11 real agent AI examples and use cases

11 agentic AI examples and use cases

AI experts and enterprise leaders see agentic AI delivering value across diverse business functions and industries by streamlining workflows, improving decision-making, and automating complex tasks. Here are 11 examples that show its potential to transform IT operations and change how work is done.

1. Risk reduction and security

Agentic AI can help with enterprise security operations and risk mitigation efforts by orchestrating the components of those activities, says IEEE Fellow Karen Panetta, professor of electrical and computer engineering at Tufts University and dean of graduate education for the Tufts School of Engineering.

For example, AI agents in a security operations center can proactively search for new and emerging threats, investigate anomalies, and automatically take corrective action without human intervention. Likewise, AI agents in risk management can look for unusual activity, examine those patterns to determine if they are fraudulent, and automatically respond as needed, Panetta said.

2. Supply chains and logistics

Agentic AI is also useful in the supply chain and logistics field, where orchestrating multiple tasks is the norm, Panetta said. For example, if a drought in a growing region affects the availability and cost of products, supply chain employees will typically need to check available supplies in other regions, confirm prices, reconfigure supply and distribution routes, and find alternative sources of products.

Historically, workers have used technology to handle much, but not all, of that work. Now, agent AI can orchestrate the entire workflow, Panetta said. Supply chain workers can input the desired outcome—for example, finding and delivering the required amount of supplies at the lowest cost or with the fastest delivery—and expect the system to not only identify how to do it, but automatically initiate actions to make it a reality.

3. Call centers

As of early 2025, agent AI is already operating “at scale” in call centers, where it builds on the improvements and efficiencies that traditional AI has brought, said Stuart Brown, a partner and digital business leader at consultancy Guidehouse.

AI agents in call centers orchestrate intelligence and automation across the multiple activities involved in serving customers, Brown explained. An agent can simultaneously analyze customer sentiment, review order history, access company policies, and respond to customer needs based on those elements.

4. Customer Service Improvement

Agent AI can also improve customer service in general, not just in call centers, Brown said.

AI agents can help human employees “get the answer faster and serve the customer faster,” he said. AI agents’ role as a support tool can help ensure that all employees, regardless of skill or experience, deliver a consistently high level of service to customers.

Furthermore, agent AI can proactively serve customers at a level that human employees or even traditional AI generally cannot, Brown said. For example, a utility company could use agentic AI to identify customers who will receive unusually high bills; contact them with that information; provide specific, accurate and personalized information about why their bills are so high; and suggest ways to lower their bills in the future.

5. Knowledge recovery

Agentic AI improves knowledge retrieval by accessing information and acting on insights. For example, an agentic AI chatbot can access a knowledge base, answer user queries and even perform next-best actions, Goodwyn said.

To illustrate, he pointed to the example of IT help desk operations. While previous generation help desk chatbots could answer specific, well-defined user questions, agentic AI goes deeper: analyzing issues, offering options, narrowing down information and even implementing recommended solutions. If the agent is unable to solve the problem automatically, the agent can investigate the problem and send it along with relevant information to a human agent, so that the user does not have to repeat all the details.

6. Multimedia creation

While generative AI can produce text, images and video, agentic AI takes it a step further. Tell an agent to develop a multimedia report, Goodwyn said, and it will delegate subtasks such as research, text generation, image selection and design to other AI systems, producing a more refined and complete final product.

This use case illustrates agentic AI as an orchestrator of AI capabilities, rather than a narrow, single-function technology, he added.

7. Scientific and material discovery

Agentic AI is showing transformative capabilities in areas such as drug discovery and new materials creation, Panetta said. Of course, other technologies—including machine learning and non-agent AI—have been used in these areas for decades, but agent AI operates at a much higher level.

“[Agentic AI] is smart enough to say: ‘This is what I know, and based on this material and [the characteristics the user is seeking] and my exploration, here is the new material or combination,” Panetta said.

Moreover, agentic AI can go beyond developing the recipe for a new compound, she added. It can also identify the optimal suppliers based on priorities such as cost or timing, and even order necessary materials.

8. Health care operations

Agentic AI’s ability to make context-aware decisions and act without constant human intervention applies to the patient experience, from diagnosis to personalized treatment. On the back end, agent AI optimizes tasks such as appointment scheduling, insurance claim processing and regulatory compliance.

Researchers at Mass General Brigham have developed a high-performance agentic AI system that involves multi-note summarization and multi-step reasoning to classify and grade cognitive impairment using unstructured clinical notes from the hospital’s electronic health records.

9. Defense and military logistics

Goodwyn pointed to agent AI’s use in defence, where it could be used for logistics planning. Consider a highly complex military task involving the movement of materials, equipment, and troops using multiple modes of transportation over varying distances.

Agentic AI is in a pilot phase in such areas, Goodwyn said. He emphasized that in this context, AI agents are used to orchestrate complex goals, augmenting human judgment rather than replacing it.

10. Manufacture

Manufacturing is another sector that showcases agentic AI’s potential, Brown said.

AI technology can make decisions and take autonomous actions in long workflows involving multiple functions and IT systems. An agentic AI workflow can span from procurement to manufacturing, connecting to IT systems that power multiple components and using narrow AI models to complete subtasks.

In such a case, Brown explained, the agent can perform a complex, multi-step workflow:

Recognize that the necessary materials are running low. Flag that the material is not available from the regular supplier. Find and order from alternative suppliers who can ship the material to the manufacturer within a specified price range and time frame. Fill out the necessary forms. Enter required data into the appropriate digital systems. Reconfigure the factory floor and production schedules to meet set deadlines.

“It used to be done by humans,” Brown said. “Now it can all be done with agentic AI.”

However, he added, it’s a best practice to keep people in the loop and determine control points based on a responsible AI framework.

11. Utilities

Agentic AI is also already being used in the utility industry, Brown said. Here, as in other areas, agentic AI can orchestrate decision-making and subtask automation to achieve a goal specified by the utility.

For example, utilities are testing AI agents’ ability to assess, test and organize responses to disasters, such as hurricanes and wildfires. The agent can analyze data to assess damage to infrastructure and its effect on individuals and communities; plan and schedule rescue and repair work; and dispatch the workers and materials needed to complete repairs on time. This can dramatically speed up recovery times, potentially saving lives in the process, Brown said.

Brown described the example of a UK utility company using agentic AI to meet a regulatory requirement to contact customers with special needs, such as medical conditions, within a certain time frame during outages. The utility has struggled to meet the requirements using conventional technologies, but it has found success with AI agents. The agents can not only alert customers to disruption, but also inquire about their needs, understand those communications and act accordingly.

A fundamental shift, but not without challenges

The Deloitte report found that 26% of respondents’ organizations are already exploring autonomous agent development to a “large or very large extent.” But like generative AI, “agentic AI is not a silver bullet for everything a company needs to get done,” the report said.

Agentic AI systems present regulatory, security, data and workforce challenges not unlike generative AI systems. These problems “are likely to be even more important and challenging due to the increased complexity of agentic AI systems,” the report said.

But despite the limitations, Deloitte and other industry experts highlight the tremendous potential of agent AI in business operations.

“A lot of people don’t understand the impact,” Brown said. “Some still think it’s just another tool. But agentic AI will bring about a fundamental change in how we work. It will create new ways of working.”

Mary K. Pratt is an award-winning freelance journalist with a focus on covering enterprise IT and cybersecurity management.