2025智能体自动化权威指南

Agents think.Robots do.People lead. Contents IntroductionCore definitionsWhy agentic automation?The agentic approachThe business valueThe backbone: OrchestrationThe foundation: Secure integration and governanceAn exampleThe differentiator: Controlled agencyThe first innovationsThe customer viewThe path forward: Think big, start smallAddressing industry and organizational concernsConclusion3456891011121314151617 IIntroduction In the modern digital landscape, enterprises have become increasinglycomplex with reliance on an extensive assortment of applications andsystems. It’s estimated that a typical enterprise utilizes more than175 different applications and systems to execute their processes,which presents significant challenges in coordination, data management,and operational efficiency. As these dynamic challenges continue toevolve, so have the solutions designed tomanage and address them. One field wherethis evolution is particularly visible isautomation. Over the years, businesses haveincreasingly embraced automation tostreamline operations, reduce costs, andimprove customer satisfaction. This white paper goesbeyond introducing agenticautomation. It provides acomprehensive roadmaptowards its successfuladoption, integration, andscaling, exploring itsweighty benefits andsignificant transformativepotential for enterprises.The paper is a resoundingcall to action for businessesto seize upon agenticautomation, marking a newevolution of digitaltransformation. However, early automation methods weren’talways as efficient in operating on unstructureddata and managing complex tasks that needintelligent decisionmaking. This has promptedthe quest for better, more robust, andcontext-aware automation solutions which,in turn, has resulted in the advent of agenticautomation, a revolutionary leap in the worldof enterprise automation. Agentic automation represents a new era inwhich agents imbued with artificial intelligence(AI) form the core of automated operations.These AI agents exceed mere tools; they arecollaborators involved in making decisions,actively learning, and working alongside peopleand robots to achieve enterprise objectives. Core definitions AGENTICAI Agentic AI refers to artificial intelligence agents bestowed with cognitiveabilities such as learning, problem-solving, and decision-making, whichcontinually adapt and evolve to enhance their performance inautomating tasks and executing complex operations. AGENTICAUTOMATION Agentic automation is an advanced strategy in intelligent enterpriseautomation that integrates traditional RPA, Artificial Intelligence, andhuman decision-making capabilities, fostering an ecosystem wherepeople, robots, and AI agents work synergistically to optimizeoperational processes, driving efficiency and productivity. AGENTICORCHESTRATION Agentic orchestration serves as the guiding value proposition of agenticautomation, effectively managing and assigning tasks anresponsibilities among people, robots, and AI agents depending on theircapabilities, acting as a conductor in an automated symphony, ensuringoperations are smooth, efficient, and aligned with the business’sstrategic outcomes. Why agenticautomation? The successful narrative of enterprise automation to date has beenlargely shaped by robotic process automation (RPA)—a technology thatgreatly streamlines structured and repetitive tasks. While RPA hasbrought considerable progress, modern businesses are continuouslyevolving and facing more complex and unstructured problems thatrequire more sophisticated approaches. For instance, traditional RPA needs moresupport when faced with contextual decision-making, adaptation to real-time changes, andhandling unstructured data. How does abusiness automate tasks that involve customerinteractions with various potential outcomes,requiring decisions made based on contextrather than fixed rules? Also, how canorganizations automate operations when theenvironment is perpetually changing, and real-time adjustments are necessary? These are among the challenges the agenticautomation paradigm addresses. By integratingAI agents—autonomous, intelligent entities—agentic automation supplements traditionalautomation capabilities. It enables businessesto automate complex, unstructured workflowsand infuses cognitive capabilities into theautomation processes. In essence, AI agentsgo beyond executing pre-programmedinstructions; they bring decision-makingprowess to the automation mix. The agentic approach The agentic approach to automation builds on the foundation of RPA andtaking it a step further by embedding advanced Artificial Intelligence (AI)capabilities into the equation. Central to this approach are the AI agentswhich come with cognitive abilities such as learning, reasoning, perceiving,hypothesizing, and problem-solving in a way that directly supportsadvancements in machine learning and data analytics. Agents think.Robots do.People lead. In this approach, complex tasks which require