University of Kwazulu-Natal

Real World Use Cases For Agentic Ai Computerworld

Bonisiwe Shabane
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real world use cases for agentic ai computerworld

Remember those simple days of yore, when generative AI meant sending a question to an AI model and getting an answer in return? You might add in a vector database to provide some context for the question and some guardrails for safety and security. That sounded hard at the time, but in retrospect it was a walk in the park. Today, the trending technology is agentic AI systems. Instead of a chatbot, a vector database, and a guardrail, you now have an endless selection of datasets, large and small models of all kinds running in all possible locations, and instead of a... Or probabilistic workflow, as the case may be.

There are new protocols connecting data and agents, new protocols connecting agents to other agents, and orchestration frameworks to chain it all together. With all this complexity, you might think that companies would be slow to adopt agentic AI. You’d be very wrong. In a Cloudera survey of 1,500 enterprise IT leaders in 14 countries released in mid-April, 57% of respondents say they’ve already implemented AI agents, and 96% say that they plan to expand their use... Aaron Ricadela | Senior Writer | May 21, 2025 Software assistants called AI agents have the ability to automate computer users’ repetitive tasks and respond to routine customer and employee questions.

Unlike prior generations of helpers built into business applications, which relied on pre-coded rules or keyword triggers, AI agents take advantage of large language models’ predictive power and ability to communicate with users in... Agents can help organizations realize a return on their AI initiatives by reducing errors and streamlining processes for tasks such as researching customers for a deal, writing job postings and offer letters, and evaluating... They can also help make individual contributors more productive and keep processes moving ahead, even overnight. Agents can look for information across different tools, taking users’ roles and other context into account and staying topical by pulling information from business documents to supplement the underlying LLMs’ training data. Read on to learn about top enterprise use cases for AI agents that your company may be able to put into practice. AI agents are software assistants, powered by generative AI, that mediate between pretrained LLMs and computer users to carry out a wide range of multistep tasks inside software applications or on the web.

Instead of responding to preprogrammed rules or keywords like previous iterations of digital helpers, AI-powered agents can predict the next logical step in a series of tasks and present relevant information or complete steps... Businesses are building and deploying AI agents to assist with recruiting, explain pay and benefits to employees, field customer inquiries, work on sales deals, make financial projections, and undertake equipment repairs. In healthcare, medical practices and hospitals are using agents to help with scheduling and improve automated note-taking and documentation during patient visits, among other use cases. Agentic AI use cases are different from RPA and other traditional automation. They play their actions autonomously, adapt, and achieve specific goals with less human intervention. The automation is not just limited to one area but spread across various fields, including Customer Experience (CX), sales and marketing, Human Resources (HR), healthcare, finance, and more.

These AI agents can process orders, identify technical issues, nurture leads and complete many other tasks in diverse industries. Agentic AI is bringing autonomy, adaptability, and real-time decision-making into the core of businesses. AI agents can now autonomously do complicated tasks, learn from past data, and continuously evolve their performance without human supervision in a variety of settings, including production floors and customer service desks. This blog will highlight the top 35 agentic AI use cases with some real-world examples across industries like healthcare, finance, retail, logistics, and more. Explore how top business managers are making the best use of agentic AI. Discover how the autonomous decision-making skills of agentic AI have changed commercial operations.

These 35 compelling application cases demonstrate its practical influence across several industries. Agentic AI in Customer Experience (CX) is an area where you can automate regular activities that need lots of attention. This intelligent can act independently, learn, and offer 24/7 support to your clients. Create responsive web apps that excel across all platforms User centric mobile app development services that help you scale. Innovation-driven enterprise services to help you achieve more efficiency and cost savings

Insights for building and maintaining your software projects Our publications for the connected software ecosystem Artificial Intelligence is no longer a futuristic concept – it’s embedded in the way we search, shop, communicate, and work. At the core of this transformation are AI agents: software-powered systems capable of perceiving their environment, making decisions, and taking action. These autonomous AI systems-sometimes called intelligent software agents-are transforming industries with real-world use cases in 2025. Whether they are scheduling appointments, guiding a customer through troubleshooting, or predicting market trends, AI agents are designed to handle tasks traditionally requiring human judgment and effort.

Their versatility makes them a valuable tool for both individuals and organizations, and their adoption is growing at a rapid pace. In this guide, we’ll explore more than 20 real-world AI agent examples across consumer, business, and industry-specific contexts. Along the way, we’ll look at how they work, why they matter, and where the technology is headed next. An AI agent is more than just an app or a chatbot. It’s a digital entity that can sense, think, and act – often without ongoing human supervision. Unlike static programs, AI agents are adaptive and capable of learning from new information.

Most AI agents operate using three key capabilities: You’ve heard the AI buzz a thousand times. Productivity, efficiency, automation—it all sounds exciting. But look closer, and the numbers tell an interesting story. “According to PwC, only 66% companies have adopted agentic AI, and reported higher productivity; that still means a third of them haven’t cracked the code. This is why real-world examples of Agentic AI matter.

They show us what’s working, where the impact is visible, and how businesses are moving from hype to measurable results. Across industries, these examples aren’t just case studies—they’re blueprints. From supply chain and logistics optimization to hyper-personalized customer journeys, we have agentic AI examples in action, demonstrating how organizations are leveraging AI to act, decide, and deliver at scale. If you’re looking to see how the world is putting agentic AI to work, let’s explore the stories that are shaping the future. Agentic AI is actively changing how organizations solve problems, make decisions, and deliver results. Curious how this looks in practice?

Here are some compelling examples of Agentic AI across industries. The landscape of artificial intelligence is rapidly evolving, with Agentic AI emerging as a truly transformative force in 2025. Moving beyond mere automation, agentic AI systems are designed to perceive, reason, plan, and act autonomously, often engaging in multi-step problem-solving without constant human oversight. These intelligent agents are not just answering questions; they’re asking, “What should I do next?” and executing complex workflows. This paradigm shift is delivering unprecedented efficiency, accuracy, and innovation across a multitude of industries. From streamlining intricate operations to enhancing decision-making, the use cases for agentic AI are becoming increasingly sophisticated and impactful.

This blog delves into the top real-world applications of agentic AI this year, exploring its profound influence on sectors like Healthcare, Finance, and Logistics, among others. At its core, agentic AI represents a significant leap from traditional AI and even generative AI. While generative AI excels at creating content (text, images, code), agentic AI focuses on intelligent action and goal achievement. It’s about building systems that can: This makes agentic AI particularly powerful for tasks that are dynamic, require complex decision-making, and benefit from real-time adaptation. The future of enterprise AI lies heavily in these autonomous AI systems, capable of orchestrating sophisticated processes.

Healthcare is a prime beneficiary of agentic AI, with use cases for agentic AI spanning from personalized treatment to administrative automation.

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