Depending on the use cases, product offering, and company, you may see various definitions of an AI agent. However, generally speaking, an AI agent is a software tool that can perform tasks autonomously for you. To perform the task, it can use a combination of human-delivered instructions and environmental triggers, as well as -- and this is perhaps the most important element -- its own reasoning capabilities to deduce what needs to be done and when. 

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"AI agents are the ones that we delegate tasks to. AI agents are goal-seeking," explains Srini Iragavarapu, AWS's director for generative AI applications and developer experiences. "They have the smarts to think about what is needed to perform the task. They can figure out what tools are needed. If more information is needed, they come back to you and ask for it." 

These AI agents differ from other AI technology you may already be using daily, such as AI chatbots or assistants, because agents can execute tasks on your behalf and identity when they're needed instead of just feeding you an answer from a predetermined subset of information. Therefore, the amount of assistance it can provide for individuals and businesses is significantly higher. 

An AI agent builds on technology you're likely already familiar with, combining the latest AI capabilities into one package to take assistance a step further. For example, it leverages tools like large language models (LLMs), reasoning, natural language processing, long context windows, and more to perform tasks more intelligently and adaptively.

"AI agents are a technology capability that use AI capabilities, including machine learning, natural language processing, to understand the context of an interaction a customer need, or a business transaction and then apply automation to manage the workflow associated with that context," according to Omer Minkara, principal research director at Aberdeen Strategy and Research. 

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To carry out tasks, an AI agent relies on a mix of human direction and the data or context it has been given permission to access. It can also be designed to react to external triggers, such as receiving an email, which can initiate a chain of actions or decisions. Ultimately, the information and context it uses depend on the user's specific use case, which can range from simple to highly complex, depending on the needs of the business. 

"We put it on this spectrum where you've got simple agents on the left-hand side, and then advanced agents, on the right-hand side, and it's an absolute spectrum," said Richard Riley, general manager of power platform marketing at Microsoft. "Some of those agents on the left-hand side of that spectrum could literally be grounded in one or two files." 

An AI chatbot can provide a user with the solution to a question by referring to its training data. However, it can only perform tasks it was predetermined to tackle. An AI agent can use the context from the information around it to make its own inferences and perform the task, which requires a higher level of reasoning. 

"A chatbot is more of a dialogue system -- you can just have a conversation with it," said Iragavarapu. "But in the AI agent world, agents are not only having conversations with you, they're actually doing things on your behalf as well." 

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A real-world example would be placing an order for groceries. If you use a chatbot or an AI assistant to ask for what to order for dinner, it will be able to generate a list and, in more advanced cases, even place the order. However, it wouldn't have the analysis capability to pull in other contexts, such as checking what you already have in your fridge (should you have a smart home inventory), analyzing your dietary preferences listed in your health app, coordinating with your calendar to know when you'll be home to receive the order, etc. 

Enterprise use cases for AI agents can be as simple or as elaborate as you'd like them to be. While some companies have AI agent offerings that can autonomously perform tasks for them across procurement, sales, customer interactions, and more, you can use AI agents to streamline a task as simple as sending a message. 

"It's more about understanding the art of the possible than it is, 'here's a menu of 10 things that you can do with agents,' because, ultimately, if you can build something, you can build anything, right?" said Riley. 

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The decision to implement AI agents into your workflow can be overwhelming, as there are so many offerings across many different business functions. However, a good place to get started is as simple as identifying which processes within your own business can use the extra assistance. 

"Number one, find a use case that matters to you," added Riley. 

These applications of AI agents don't have to be super elaborate. In fact, instead of seeking to implement complex AI agents that tackle multi-step challenges, it is better to look closely at your own business processes and choose ones that will have a greater impact, even if they are on the simple end of the spectrum. 

According to Minkara, one way to frame your thinking is to imagine these AI agents are employees meant to assist with some of the lower-level tasks in your organization's workflows. 

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"The vision is that you can automate and have AI agents pretty much be your digital employees managing many of the low-touch or low-complexity interactions," said Minkara. 

Thinking of AI agents as your team of employees can help you identify which tasks you would be interested in delegating to additional employees if you were given the opportunity and, in turn, how to best maximize the use of these AI agents. 

Beyond that, thinking of deploying an AI agent as hiring an employee can help ensure that you are taking the proper measures before "hiring" or deploying AI agents within your organization. A common mistake when using AI agents is deploying them out of the box and thinking they will positively transform your business. However, successful execution requires more careful deliberation.  

"Two to three months is pretty typical in a hiring cycle where you talk to many different hiring managers to make sure that the employee has the right skills and knowledge to fit in and also execute, said Minkara. "Now, we're not applying that same diligence to AI agents."

For a consumer or business leader, getting access to an AI agent is easier than ever, as most major tech vendors now have an AI agent offering. Companies with their own AI agents include Microsoft, Amazon, OpenAI, Google, AWS, Salesforce, Adobe, Zoom, and Qualtrics -- just to name a few. 

Most of these companies have intuitive AI agent-building systems. To get started, you can provide grounding data and answer a couple of questions. For enterprises specifically, there are often brand representatives who can help businesses identify which use cases could benefit from the addition of generative AI and what steps are needed to implement them. For businesses, selecting a vendor may just be a matter of staying with the vendor ecosystem you currently use. 

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The same applies if you are a consumer who wants to try out the technology for yourself. You can do that within the AI platform you are already using. For example, both ChatGPT and Google offer Deep Research, an agentic capability that searches through hundreds of sources across the web and compiles them into one neat document you can easily review, thereby accomplishing within minutes what would otherwise take you hours. Picking one over the other may just come down to selecting what you are the most familiar with. 

Assessing the hygiene of your company's data infrastructure is a major piece of the puzzle when considering implementing AI agents. This is not new advice, as it is also a pivotal component in most digital transformation opportunities, yet it is an often overlooked aspect of enterprise workflows. 

Minkara compares using bad, unorganized data to putting bad gas in a supercar. With bad fuel, you're not going to get the performance that the supercar is supposed to deliver. You need to use the good fuel -- and good data.

"The algorithms may be great, but if your data is garbage in... then you may have garbage outcomes," said Minkara. 

As AI agents can perform tasks for you, there's a lot of concern about the potential for these assistants to go rogue and wreak havoc on your personal or enterprise workflows. However, the first thing to remember is that they are trained to meet your specific needs and can not work outside of those confinements. 

"AI agents only can do what you allow them to do, so there is a set of actions that the agent can take and a set of permissions that the agents have within those actions to do," said Riley. 

This doesn't mean that AI agents are infallible. Ultimately, they are still AI models, and -- as seen with simpler AI tools such as ChatGPT -- they often hallucinate or output false information. Even though these AI agents are capable of higher-level reasoning, it does not exempt them from traditional pitfalls. 

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"Artificial intelligence is intelligence, yes, but it is still artificial, and you're still going to need some swimming lanes around it," said Minkara. 

Proper safeguards can prevent these pitfalls from affecting your business. Specific precautions can be taken both during and after the training process to ensure the best results. 

"As you are building these agents, it's important to have evaluation sets, datasets, quality analysis, and alarms to monitor when the agent isn't performing as expected for your use cases. This is something that you, as an enterprise and customer, can do," said  Iragavarapu. 

When considering AI agents, another common concern is how these assistants correlate to artificial general intelligence (AGI), an AI system so capable it can perform at the level humans do -- without human assistance. Many companies are currently working on achieving AGI; OpenAI has made clear that it's the company's ultimate goal. 

Even though AI agents can do tasks independently and give users a glimpse of AGI, these technologies are very different from each other. AGI is much more advanced and powerful, and still quite a bit away. A lot of research has to be done to get to that point, and not even experts can fully agree on how long it will take to get there. 

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"I think I feel like AGI is the top of the totem pole, and I guess we can call it the North Star," said Minkara. 

To reiterate, AI agents can work only within the confines of what you have allowed them to do; they're not quite taking actions into their own hands but rather doing what they are told to do. 

If you look at the news headlines and see AI agents mentioned everywhere, that reflects actual trends. A study by Boston Consulting Group (BCG) predicts that the market for AI agents will continue to grow at a fast rate, with a 45% CAGR over the next five years.

Furthermore, in a report published in November 2024, Deloitte predicted that 25% of companies that use generative AI will launch agentic AI pilots or proofs of concept in 2025. That number is expected to grow to 50% in 2027.

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"There will be as many agents as there are business processes, and there are a gazillion different business processes," said Riley. 

As AI agents represent the next frontier in artificial intelligence, and to stay ahead of the curve, right now is a good time to explore how they can be strategically leveraged to drive growth and innovation in your business.