, some local governments have saved more than 100 hours of work time per month for staff after introducing "AI digital employees " . For example, in the circulation and approval process of official documents, the intelligent agent can automatically complete format review, content extraction, distribution and circulation, and preliminarily formulate approval opinions for reference by leaders based on preset rules. For example, in window units such as human resources and social security and taxation, AI agents can pre-check whether the materials are complete and whether the filling is correct, reducing the time for manual repeated verification. Optimize government services: In the service field for enterprises and the public, Agentic AI can provide 7×24 hours of uninterrupted intelligent services. Typical applications are government intelligent customer service and guide assistants . Traditional government hotlines and lobby consultations require a large number of manpower on duty, and knowledge is not updated in a timely manner. After the introduction of the intelligent agent driven by the big model, residents can consult policies and the progress of handling at any time through websites, WeChat, etc. The AI ​​assistant will instantly query the policy database to give authoritative answers, and even guide the masses to complete the handling process online. If a complex situation cannot be handled, the agent can also generate a summary of the problem and transfer it to a human specialist for follow-up, thereby improving the overall service efficiency and quality. In addition, some advanced cities have launched a one-stop service agent . Users only need to propose goals (such as starting a business, applying for subsidies), and the intelligent agent will handle all related matters on their behalf across departments, truly achieving " one thing at a time " . This requires opening up the data and system permissions of various departments, but once achieved, it will greatly improve the satisfaction of the masses and reduce running back and forth.

Assisted decision-making and governance: Government managers can use Agentic AI to gain insights and simulate decisions . For example, multiple environmental monitoring and traffic AI agents are deployed in urban management to report abnormal indicators in real time and summarize them into a visual situation dashboard for decision makers to refer to. More advanced agents can simulate the execution effects under different parameters for a certain public policy and assist leaders in formulating plans. In emergency management, intelligent agents can integrate meteorological, water conservancy, and geographic information to automatically judge the development trend of disasters and propose response measures. For macroeconomic decisions, agents can collect and analyze massive economic data and corporate feedback to discover potential problems and opportunities. These are all difficult to do in a timely manner through traditional manual research. By allowing AI to participate in information processing and solution generation, the government decision-making process will be more data-driven and scientific.

Process automation and collaboration: The lengthy processes and difficulty in departmental collaboration within the government have always been pain points. Agentic AI helps to break down departmental barriers and realize automatic process connection. For example, the approval of a construction project involves multiple departments such as development and reform, planning, and environmental protection. In the past, enterprises had to run one by one, but now there can be an approval agent to act as a project manager, track the progress of the project, automatically convert the output of the previous link into the materials required for the next link and submit them to the relevant departments, remind the relevant personnel to approve, and urge the process to be completed on time. If a link is stuck, the agent can also push reminders to the superiors. This intelligent workflow can significantly shorten the project approval cycle. For example, in government procurement bidding, AI agents can automatically collect procurement needs from various departments and summarize and publish bidding notices; after the bidding deadline, they can automatically organize evaluation experts to score (even assist experts in checking the scoring basis), generate evaluation reports for approval, and realize the digitalization of the entire bidding process . The information exchange between departments is smoothly connected through the agent , reducing human delays and communication costs.

Decision-making execution and supervision: Agentic AI can also play a role in policy execution and supervision. For example, tax audit agents can automatically detect abnormal invoices and tax records through cross-database data analysis, and generate early warning lists for auditors to focus on. Environmental inspection agents combine drone and IoT sensor data to automatically detect illegal emissions or pollution incidents and notify law enforcement personnel. Market supervision agents inspect e-commerce platform data every day, and find clues such as abnormally high prices and false propaganda to lock evidence in time. It can be foreseen that various law enforcement auxiliary agents will emerge in an endless stream, making supervision and law enforcement more proactive and accurate. Agents can also contribute to the evaluation of policy implementation effects: through public opinion agents, social media and news are analyzed, and sentiment and hot spots of public feedback on a certain policy are analyzed to help the government understand public opinion; through economic data agents , changes in policy KPI indicators are tracked to evaluate policy effectiveness. This is equivalent to establishing an intelligent monitoring mechanism for policy feedback , making governance more closed-loop.

In general, Agentic AI will bring the government into the era of " Digital Governance 2.0 " : internal operations will be more efficient (automated processes), external services will be more convenient (smart assistants will respond at any time), decisions will be more scientific ( AI -assisted analysis), and supervision will be smarter (proactive problem discovery). Of course, government departments must also be extremely cautious when applying Agentic AI to ensure fairness, justice, and security. For example, it is necessary to prevent algorithmic discrimination and ensure that AI decisions are in line with policy intent; AI can only assist in important matters and cannot replace human decision-making; at the same time, data security and privacy protection measures should be established to win the public's trust in government AI applications. If handled properly, Agentic AI will become an " accelerator " for the construction of digital government , providing higher quality public services at a lower cost.

Manufacturing Industry

The manufacturing industry is undergoing an upgrade from automation to intelligence, and Agentic AI has injected new momentum into " Industry 4.0" . In the intelligent manufacturing scenario, the introduction of autonomous intelligent agents can optimize production, maintenance, supply chain and other links, and improve efficiency and flexibility.

Production process optimization: Agentic AI can act as the " brain " of the factory , autonomously managing and adjusting the production process. In highly automated factories, traditional control systems such as PLCs execute preset assembly line programs, but after adding agents , the system can dynamically adjust production parameters and rhythms based on real-time data . For example, a production scheduling agent monitors the operating status, order priority, and equipment load of each production line in real time, and intelligently schedules and dispatches production: when a production line encounters a bottleneck or failure, the agent automatically reallocates tasks to other production lines to avoid downtime . Or dynamically adjust the production order of each product according to the order delivery date to maximize capacity utilization. Such autonomous scheduling makes production more flexible without waiting for manual decisions. Similarly, in process manufacturing (such as chemicals and pharmaceuticals), agents can automatically fine-tune control variables such as temperature, pressure, and ingredients based on sensor feedback to maintain the optimal state of the process and improve yield and quality consistency.

Predictive maintenance: Equipment maintenance is an important part of the manufacturing industry, and Agentic AI takes predictive maintenance to a higher level. Traditional predictive maintenance relies on sensor data + threshold alarms, or manual regular inspections. After the introduction of AI Agent , the system can continuously learn the normal mode of equipment operation and actively detect abnormal signs . The maintenance agent collects multi-source data such as vibration, temperature, and current, issues an early warning at the slightest abnormality, and can analyze and determine the possible fault location and cause . The agent can then automatically arrange maintenance plans: for example, insert a certain equipment shutdown maintenance into the optimal time of the production plan, and notify technicians in advance to prepare the required parts. This autonomous maintenance reduces the occurrence of sudden downtime and major failures, and improves the overall availability of equipment. In addition, the maintenance agent can also manage the inventory of spare parts, predict possible future replacement needs based on the health status of the equipment, and purchase spare parts in a timely manner to avoid downtime due to lack of parts.

Quality control and process improvement: In the past, quality inspection was mostly carried out after production, and problem detection was delayed and wasteful. Intelligent quality inspection agents can be integrated into production lines to detect product quality and adjust processes in real time. For example, computer vision agents detect dimensional defects of each part on the production line. If the deviation is found to continue to increase, the process agent is immediately notified to adjust the machine calibration to prevent problems before they occur. Through this closed-loop control, defective products are killed in the production line. At the same time, Agentic AI can analyze quality data across batches and discover hidden factors that affect quality. For example, it can identify the association between a specific material batch and the defective rate, or the impact of certain environmental conditions (temperature and humidity) on product performance, thereby suggesting process optimization. Tesla's Berlin factory practice shows that after deploying the Agentic system, the production cycle of each Model Y was shortened by 18% , and the quality defect rate dropped to 0.7% (Note: This data shows that Agentic AI has significantly improved manufacturing performance through production optimization and quality improvement). Although further verification is needed, this example foreshadows the great potential of intelligent agents in manufacturing optimization.

Supply chain and logistics: The supply chain of the manufacturing industry is complex and large, and Agentic AI helps to achieve intelligent coordination of the supply chain. A supply chain agent can track the data of the entire process from raw material procurement, inventory to logistics distribution, and autonomously coordinate supply and demand. For example, when the production agent realizes that the inventory of a key part will be insufficient, it will immediately notify the procurement agent to find the source, automatically place an order for replenishment by comparing the price and delivery date, and even contact the backup supplier to ensure continuous supply when the supplier is delayed. The inventory management agent dynamically optimizes the inventory level according to the production plan and sales forecast to reduce backlogs and out-of-stock. In terms of warehousing and internal logistics, the scheduling agent can control AGV ( automatic guided vehicles ) and robots to realize unmanned handling and distribution of materials in the workshop, ensuring that the production line obtains the required parts in time. For finished product logistics, the agent can select the best shipping warehouse and route based on order distribution and real-time transportation information, reducing logistics costs and shortening delivery time. In short, Agentic AI allows the supply chain to achieve end-to-end automatic adjustment , and the supply, production, and sales links are closely connected to adapt to the rapidly changing market demand .

Collaborative and flexible production: The shift from large-scale to customized small-batch production in the manufacturing industry requires extremely high flexibility and responsiveness. Agentic AI can act as a coordinator between different production units . In multi-factory collaborative manufacturing, the production agents deployed in each factory can communicate with each other and support each other's production tasks when the load is uneven. For order-driven production, AI agents can automatically adjust the production line configuration (such as 3D printing parameters, CNC processing programs) according to order specifications, achieving highly flexible switching instead of relying on manual resetting. Agents can also connect the design department and the production department: when the design changes, the agent automatically evaluates the impact on the existing production plan and coordinates the update of the process flow and process sequence. These reduce the intermediate manual communication links and enable the flexible manufacturing system to have self-regulation capabilities.

In short, the application of Agentic AI in the manufacturing industry can comprehensively improve the coordinated optimization of the elements of " people, machines, materials, methods, and environment " : people (decision-making) - reduce human intervention in decision-making and allow intelligent entities to optimize in real time; machines (equipment) - realize autonomous management and maintenance of equipment; materials (materials) - real-time matching of supply and demand in the supply chain; methods (processes) - dynamically adjust processes to ensure quality; environment (environment) - intelligent coordination of multiple factories and multiple links. Research shows that Agentic AI can optimize manufacturing processes by autonomously managing tasks such as predictive maintenance, supply chain logistics, quality control, and process adjustments . As the technology matures, we are expected to see the emergence of " self-smart factories " : various types of intelligent agents are spread throughout the workshop , working together like digital workers, and the factory can manage and operate autonomously 24 hours a day, pushing efficiency and quality to new heights.

Energy Industry

The energy industry includes electricity, oil and gas, new energy and other fields. Agentic AI can help the energy system develop in a smarter, more efficient and reliable direction.

Smart grid management: The power system is becoming more and more complex: distributed energy, large-scale energy storage access, electric vehicle charging load, etc. bring challenges. AI Agent plays the role of dispatcher and guard in the smart grid . For example, the load balancing agent monitors the power load and electricity price of each region in real time, automatically adjusts the power transmission between regions, and reduces the peak-valley difference; when the overload risk of a certain node increases, it notifies in advance to enable the backup power supply or peak-to-valley filling measures . In the distribution network, the fault detection agent quickly locates the fault section through voltage and current abnormal pattern recognition, and directs the switch disconnection and power supply path switching to minimize the scope of power outage . At the same time, the maintenance dispatch agent automatically notifies the nearest repair personnel and parts warehouse according to the fault location to shorten the repair response time. For the grid connection of new energy, the prediction agent predicts the output of photovoltaic and wind power based on weather and historical data, coordinates the output plan of conventional power sources in advance, and ensures the balance of supply and demand. The collaboration of multiple such agents can elevate the operation of the power grid to a new level of self-adaptation and self-healing , and reduce accidents and waste caused by untimely human dispatch.

Energy production optimization: Agentic AI is also very useful in the field of energy production such as oil and gas . The production of oil and gas fields involves many wellheads and pipelines, which need to be optimized in real time. The production optimization agent can dynamically adjust the parameters of the pumping unit / fracturing pump according to the pressure and production data of each oil well to achieve balanced production and avoid damage to the oil layer due to too fast production or reduced production due to too slow production. In addition, the maintenance agent monitors pipeline pressure and equipment vibration, predicts corrosion or leakage risks, arranges maintenance in advance, and prevents environmental accidents. In refineries and chemical plants, process control agents read thousands of sensor data and autonomously fine-tune reaction conditions so that production is always close to the optimal point while ensuring safety margins. Energy giants have also begun to invest heavily in such autonomous optimization systems: Abu Dhabi National Oil Company ( ADNOC ) is reportedly working with technology companies to integrate highly autonomous Agentic AI into its value chain, aiming to optimize energy production and operations through artificial intelligence . This includes using AI agents to monitor reservoir conditions, optimize drilling paths, and adjust refinery production plans. Through global intelligent optimization, traditional energy extraction and processing processes will become more efficient and economical.

Energy consumption and dispatch: On the energy consumption side, Agentic AI can help achieve sophisticated energy consumption management and interactive demand response . For example, large buildings or factories are equipped with energy optimization agents to collect data from power-consuming equipment in real time and combine it with electricity price information to intelligently control the start and stop of air conditioners, lighting, and production equipment to meet business needs at the lowest cost. When the grid load is high and the price is expensive, the agent temporarily reduces the interruptible load; during off-peak hours, the load is appropriately increased or the energy storage is charged. This proactive demand-side response is completed through seamless software agents, which not only saves energy costs for users, but also helps the grid to operate stably. For thousands of home users, AI agents can learn the energy consumption habits of residents and automatically adjust the air conditioning temperature at home, the charging time of electric vehicles, etc., without affecting the comfort of users. If deployed on a large scale, these " home energy consumption agents" are combined into a virtual power plant that can respond to grid dispatch in a centralized manner. When the grid needs to reduce peak load, thousands of home agents can be coordinated through the cloud to slightly increase the air conditioning temperature by 1 degree and delay the charging of electric vehicles by 1 hour, which can reduce the load equivalent to that of a power plant. In areas where new energy accounts for a high proportion, the consumer-side agent can also adjust the electricity consumption plan according to the output of renewable energy, such as guiding more loads to operate when there is sufficient sunlight, making full use of clean energy. These practices enable the energy system to achieve closed-loop optimization from power generation, transmission and distribution to power consumption, improve overall efficiency and promote clean energy consumption.

New energy operation and maintenance and dispatch: For new energy assets such as wind farms and photovoltaic power stations, Agentic AI can improve the operation and management level. The wind turbine operation and maintenance agent monitors the vibration and power curve of each wind turbine, and remotely adjusts or dispatches personnel for maintenance in a timely manner when abnormalities are found, thereby reducing downtime. The group intelligent agent can also dynamically optimize the angle and output of each wind turbine based on the wind speed distribution of the wind farm, so as to maximize the power of the entire wind farm and avoid disturbances between wind turbines. The cleaning robot agent of the photovoltaic power station determines the best time to clean the panel according to the weather forecast to improve power generation efficiency. From a more macro perspective, the new energy dispatch agent can comprehensively manage multiple energy sources within the region: when the photovoltaic output is excessive, it automatically directs the pumped storage or water electrolysis hydrogen production device to absorb the remaining power; when the wind and light are insufficient, it coordinates to start the gas unit or discharge energy storage equipment to supplement. This realizes the integrated dispatch of all links of source, grid, load and storage, and ensures stable power supply in the highly variable new energy era.

In summary, Agentic AI promotes the energy industry to move towards smart energy and achieve full-link optimization from mining, power generation to transmission and distribution, and consumption. The characteristics of energy systems are huge scale, strong real-time performance, and high safety requirements. The introduction of autonomous intelligent agents can improve their self-regulation and error correction capabilities. It should be noted that the energy industry has extremely high requirements for safety and stability, so when applying Agentic AI, its reliability and controllability must be strictly verified. For example, key scheduling decisions still require human review or the addition of safety constraints to prevent AI from making improper operations in extreme situations. However, it is foreseeable that as trust is established, AI agents will gradually assume more responsibilities for energy scheduling. In the future power control room, dispatchers may be more likely to supervise the work of a group of AI assistants rather than directly control each device. " Unattended " self-optimizing energy systems will become possible, providing society with a continuous safe and efficient energy supply.

Retail Industry

In the consumer-oriented retail industry, Agentic AI can help retail companies build a more intelligent, efficient and personalized business model, achieving a " win-win " experience for merchants and customers .

Store operation automation: In physical retail stores, the introduction of intelligent agents can significantly improve operational efficiency and reduce costs. First, the inventory management agent can monitor the store inventory in real time, and automatically determine the replenishment plan based on sales data and forecasting models. When the inventory of a certain product is close to the lower limit, the agent will send a replenishment request to the warehouse or supplier, and even directly transfer the surplus inventory of neighboring stores in large chains to achieve retail inventory optimization . Secondly, the loss control agent analyzes cash register data and camera monitoring to identify abnormal transactions or potential theft behaviors, and promptly reminds the store manager to intervene, thereby reducing losses and theft . Thirdly, the display and price management agent can automatically adjust the display location of the product in the store as well as the pricing and promotion strategy based on sales conditions and slow-moving product data. For example, when the system finds that a certain product has not been in demand for a long time, the agent can suggest a price reduction promotion or a change of display location; for popular products, it ensures sufficient inventory and optimizes the display. For large supermarkets, the agent can also generate a task list for employees, such as which shelves need to be sorted and replenished, and which products need to change price tags, greatly improving the refinement and timeliness of store management .

Improve customer interaction experience: Agentic AI enables retailers to provide customers with more immediate and personalized services. One important application is the intelligent shopping guide assistant . Online, chatbots have long been used to answer customer inquiries, but Agentic AI allows shopping guides to do more than just answer questions. Instead, they can actively accompany customers throughout the shopping process. For example, when customers browse products on the website, the AI ​​assistant Agent will recommend relevant products and discount combinations in real time based on customer preferences (equivalent to a personal shopping guide), and can answer product details, compare differences, and even provide matching suggestions . If the customer hesitates, the Agent can send a limited-time coupon to facilitate the transaction; if the customer adds the product to the shopping cart but does not check out, the Agent will automatically remind him later and provide a small discount to encourage purchase. This kind of interaction with thousands of people brings higher conversion rates . In offline stores, combined with mobile apps or in-store terminals, customers can also get AI shopping guide services: when customers use their mobile phones to scan products or make requests (such as looking for sugar-free drinks), the Agent will immediately inform them of the relevant information, guide them to the corresponding shelves, and recommend brands they may like based on customer membership data. In this way, even if there are not enough sales staff, customers can enjoy attentive consulting services. At the same time, in terms of after-sales service, Agentic AI supports 24-hour customer service . Customers can consult retailers about order issues and apply for returns and exchanges at any time through voice or chat, and AI customer service will quickly handle routine issues . According to industry summaries, such applications improve employee productivity (automatically handle routine tasks), improve customer experience (timely and personalized responses), and drive sales growth (accurately recommend promotions) . It can be seen that Agent has already demonstrated its value in retail services.

Marketing and precision recommendation: Retail companies have accumulated a large amount of consumer data for a long time, but how to use this data in real time to increase sales is a difficult problem. Agentic AI has great potential in the field of marketing. Marketing campaign agents can analyze members' purchase history, browsing behavior, and factors such as holidays and weather, and automatically generate marketing plans for different segments of the population. For example, push diaper discounts and free shipping activities to customers who often buy baby products; push notifications of new organic food launches to vegetarians. Compared with manual planning, AI agents can mine more fine-grained associations and design highly personalized promotional content, thereby improving marketing hit rate and return rate. In addition, on e-commerce platforms, recommendation system agents continuously learn the interests of each user, dynamically adjust the product recommendation list, and realize the homepage and product sorting of thousands of faces, which greatly improves user stickiness and customer unit price. Some AI agents can even generate creative advertisements and product descriptions. For example, when a new product is launched, the copywriting agent automatically writes advertising slogans and social media posts based on the product selling points and target groups, and then the image generation agent generates matching visual materials and puts them on various channels to realize the automation of marketing content production.

Supply Chain Collaboration and Logistics: End-to-end speed and efficiency of the retail supply chain are critical to profitability. Agentic AI can enhance supply chain collaboration . Replenishment Agents automatically place purchase orders upstream based on store sales and warehouse inventory, and connect with suppliers’ systems to confirm delivery dates. When anomalies occur (such as supplier delays or insufficient production capacity), Agents promptly look for alternative sources of supply or adjust purchase volumes. Transport routing Agents plan optimal routes for delivery trucks, combining real-time traffic and geographic distribution of orders to reduce logistics time and costs . In e-commerce warehouses, intelligent scheduling Agents assign picking tasks to robots or pickers, optimize walking paths for batch picking, and arrange packaging order based on order commitment time and delivery area to achieve optimal delivery time . In addition, customer-facing delivery Agents can automatically negotiate express resources based on the time period and address selected by the customer to achieve service commitments such as scheduled delivery and minute delivery. Through these autonomous collaborations, retail companies can build an agile supply chain that reduces inventory and responds quickly to market demand.

Omnichannel integration: Nowadays, retail is all about online and offline integration and omnichannel operation. Agentic AI helps integrate data and operations from different channels to achieve a consistent customer experience. For example, omnichannel customer service agents uniformly handle customer questions from various channels such as store consultations, website chats, and social media messages, maintain consistent responses, and apply information obtained online (such as a customer has collected a certain product on the App ) to offline services (the in-store shopping guide agent knows to recommend the product to the customer). For another example, the price optimization agent can dynamically adjust the prices of each channel based on the inventory of online e-commerce and offline stores, so that online and offline prices are linked and their respective promotion strategies are considered to avoid conflicts. The inventory agent also coordinates the inventory of each channel. When online orders increase, the agent determines whether the nearest store can deliver faster. If so, it generates a store delivery order and pushes it to the corresponding store for execution. These applications require powerful agent logic to decide the optimal channel so that inventory and sales are seamlessly synchronized . Through Agentic AI , omnichannel retail can truly achieve " one inventory at the front end and one mind at the back end " , which is convenient for consumers and improves the operational efficiency of enterprises.

In summary, Agentic AI is reshaping the retail industry : it provides retailers with automated operations, personalized marketing, and agile supply capabilities, allowing companies to stand out in the fierce competition. For consumers, they will also enjoy a smarter shopping experience, such as an AI shopping assistant that " understands you " and ubiquitous instant services. Of course, the retail industry also needs to pay attention to customer acceptance of AI applications, ensure transparency and protect privacy. For example, does the recommendation make the customer feel offended? Is the use of data authorized? These all need to be properly handled. Overall, in a future retail environment full of agents , consumers may find it difficult to detect the AI ​​figures working silently , but they will actually feel that the service has become faster, more accurate, and more intimate .

Healthcare Industry

The healthcare sector has a huge demand for digital transformation. The application of Agentic AI is expected to improve the efficiency of medical services, assist in medical decision-making, and improve patient experience, but it also needs to be steadily promoted under security supervision. The following discusses the intelligent upgrade scenarios in the medical field.

Medical assistance and diagnosis and treatment support: Agentic AI can be a powerful assistant to medical staff. The electronic medical record assistant Agent can automatically generate medical records when the doctor is consulting : through voice recognition, the doctor-patient conversation is recorded in real time, and the key points such as symptoms, signs, and diagnosis are structured and entered into the medical record system . This reduces the time that doctors spend writing medical records, allowing them to focus on communicating with patients. At the same time, the medical record agent can also automatically prompt the inquiry items that may be missed and give preliminary diagnosis suggestions for the doctor's reference based on the content of the conversation. For medical imaging diagnosis, AI agents can read X- rays, CT , MRI and other films and mark suspicious lesions. A radiology agent can summarize the patient's images and historical records, generate a preliminary diagnosis report, list possible conclusions and basis, and then be reviewed and confirmed by the doctor. This improves diagnostic efficiency , especially in hospitals where there is a shortage of radiologists. In terms of personalized treatment, the treatment optimization agent can track the patient's various examination indicators and automatically adjust the treatment recommendations when new test results come out . For example, the diabetes management agent monitors the patient's blood sugar data. If the blood sugar is high for several consecutive days, the agent reminds the doctor to consider adjusting the drug dosage or diet plan . IBM 's research points out that in the field of healthcare, intelligent agents can monitor patient data, adjust treatment plans in real time based on new test results, and provide feedback to clinicians through chat interfaces . This shows that AI agents can help doctors make continuous and dynamic diagnosis and treatment decision optimization.

Nursing and patient support: Agentic AI can also directly serve patients, providing more continuous and personalized care. A typical scenario is virtual health assistants . For example, " digital health manager " Agents interact with chronic disease patients on a daily basis through mobile phone apps : reminding patients to take medicine on time and measure blood pressure and blood sugar; when patients report symptoms, Agents give lifestyle advice or reminders to adjust medication; if serious abnormalities are detected in patient indicators, Agents immediately recommend medical treatment and notify their doctors to follow up . Meinian Health has already launched such applications. For example, their " Healthy Xiaomei " AI butler can accompany users to complete health management after physical examinations, provide personalized life advice and review reminders . In hospital wards, nursing robot Agents can patrol beds, communicate with patients by voice, answer common questions, give rehabilitation advice, monitor patient calls, and report emergency situations to nurses. This reduces the burden on nurses , especially during night shifts or when nursing staff are insufficient to ensure uninterrupted care for patients. In terms of mental health, AI chat agents can be used as initial screening for psychological counseling, accompany patients to talk and relieve their emotions, and recommend referrals to artificial doctors for identified high-risk patients with depression tendencies, which not only protects patient privacy but also expands service coverage. Personalized support is of great significance in medical care: many patients find it difficult to get continued guidance after leaving the hospital, and AI assistants can act as 24- hour online " family doctors " , helping patients follow doctor's orders and identify problems in a timely manner.

Hospital operation and management: Agentic AI can optimize the operation process of medical institutions and improve resource utilization efficiency. The appointment scheduling agent can automatically arrange diagnosis and surgery time for patients according to the doctor's schedule, operating room availability and patient urgency, so that the appointment waiting time is the shortest and resources are fully utilized. The inpatient bed management agent tracks the bed usage and expected discharge time of each ward in real time, and intelligently arranges beds for newly admitted patients to avoid vacant beds or poor turnover. The medical staff scheduling agent refers to the historical outpatient volume and special days (such as holidays and epidemics) to predict the peak of medical visits, provide suggestions for outpatient and emergency scheduling, and reduce patient waiting. The drug inventory agent predicts the speed of drug consumption based on the prescription situation, and automatically prompts the pharmacy to restock or adjust to ensure a continuous supply of drugs. The surgical supplies agent checks whether the required instruments and items are complete before each operation, and automatically updates the consumption inventory after the operation. All of these can make hospital operations more refined and agile . Some hospitals are also exploring " medical process RPA+AI" solutions. For example, intelligent agents automatically handle large-volume transactions that require compliance with rules, such as medical insurance settlement review and medical record coding, thereby reducing repetitive work for administrative staff.

Medical research and new drug development: Agentic AI is also expected to accelerate innovation in medical research. Research assistant agents can read massive amounts of medical literature and databases, helping researchers find relevant research and extract important information in a short period of time. For example, an oncology research agent can track new papers and clinical trial results every day, providing doctors with the latest evidence support for treatment plans. In the field of drug discovery, AI agents can be used for virtual screening and experimental design . A compound discovery agent can automatically screen molecules with certain biological activities in the chemical space and use generative models to design new candidate drugs . For example, the research team developed an agentic system such as ChemCrow to discover new compounds . At the same time, the experimental assistant agent recommends the most likely experimental combination based on model predictions, optimizes experimental plans, and reduces research and development time. In addition, multimodal AI (such as Nvidia 's medical agent architecture ) can integrate imaging, genetic, and clinical data for reasoning, providing cross-domain insights for scientific research. In the long run, Agentic AI may become the "AI experimenter " of every medical research team , taking on the heavy tasks of data analysis and hypothesis generation, allowing researchers to focus on the creative aspects.

It is worth noting that medical care is a high-risk field where human lives are at stake, and the adoption of Agentic AI must be done with extreme caution. Safety, accuracy, and ethics are the key words. Medical AI agents can only be used with sufficient verification. For example, diagnostic suggestion agents need to undergo large-scale clinical testing to ensure that critical diseases are not missed or misdiagnosed. At the same time, AI cannot completely replace doctors' decisions, but should play an auxiliary role. The regulatory authorities are also accelerating the formulation of standards for medical AI , such as the registration and approval requirements for AI medical devices and the responsibility identification framework . Data privacy is also a focus, and the use of patient data and the training of agents must comply with regulations and ethical reviews. Despite many challenges, medical practitioners generally believe that AI (especially Agentic AI ) will have a transformative impact in the future . For example, a securities research predicts that by 2025 , AI will have a transformative impact in all three major medical fields, with intelligent agents (Agentic AI) listed as the first . In short, Agentic AI will drive medical care towards the direction of " smart medical care " , that is, a service model that is patient-centric, data-driven decision-making, automates trivial tasks, and connects doctors and patients more closely. If technology and regulation are dual driving forces, we have reason to expect a more efficient and warmer medical service system to arrive soon.

 ICT vendors and industry users’ response strategies

The development of Agentic AI not only affects technology and industry applications, but also ICT vendors and industry users who play different roles must formulate corresponding strategies to adapt to this trend. The following discusses how six types of ICT vendors (infrastructure, data, platform, application, service, security) and industry users should respond.

Infrastructure vendor strategies

Infrastructure vendors (including chip, hardware server, cloud infrastructure and network equipment providers) stand at the bottom of the Agentic AI wave and need to plan computing power and architecture in advance to support the large-scale implementation of Agentic AI .

• Improving computing power supply and efficiency: Facing the surge in Agentic AI inference load, chip manufacturers should accelerate the launch of AI accelerators and processors optimized for inference . For example, they should provide low-power, high-concurrency NPU/GPU to meet the needs of massive agents for online inference at the same time. Basic hardware needs to provide higher AI computing power per unit energy consumption to alleviate the pressure on electricity from large-scale deployment of AI computing power.Manufacturers can invest in new process technologies and chip architecture innovations (such as brain-like computing and integrated storage and computing) to break through energy efficiency bottlenecks. At the same time, large cloud providers should expand data centers and even consider investing in renewable energy or building their own power stations like Microsoft and Google., ensuring sufficient and stable computing power supply in the future. This advanced investment will establish their leading position in the AI ​​infrastructure market.

  
  

• Build an Agentic- friendly infrastructure platform: Cloud infrastructure needs to evolve from traditional IaaS to managed services for Agents . Manufacturers can develop an "Agent runtime " environment that allows enterprises to easily deploy and monitor their AI Agents . For example, container / function services are provided that are specially optimized for intelligent agents with long life cycles that need to call external APIs ; Agent state persistence and high-speed caching are supported to improve the efficiency of Agent continuous reasoning. A scheduling system should also be provided to intelligently allocate computing resources to a large number of Agent instances to ensure that mission-critical Agents have sufficient computing power and low-latency operation, while low-priority Agents can reduce frequency or suspend when resources are tight. This is similar to cloud-native elastic scaling, but requires more sophisticated resource scheduling based on the importance and real-time requirements of the Agent . In addition, the infrastructure should have built-in Agent monitoring and security isolation modules so that enterprises can safely deploy and run AI agents on it.

  
  

• Strengthen edge and IoT layout: Many Agentic AI applications occur at the edge (such as industrial sites and unmanned equipment), and infrastructure vendors should provide cloud - edge - end integrated solutions. Strategies include launching edge AI hardware (edge ​​computing gateways, AI cameras, etc.) to support on-site agent deployment; ensuring coordinated communication between edge agents and the cloud through network devices such as 5G/ private networks; adding unified orchestration and update functions for edge agents to the cloud management platform to achieve collaboration between the center and edge agents . For example, providing an IoT platform can distribute and manage the software versions and configurations of thousands of edge agents to achieve OTA upgrades. By seizing edge scenarios, infrastructure vendors can consolidate their position in the IoT and AI integration market.

  
  

• Open cooperation to build an ecosystem: Since Agentic AI involves complex software and hardware collaboration, infrastructure companies should actively cooperate with upper-level AI frameworks and industry ISVs to create optimized end-to-end solutions. For example, chip companies cooperate with AI framework developers to adapt so that popular Agent frameworks can perform best on their hardware; server manufacturers work with database and storage software companies to optimize the IO performance of Agents when accessing knowledge bases ; network equipment manufacturers cooperate with cloud vendors to provide secure connections and acceleration channels from the cloud to the edge to avoid network bottlenecks when Agents call external APIs . This kind of ecological cooperation helps to launch a reference architecture, so that customers know how to build an Agentic AI system based on the vendor's infrastructure . For customers, the reference architecture reduces the complexity of integration; for vendors, it can expand the influence of their own technology stack in the Agentic era.

  
  

In short, the core of the infrastructure vendor's strategy is to provide a solid and efficient " runway " so that thousands of AI Agents can run safely on it. If they can take the lead in computing power supply, architecture optimization, edge coverage and ecology, these vendors will occupy a key position in the Agentic AI ecosystem and drive new growth. On the contrary, if they are not well prepared, they may be abandoned by customers due to computing power bottlenecks or architectural incompatibility. In the next few years, grasping the pulse of Agentic AI 's demand for infrastructure will determine the success or failure of traditional hardware vendors in the new AI era.