What are AI agents?
Written by: Amanda Phagan, Edited by: Joey Morris
Published: April 16, 2025
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:
| Type of agent | What they do | Example |
|---|---|---|
| Simple reflex agents | Perform tasks based on a set of rules; can't consider "memory" of past experiences or future consequences | When an elevator button is pressed, the elevator control system responds by navigating to the desired floor. |
| Model-based reflex agents | Operate using an internal model of their environment; can respond to changes and determine how their actions affect consequences | Self-driving vehicle sensors detect stop signs, pedestrians, and other environmental obstacles — and deploy the brakes accordingly. |
| Goal-based agents | 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 | An automated customer service representative presents a solution to help a customer solve their stated problem. |
| Utility-based agents | Measure the "desirability" of different choices and make decisions to maximize an overall beneficial outcome | A navigation app identifies the fastest, easiest route for the user to take, even if there are other viable routes. |
| Learning agents | Gather information through user inputs and adapt over time without any predetermined programming | 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.