终极代理型AI指南

The UltimateAgentic AI Guide 100+ Real World Use Cases ofAgentic AI for the Enterprise Enterprise AI adoption is skyrocketing. Just a fewyears ago, only 48% of organizations wereexperimenting with AI. Today, that figurehasjumped to 72% — and it’s only growing. However, we’re now moving into a new phase ofAI — one driven by agentic AI. Unlike non-agentic AI models that rely onpredefined rules, agentic AI systems can reason,learn,and make decisions on their own withouthuman intervention. Why? Because AI delivers efficiency, automation,and scale like never before. As more companiesrecognize the long-term value of AI, the number ofreal-world use cases in production has surged. Why does this matter for your organization By 2028, 33% of enterprise software applications will incorporate agentic AI tohelp manage complex tasks and workflows. That’s because agentic AI provides the flexibility, efficiency, and scalability necessaryto achieve these goals while helping your business stay agile to accommodate shiftingneeds and operational demands What is agentic AI? In this guide, we’ll break down: Agentic AI refers to AI systems that canautonomously pursue complex goals, makedecisions, and execute multi-step processes —all without explicit human supervision or interven-tion. These systems can plan, adapt, andtake action, much like a human employee. •What agentic AI is and how it differs fromnon-agentic AI•100+ real-world use-cases showing howenterprises are already leveraging agentic AI•Practical examples across industries —including HR, IT, finance, sales, customerservice, and more•How to get started with agentic AI in yourorganization today How does agentic AI work? Agentic AI can plan, reason, take action, and learn toadjust its behavior over time. In this way, AI agentsoperate through a cyclicalperception-reasoning-action loop process: If you’re looking for ways to automatedecision-making, streamline workflows, and unlocknew levels of productivity —this guide is for you. •Perception: They utilize sensors (such as APIs,cameras, or data feeds) to gatherenvironmental information •Reasoning: They employ reasoning (oftenpowered by Large Language Models or LLMs)to process this data and make decisions•Action: They use actuators or software actionsto execute those decisions. How is agentic AI different? Most AI models today rely on humans to promptthem for every task. Agentic AI, on the other hand,sets its own goals and follows through. It cantroubleshoot technical issues, automate workflows,and even handle real-world tasks. Consider a real-world example of a bank’s AIcustomer service assistant that operatesautonomously through aperception-reasoning-action loop: •Autonomy:Unlike non-agentic AI that oftenrelies on specific, pre-programmed instructions,agentic AI can make independent decisions.Agents can perceive their environment and takeactions to achieve their goals without constanthuman intervention. •Perception (Data Collection): The chatbot usesNatural Language Processing (NLP)to understand customer queries like “Whatis my account balance?” or “How do I resetmy password?” •For example, a chatbot might respond tocustomer inquiries, but it lacks the abilityto take independent action beyondpredefined responses. •Reasoning (Data Processing and DecisionMaking): Machine Learning models poweredby Large Language Models (LLMs) interpret thequery, retrieve relevant information from thebank’s database, and determine thebest response. •Goal-Oriented Behavior:AI agents aredesigned to pursue specific objectives.They can plan, execute, and adapt their actionsto reach those goals, demonstrating a level of •Action (Executing Decisions): The AI assistantprovides the response in natural language, suchas “Your account balance is $1,234.56” or offerspassword reset instructions. Each of these critical steps enables an AI agent toautonomously interact with customers, deliveringtimely and accurate assistance. Because AI agents can effectively perceive, reason,and act in complex environments, they can handlea huge range of dynamic tasks across diverseindustries, departments, and use cases. That’s whywe’ve included case study examples to illustrate howcompanies can use Moveworks and other agentic AItools to achieve these remarkable results. Moveworks supports enterprise-wide agenticuse cases proactive problem-solving. •Perception and Action:AI agents interact withtheir environment through sensors andactuators, enabling them to perceive andrespond to changes. Creating and implementing your own AI agentsoffers your business exceptional customization inautomating and optimizing operations. Moveworks enables you to swiftly create anddeploy AI agents with minimal coding required.The platform is designed to help you: •Reasoning and Learning:They possess theability to reason, plan, and learn from theirexperiences, allowing them to improve theirperformance over time. •Automate repetitive tasks like IT ticketresolution,