AI agents have taken over as perhaps the most hyped technologies over the past year, after the excitement over generative AI seemed to sink under the weight of unrealistic expectations.

AI agents take 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 already pursuing AI agents.

CRM leader Salesforce has since centered its strategy around AI agents, with the announcement of Agentforce. IT service management giant ServiceNow has also added AI agents to its Now Platform. Microsoft and others are also joining the fray.

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 , 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 nine top uses for AI agents, as seen by several AI experts.

Software development

AI agents promise to transform , or copilots, into smarter software development tools that write large pieces of code. While coding assistants have so far 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 , 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 , 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 , 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 , 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.”

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,” .

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 . “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 , 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 , 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 , 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 a another business benefit. “And we’re finding things that aren’t correctly documented, so it helps us make the processes better,” Avancini adds.

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 , 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.”