Enterprises are now rolling out AI agents by the thousands, even after excitement over generative AI seemed to sink under the weight of unrealistic expectations.
But agentic AI takes gen AI a step further by emphasizing operational decision-making rather than content generation. The promise the approach has for impacting business workflows has organizations such as Aflac, Atlantic Health System, Legendary Entertainment, and NASA’s Jet Propulsion Laboratory pursuing agentic AI systems in their early days. Today, organizations as varied as DeVry University, AT&T, AUM Biotech, and Smarsh have had success implementing the technology.
With AI agents popping up in so many situations and platforms, organizations interested in the technology may find it difficult to know where to start. A handful of use cases have so far risen to the top, according to AI experts.
AI agents will integrate smoothly with ERP, CRM, and business intelligence systems to automate workflows, manage data analysis, and generate valuable reports, says Rodrigo Madanes, global innovation AI officer at EY, a consulting and tax services provider. AI agents, unlike some past automation technologies, can make decisions in real-time, making process automation a primary use case.
“AI agents can automate repetitive tasks that previously required human intervention, such as customer service, supply chain management, and IT operations,” Madanes says. “What sets the technology apart is its ability to adapt to changing conditions and handle unexpected inputs without manual oversight.”
Here are 11 top uses for AI agents, as seen by several AI experts.
Software development
AI agents promise to transform AI coding assistants, or copilots, into smarter software development tools that write large pieces of code. While coding assistants initially received mixed reviews, analyst firm Gartner predicts that smarter AI agents will write the majority of code within three years, leading to a need for most software engineers to reskill.
Coding agents will not only write the code, but separate agents will review code for errors, says Sheldon Monteiro, executive vice president and chief product officer at Publicis Sapient, a digital transformation advisory firm.
“With DevOps toolchains already automating workflows, adding AI agents is a natural evolution,” he says. “These agents can autonomously reverse engineer specifications from code, forward engineer test cases and code from specifications, and approve artifacts that that meet certain threshold criteria, improving the overall level of automation.”
Many organizations, including MITRE, have unleased agents to assist with coding. According to CTO Charles Clancy, MITRE has developed its own AI agents for code management.
“The best use case that seems to work well is in repository management, where it’ll go through and do bug fixes of code repositories,” he adds.
For example, 10-year-old source code might no longer compile properly on a modern computer, he says.
“The AI agent will download it, try to build it, and if it doesn’t run, it’ll fix the build scripts and code if necessary, check the code back into the repository, and flag it was done by an AI agent,” Clancy adds.
RPA on ‘steroids’
Many organizations are already using robotic process automation to automate simple and repetitive tasks in many areas. AI agents can also automate tasks, but they also can take on more complex problems that require higher-level decision-making functionality, Publicis Sapient’s Monteiro says.
“With AI, RPA moves beyond rule-based actions to adaptable, autonomous processes, significantly enhancing efficiency across business operations,” he says. “The new tools give us the ability to train agents to not just do the simplest of those tasks that RPA was doing, but actually to be able to understand some of the nuances of when exception logic also works.”
Some AI experts predict that agents will take on more complex tasks than RPA can handle, with agents sometimes working alongside RPA to achieve new levels of automation.
Many organizations will soon use AI to augment, and in some cases, replace traditional RPA, says Shae Khan, AI research scientist at the IBM MIT AI Lab. AI agents will be used to handle complex and dynamic tasks that require decision-making capabilities, while RPA will continue to be used for repetitive, rule-based processes.
Customer support automation
Organizations have long used simple chatbots and voice bots to handle simple customer service requests, but AI agents will allow customer service automation to evolve into a more robust service that doesn’t just answer a few frequently asked questions, says Glenn Nethercutt, CTO at Genesys, provider of AI-based customer experience solutions.
“The way I tend to define agentic AI is an autonomous ability to perform reason-based, multistep tasks that are nondeterministic,” Nethercutt says. “It’s an ability to handle really complex and adaptive decision-making processes without having human guidance.”
These customer service agents will cover a variety of industries and functions, including retail, financial services, and IT service desk help, he says. Instead of a highly curated bot that answers a limited number of questions, AI agents will be able to understand and provide contextual answers for a wide range of customer needs.
For example, a bank customer will be able to say, “Take money from my account that has the most money in it and move it to my checking account.” A simple chatbot typically can’t understand what “the account with the most money in it” means, Nethercutt says.
“The idea that presents itself is having this kind of catalog of the actions that can be done, and having an AI that is intelligent enough,” he says. “Here’s the panoply of options I have in front of me, and what I can choose to use, and guardrails will become increasingly complex.”
The customer support story has extended to voice services, with vendors like RingCentral offering agent voice platforms to handle customer queries. RingCentral’s AI Receptionist can answer voice calls, schedule appointments, route callers based on conversational context, and capture information for follow-up.
In some cases, users are resolving a large majority of their inbound calls using the voice agent. For example, Integral Recruiting Services uses AI Receptionist to automate 93% of inbound calls, reducing interruptions to recruiters and speeding up candidate placement, according to RingCentral.
Managing customer engagement
In other cases, companies are using agents to go beyond customer support to autonomously manage customer relationships. Monte Carlo, a data and AI observability company, has built a multi-agent system that oversees dozens of accounts without a dedicated account team. The agents analyze product usage, CRM data, customer conversations, onboarding progress, renewal timelines, and support activity, says Barr Moses, CEO of the company.
The goal of this agentic analysis is to determine whether a customer account needs onboarding assistance, expansion opportunities, renewal outreach, or escalation, the company says. The system routes customers into the appropriate workflow without human triage.
Bloomreach, an AI-powered e-commerce vendor, has combined the AI capabilities in analytics, content generation, and productivity into agents that understand customer behavior, identify potential audiences, build personalized marketing campaigns, and determine the best timing and channels for outreach.
Retailer 260 Sample Sale used Bloomreach’s Loomi Marketing Agent to identify its highest-intent customers, automatically create personalized outreach, and optimize campaign execution. The result was a 2.4x higher conversion rate while targeting 82% fewer customers than its previous broad-send approach, according to Bloomreach.
Automating enterprise workflows
With ServiceNow, Salesforce, and other vendors embracing AI agents, enterprise workflows will be a sweet spot for the technology, experts say, enabling businesses to streamline processes by automating routine tasks.
For instance, an AI agent could turn meeting notes into project tickets without human input or trigger a supplier order in response to a demand-supply prediction, Monteiro says.
Organizations deploying IT tools from a large vendor across the business should have an advantage over companies using a variety of solutions that may need to be linked by APIs, he adds. It will be important for enterprises to pool all their data and avoid information silos.
“The question that is materializing for CIOs is, Who are you going to entrust with building your context store, which is your deep knowledge of how your enterprise works?” he adds. “Think about all of the knowledge you have of your enterprise. What if your LLM actually knew the entirety of how your enterprise works?”
Cybersecurity and threat detection
Several cybersecurity providers have deployed AI agents to detect and respond to threats. “Agentic AI in cybersecurity can autonomously detect, react to, and even mitigate security and fraud threats in near real-time, reducing response times to potential attacks and enhancing overall security,” Monteiro says.
In addition, AI agents can enable personalized security protocols that adapt to specific threats and vulnerabilities, according to AI agent vendor Beam. “This agentic automation ensures a more robust defense mechanism,” the company claims.
AI agents can also drive efficiency and cost savings by automating routine tasks and security responses, according to Beam.
Enhanced productivity
Avantia, a global law firm, uses both commercial and open-source gen AI to power its agents, which then act as companions that sit inside Microsoft Word or Outlook, ready to carry out tasks.
“The key challenge in our area is there are hundreds of tasks that might not be particularly well automated,” says CTO Paul Gaskell. “And they don’t lend themselves well to a SaaS solution. There are too many separate tasks in too many places.”
The business benefit is that attorneys can get through the contracting process faster, respond to customers faster, and transact faster than anyone else.
“If a customer asks us to do a transaction or workflow, and Outlook or Word is open, the AI agent can access all the company data,” he says. “And because these are our lawyers working on our documents, we have a historical record of what they typically do.”
Another company using agents to automate business processes is SS&C, a financial services and healthcare technology company. The company receives documents from its 20,000 customers in a variety of formats, including emails and PDFs, says Brian Halpin, the company’s senior managing director of automation.
SS&C needs to process millions of documents a month, and the company has 20 use cases for AI agents to interact with documents.
The system went into production in mid-2024 and processed 50,000 documents in November. “And we’ll keep ramping that up,” he says.
With traditional automation, humans had to look at almost every document, he says, but with agents the automated percentage is in the low 90s, with only a small number of documents needing manual review.
Generating reports
Writing text and creating images were two of the first popular use cases for gen AI. Now, AI agents can turbocharge the content creation process. EY, for example, uses AI agents in its third-party risk management service.
“You hire us to evaluate some vendor you bring on board,” says Sinclair Schuller, principal at EY. “Our risk assessors do that work, spending up to 50 hours on one vendor, poring over contracts and other documents to produce a report that calls out risks we observe.”
That’s the way it was done in the past, until gen AI came along. Human experts now enhance reports generated by AI.
“Now we can feed AI all the contact and public documentation, and it can spin out a report in minutes instead of days with tremendous accuracy and detail,” he says. “AI plus human expertise is a tremendous boost in quality,” he says.
Now, with AI agents, the process is changing yet again. EY will release an agent-drive version of the process to evaluate vendors. “It’ll be a continuous monitoring of vendors, which was previously not possible,” Schuller says.
AI agents aren’t just about optimization use cases, he adds. “The real value is this expansion of the market, and expansion of revenue opportunities.”
HR and employee support
Another relatively low-risk, high-value use case for AI agents is answering employee questions and handling simple tasks on their behalf. A January IBM survey on gen AI development, in fact, concluded that 43% of companies use AI agents for HR.
Indicium, a global data services company, began deploying AI agents in mid-2024, for example, when the technology started to mature.
“You’d start seeing off-the-shelf applications — both open source and proprietary — that made it easier to build them,” says Daniel Avancini, the company’s CDO.
The agents are used to making things easier for HR, he says, including tasks such as internal knowledge retrieval, tagging, and documenting, as well as other business processes.
Each agent is like a microservice, specializing in one particular thing. “And they all talk to each other in a multi-agent system,” he says.
And these prompt-based conversations can get peculiar. The tricky thing is there’s a possibility of hallucinations and all the other problems that come with gen AI. “So there’s a lot of tweaking of the model so they don’t do the wrong thing or access the wrong information,” he says.
On the positive side, the AI agents can handle a lot of questions autonomously, creating another business benefit. “And we’re finding things that aren’t correctly documented, so it helps us make the processes better,” Avancini adds.
Some companies are also using agents to pump up workforce training programs. Online training vendor 5app uses coaching agents to supplement human-led catching between sessions, says Philip Huthwaite, the CEO there. The goal is to keep learning points front of mind between human coaching sessions and to keep employees engaged with the subject matter.
Coaching agents can offer interactive training activities, such as tailored roleplay scenarios, allowing employees to put their knowledge into practice. Agents also offer a cost-effective way to personalize training to each employee, Huthwaite says.
Business intelligence
Another area where AI agents will have a large impact is business intelligence. While BI dashboards are relatively simple to use, gaining insights that go beyond the standard categories has often taken the work of a data team to extract, says Ryan Janssen, co-founder and CEO at Zenlytic, an AI-powered BI vendor.
An AI agent paired with a BI solution could give more employees access to useful analytics, he says. For example, an AI agent for BI could advise a marketing team about where to spend its budget or create a chart based on an example drawn on a napkin, Janssen says.
AI agents that understand voice inputs can generate business data insights based on spoken questions such as, “What are our top three marketing channels?”
“That’s a very natural question, but it’s ambiguous,” Janssen says. “What you can’t do with the chatbot versus an agent is disambiguating that ambiguous question. What do you mean by ‘top’? The agent, when well built, will say, ‘Oh, wait, this is ambiguous; I need to go back and use a tool for this.’”
Many organizations are just at the start of their agentic AI journeys, and there are hundreds of uses yet to be discovered, Janssen adds. Coding agents are an early use case because programming is detail-driven and time consuming, but now coding hobbyists are building apps using coding assistants.
“The way that they are best applied is when you have work that is grindy, takes a lot of work, or requires a lot of attention to detail,” Janssen says.
When dozens of agents get strung together and organized, enterprises will see new breakthroughs, he adds.
“We haven’t even scratched the surface yet with what agents can do,” he says. “We don’t know what an organization looks like yet, how they’re supposed to interact, and how it is governed. But I have no doubt that over the next couple of years, we’re going to figure that out.”
Agents on the manufacturing floor
Manufacturing organizations are increasingly deploying agents to control or monitor equipment on the shop floor, according to several sources. Manufacturing-focused AI vendor Augury finds that 87% of manufactures in the US and Europe have adopted or experimented with generative and agentic AI tools as of June 2026.
For example, Augury combines machine health and other data with advanced reasoning from Google’s Gemini models, allowing manufacturers to build self-optimizing production environments, the company says.
Meanwhile, XOi, a data intelligence company, helps organizations capture and structure information about physical assets across manufacturing, facilities operations, HVAC, and other environments
Xoi is seeing growing interest in AI systems that help techs and operators identify equipment, retrieve maintenance history, find relevant documentation, and provide contextual recommendations based on machine-specific information.
Agents help people make faster, more informed decisions in environments where incomplete information, equipment complexity and downtime carry significant operational consequences, the company says.