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10 real-world agentic AI examples and use cases
AI that doesn't just follow instructions but figures out how to get things done -- that's the promise of agentic AI, an emerging approach that's already changing some sectors.
From cybersecurity to supply chain management, agentic AI can help businesses automate complex, multistep tasks in real time.
The term agentic AI, or AI agents, refers to AI systems capable of independent decision-making and autonomous behavior. These systems can reason, plan and perform actions, adapting in real time to achieve specific goals.
Unlike traditional automation tools that follow predetermined pathways, agentic AI doesn't rely on a fixed set of instructions. Instead, it uses learned patterns and relationships to determine the best approach to achieving an objective.
To do this, agentic AI breaks down a larger main objective into smaller subtasks, said Thadeous Goodwyn, director of generative AI at Booz Allen Hamilton. These subtasks are then delegated to more specialized AI models, often using more traditional, narrow AI models designed for specific actions.
The decisions and actions of these component AI systems ultimately enable the AI agent to achieve its primary objective. And this capability is quickly maturing, according to Goodwyn.
"The idea of agents is not new; we've been working on this for a while," he said. "But the reason why it's getting so much attention now is because large language models and generative AI accelerated some of the characteristics agentic AI needs to be successful."
According to "Deloitte's State of Generative AI in the Enterprise" report, agentic AI is one of the most closely watched areas in AI development. Respondents described agentic AI (52%) and multiagent systems (45%) -- a more complex variant of agentic AI -- as the two most interesting areas in AI today.
10 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, enhancing decision-making and automating complex tasks. Here are 10 examples showcasing its potential to change how work gets done.
1. Multimedia creation
Although 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 like research, text generation, image selection and design to other AI systems, ultimately delivering a more refined and complete final product.
This use case exemplifies agentic AI as an orchestrator of AI capabilities, rather than a narrow, single-function technology, he added.
2. Knowledge retrieval
Agentic AI improves knowledge retrieval by accessing information and taking action based on insights. An agentic AI chatbot can, for example, access a knowledge base, answer user queries and even run next-best actions, Goodwyn said.
To illustrate, he pointed to the example of IT helpdesk operations. Whereas earlier-generation helpdesk 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 unable to resolve the problem automatically, the agent can triage the issue and route it to a human agent along with relevant information, saving the user from having to repeat all the details.
3. Risk reduction and security
Agentic AI can aid in enterprise security operations and risk reduction efforts by orchestrating the components of those activities, said Karen Panetta, an IEEE fellow, 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 scan for new and emerging threats, investigate anomalies and automatically take corrective action without human intervention. Similarly, in risk management, AI agents can search for unusual activity, investigate those patterns to determine if they're truly fraudulent, and automatically respond as needed, Panetta said.
4. Supply chain 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 produce, human supply chain workers typically would have to check available supplies in other regions, confirm prices, reconfigure supply and distribution routes, and find alternative sources of produce.
Historically, workers used technology to handle much, but not all, of that work. Now, agentic AI can orchestrate the entire workflow, Panetta said. Supply chain workers could input the desired outcome -- for example, finding and delivering the needed quantity of supplies at the lowest cost or with the quickest delivery -- and expect the system to not only identify how to do so, but automatically initiate actions to make it a reality.
5. Call centers
As of early 2025, agentic AI is already "running 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 might simultaneously analyze customer sentiment, review order history, access company policies and respond to customer needs based on those elements.
6. Overall customer service
Agentic AI can also improve customer service overall, not only 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 supportive tool can help ensure that all employees, regardless of skill or experience, provide a consistently high level of service to customers.
Furthermore, agentic AI can proactively serve customers at a level that human employees or even traditional AI generally cannot, Brown said. For example, a utility company might use agentic AI to identify customers who will receive unusually high bills; contact them with that information; offer specific, accurate and personalized information about why their bills are so high; and suggest ways to lower their bills in the future.
7. Scientific and materials discovery
Agentic AI shows transformative capabilities in areas such as drug discovery and new material creation, Panetta said. Of course, other technologies -- including machine learning and non-agentic AI -- have been used in these areas for decades, but agentic AI works on a much higher level.
"[Agentic AI] is smart enough to say, 'This is what I know, and based on these materials and [the characteristics the user is seeking] and my exploration, here's 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. Defense and military logistics
Goodwyn pointed to agentic AI's use in defense, where it can be used for logistics planning: a highly complex military task that involves moving material, equipment and troops via multiple transport modes across varied distances.
Agentic AI is in a pilot phase in such areas, Goodwyn said. He emphasized that AI agents are used in this context to orchestrate complex objectives, augmenting rather than replacing human judgment.
9. Manufacturing
Manufacturing is another sector that showcases agentic AI's potential, Brown said.
The technology can make decisions and take autonomous actions in long workflows encompassing multiple functions and IT systems. An agentic AI workflow could span from procurement to actual manufacturing, connecting to IT systems that power various components and using narrow AI models to complete subtasks.
In such a case, Brown explained, the agent could perform a complex, multistep workflow:
- Recognize that a needed material is running low.
- Flag that the material is not in stock from the regular supplier.
- Search and order from alternative suppliers who can ship the material to the manufacturer within a previously specified price range and time frame.
- Fill out the necessary forms.
- Populate required data within the appropriate digital systems.
- Reconfigure factory floor and production schedules to meet established deadlines.
"That 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 humans in the loop, determining control points based on a responsible AI framework.
10. Utilities
Agentic AI is also already in use in the utilities industry, Brown said. Here, as in other areas, agentic AI can orchestrate decision-making and subtask automation to achieve an objective specified by the utility.
For instance, utilities are testing AI agents' ability to assess, triage and organize responses to disasters, such as hurricanes and wildfires. The agent can analyze data to rate infrastructure damage and its effect on individuals and communities; plan and schedule rescue and repair work; and route the workers and materials needed to complete repairs on time. This can dramatically accelerate recovery times, potentially saving lives in the process, Brown said.
Brown described the example of a U.K. utility company that uses agentic AI to meet a regulatory requirement to contact customers with special needs, such as medical conditions, within a certain timeframe during outages. The utility company struggled to meet the requirements using conventional technologies but has had success with AI agents. The agents can not only alert customers to disruption but also inquire about their needs, understand that communication and act upon it.
A fundamental shift, but not without challenges
The aforementioned Deloitte report found that 26% of respondents' organizations were 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 noted.
Agentic AI systems pose regulatory, security, data and workforce challenges, not unlike generative AI systems. These problems "are arguably 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 stress the immense potential of agentic AI in business operations.
"Many people don't understand the impact," Brown said. "Some still think it's just another tool. But agentic AI will bring a fundamental change in how we operate. 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.
