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What are AI agents?

Written by: Amanda Phagan, Edited by: Joey Morris

Published: April 16, 2025

Key takeaways

  • AI agents are software programs that collect data, interpret context, make autonomous decisions, and complete tasks to meet specific goals.
  • edX helps business leaders build AI agent expertise through executive education in AI strategy, implementation, change management, and responsible use.
  • 26% of surveyed organizations are exploring AI agents to a large extent, according to Deloitte’s 2025 State of Generative AI in the Enterprise report.
Senior man sitting at his kitchen table looking at his cell phone. He is booking an online appointment with the help of his smart home device.

It seems that new applications for artificial intelligence (AI) are emerging every day, capturing the attention of both industries and consumers. Advancements in large language models (LLMs), generative AI, machine learning, and, most recently, AI agents are making headlines.

What are AI agents, and how are they revolutionizing our digital experiences? Learn more in our guide.

Understanding AI agents

An artificial intelligence agent is a software program that collects data, interacts with its environment, and performs tasks that solve specific problems. AI agents can process natural language, make autonomous decisions, and meet predetermined goals.

AI agent examples

You've likely interacted with an AI agent without even realizing it. AI agents regularly appear in consumer-facing technology, so you've probably engaged with one to find an answer to your question, resolve an order issue, or schedule an appointment. Some examples of AI agents at work include:

  • Automated call center representatives
  • "Smart" home devices
  • Virtual assistants
  • Fraud detection alerts
  • Self-driving cars
  • Spam email filters
  • Personalized product recommendations
  • Transportation app price fluctuations
  • Manufacturing robots

How do AI agents work?

How an AI agent works depends on its intended purpose. However, most AI agents incorporate LLMs' natural language processing techniques to automate and simplify complex tasks or workflows. At a basic level, AI agents receive a command or goal, "perceive" the situation, and then make a decision — often on behalf of a person.

How are AI agents different from other AI tools?

Many types of software are programmed to execute pre-determined tasks — so how are AI agents different?

AI agents can often stand alone as "rational" problem solvers. Their ability to autonomously "perceive" their environment and learn from it allows them to respond to unique situations and provide desirable results. This ability differentiates AI agents from generative AI tools, pre-programmed chatbots that follow flowcharts, and other basic AI models that require specific prompts or rules to generate outcomes.

Benefits of AI agents

According to Deloitte's 2025 State of Generative AI in the Enterprise report, 26% of respondents are exploring ways to incorporate AI agents into their organizations to a "large" or "very large" extent. This interest is driven by the several organizational benefits that AI agents offer, including:

  • Cost-effectiveness: AI agents are relatively inexpensive to implement.
  • Scalability: AI agents' efforts across an organization or industry can usually be multiplied efficiently.
  • Real-world applications: AI agents can help improve supply chain efficiency, aid in scientific discoveries, and navigate physical environments.
  • Productivity: Human teams can delegate repetitive, tedious tasks to AI agents, freeing up more time to work on mission-critical tasks best handled by people.
  • Customer experience: AI agents can respond around the clock to customer concerns, improving customer satisfaction.

Types of AI agents

There are five types of AI agents:

Simple reflex agents
What they do
Perform tasks based on a set of rules; can't consider "memory" of past experiences or future consequences
Example
When an elevator button is pressed, the elevator control system responds by navigating to the desired floor.
Model-based reflex agents
What they do
Operate using an internal model of their environment; can respond to changes and determine how their actions affect consequences
Example
Self-driving vehicle sensors detect stop signs, pedestrians, and other environmental obstacles — and deploy the brakes accordingly.
Goal-based agents
What they do
Act to achieve specified goals; can evaluate plausible outcomes of their actions and select the one most likely to meet a need or solve a problem
Example
An automated customer service representative presents a solution to help a customer solve their stated problem.
Utility-based agents
What they do
Measure the "desirability" of different choices and make decisions to maximize an overall beneficial outcome
Example
A navigation app identifies the fastest, easiest route for the user to take, even if there are other viable routes.
Learning agents
What they do
Gather information through user inputs and adapt over time without any predetermined programming
Example
A music app analyzes the songs you've "liked" and recommends other music you may also enjoy.

How executives can prepare to utilize and implement AI agents

AI agents aren't going away — so learning about them and considering potential implementations may be a wise business decision. As you research your options, be sure to:

  • Learn more about AI for business: Enrolling in an artificial intelligence executive education program can help you learn how to integrate AI into your business strategy.
  • Brush up on change management: AI agents may present a learning curve for some teams. Learn the basics of change management to get ahead of any potential obstacles.
  • Plan an interconnected system of AI agents: According to Deloitte's January 2025 State of Generative AI in the Enterprise report, isolated AI initiatives are less likely to improve return on investment (ROI) than multiple-agent systems that work together to streamline business processes.
  • Consider the ethics of implementing AI: Though it has many potential benefits, AI also presents a number of potential problems relating to security, privacy, and other important factors. Don't forget to carefully consider how to use AI ethically in your business strategy.

Frequently asked questions about AI agents

What are the key considerations when selecting an AI agent solution for my business?

The type of AI agent you select for your organization depends on your business goals and your company's line of work. You'll want to choose a model that can handle your organization's key tasks and workflows — and help you reach short- and long-term milestones. When selecting an AI agent solution, consider the following questions:

  • What tasks is the solution best known for performing?
  • How complex should the solution be?
  • What solutions, if any, are our competitors using?
  • How robust are the solution's privacy and cybersecurity features?
  • How easily can the solution be integrated?
What types of tasks or processes are best suited for AI agents?

AI agents are best suited for "always-on" tasks that humans aren't able or willing to do as efficiently — like responding to customer service inquiries 24/7, providing personalized shopping recommendations, and constantly searching for security threats.

What are common challenges organizations face when implementing AI agents?

When integrating AI agents into your organization, you may encounter the following risks and anxieties:

  • Lack of human oversight: Deloitte states that 35% of organizations surveyed in their January 2025 State of Generative AI in the Enterprise report cite mistakes with real-world consequences as their top barrier to AI adoption.
  • Unclear ethical boundaries: Many people question the ethics of employing AI agents to perform traditionally human skills. How can organizations balance human rights, healthy company culture, high-quality results, and increased efficiency?
  • Developing data governance: AI agents often have access to sensitive data — so a company's cybersecurity and data governance measures should be firm and time-tested before agentic AI is implemented.
  • AI unfamiliarity: People at your organization may be completely unfamiliar with and wary of AI, so education is key to managing expectations and encouraging adoption.

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