21 Examples of AI Agents

AI agents are quickly becoming a driving force behind automation, personalization, and decision-making across industries. At their core, AI agents are systems that can perceive their environment, make decisions, and take action to achieve specific goals with minimal or no human intervention. 

From the virtual assistants on our phones to smart chatbots, self-navigating robots, and intelligent recommendation engines, these agents are already part of our daily lives. In this blog, we’ll look at what AI agents are and how they work then highlight real-world examples that show their impact on business and everyday experiences.

What are AI agents?

An AI agent is a system that perceives its environment, makes decisions, and takes actions to achieve specific goals. It operates autonomously by processing input from sensors or data streams, interpreting that input based on programmed logic or learned behavior, and executing actions that influence the environment. The defining feature of an AI agent is its ability to act intelligently, making choices that align with a given objective, whether that’s reaching a destination, solving a problem, or responding to user input.

AI agents come in many forms, from simple rule-based systems to adaptive, learning-driven models. Some agents react only to immediate inputs, while others build internal models, plan ahead, or learn from past experiences to improve over time. They can function individually or as part of larger systems, interacting with humans, software, or other agents. AI agents are used in everything from virtual assistants and chatbots to industrial automation, robotics, and complex simulations, enabling machines to operate with increasing autonomy and intelligence.

Benefits of using AI agents

AI agents bring powerful advantages to modern systems by enabling machines to make decisions, adapt to changing conditions, and automate tasks without constant human input. Whether embedded in customer service platforms, industrial machines, or autonomous robots, AI agents can improve efficiency, accuracy, and scalability across a wide range of industries.

Increased efficiency and automation

AI agents can handle repetitive or time-consuming tasks much faster than humans, freeing up people to focus on higher-value work. In industries like finance, logistics, or customer service, AI agents help streamline operations by automating workflows, reducing delays, and minimizing manual intervention.

Adaptability in dynamic environments

Unlike rigid rule-based systems, many AI agents can adapt to changing inputs and unexpected conditions. For example, a delivery robot or virtual assistant can update its behavior based on real-time feedback, improving performance in environments where conditions constantly evolve.

Improved decision-making

AI agents analyze data and evaluate multiple options before taking action, often optimizing for specific goals or outcomes. This leads to smarter, data-driven decisions, whether it's choosing the most efficient delivery route, adjusting supply chain operations, or recommending personalized content.

Scalability across systems and users

AI agents can operate simultaneously across many instances, handling tasks for thousands of users or devices without requiring a proportional increase in human resources. This makes them ideal for scaling customer support, managing cloud infrastructure, or coordinating large networks of devices.

Continuous learning and improvement

Learning agents can improve over time by analyzing their own successes and failures. This ability to self-correct and evolve means performance gets better the longer the system is in use, resulting in increased value and reliability over time.

Consistent performance and availability

AI agents can work around the clock without fatigue, delivering consistent performance and responsiveness. In applications like monitoring, security, or technical support, this 24/7 availability ensures rapid response and minimal downtime.

Simple reflex agents

A simple reflex AI agent is the most basic type of intelligent agent, designed to act solely based on the current state of its environment without considering past experiences or future consequences. It uses condition-action rules—often called “if-then” rules—to decide how to respond to specific sensory inputs. 

These agents do not maintain any internal memory or model of the world; instead, they rely on immediate perception to trigger actions. While simple reflex agents are fast and effective in predictable, fully observable environments, they struggle in complex or dynamic situations where context or history matters.

Below are several real-world examples that illustrate how simple reflex agents are used in practical applications today.

1. Thermostat

A basic home thermostat is a classic example of a simple reflex agent. It monitors the current temperature in a room and makes a decision based on a predefined rule: If the temperature drops below a set threshold, turn on the heat; if it rises above another threshold, turn it off. 

The thermostat doesn’t consider past temperature trends, user behavior patterns or future predictions; it reacts only to the current input from its temperature sensor. This makes it highly efficient for straightforward tasks but limited in adaptability.

2. Automatic door sensor

Automatic doors at stores or office buildings use motion detectors or pressure sensors to trigger opening and closing. When the sensor detects motion (such as someone approaching), the door opens; when no motion is detected for a few seconds, it closes. 

The system doesn’t track who has entered or exited or how many people are nearby. It simply executes a programmed response to current sensor input. This simple reflex mechanism is reliable for environments where quick, direct action is needed without complex decision-making.

3. Roomba’s obstacle avoidance (basic models)

Basic models of robotic vacuums like the Roomba use bump sensors to detect obstacles in their path. When the vacuum collides with an object—a wall, chair leg, or toy—it immediately changes direction and continues cleaning. 

These models don’t build a map of the room or remember where they’ve already been. Instead, they rely on reactive behavior: If a bump is detected, turn and move in a new direction. While this makes them simple and cost-effective, it also means they can be inefficient in terms of coverage and navigation.

Model-based reflex agents

A model-based reflex agent is an AI system that improves upon the simple reflex agent by maintaining an internal model of the environment. This model allows the agent to keep track of unobservable aspects of the current state by using past information and sensor inputs. 

Instead of making decisions based solely on immediate perceptions, the agent uses its internal state—updated over time—to make more informed and context-aware choices. This makes model-based reflex agents more adaptable and effective in dynamic, partially observable environments where a simple “if-then” rule isn’t enough.

Some examples of how model-based agents are being used include:

4. Smart home security system

A smart security system equipped with motion sensors and cameras can use a model-based reflex approach to detect suspicious activity. For example, if motion is detected in the living room at 3 a.m. and no one is scheduled to be home, the system can flag it as unusual and send an alert. 

The agent maintains an internal model that includes factors like time of day, user presence, and previous activity patterns, enabling it to distinguish between normal and abnormal events rather than reacting blindly to every motion detected.

5. Self-driving car (basic navigation layer)

Autonomous vehicles use model-based reflex systems to help with real-time driving decisions. If a pedestrian is detected near a crosswalk, the car’s AI can slow down or stop, not just because the pedestrian is visible now, but because its internal model includes assumptions about road rules, object motion, and possible occlusions. 

This model helps the car anticipate changes, even when parts of the environment (like someone stepping into the road) are not immediately visible.

6. Warehouse robotics system

Robotic systems used in warehouses, such as automated guided vehicles (AGVs), often rely on model-based reflex behavior. These robots track their current location, the layout of the warehouse, and recent movements of other robots or obstacles to determine safe, efficient paths. 

For instance, if an AGV encounters a blocked path, it uses its internal map (model) of the warehouse to reroute instead of simply stopping or reversing direction. This allows for more continuous and intelligent navigation in a constantly changing environment.

Goal-based agents

A goal-based agent is an AI system that makes decisions by considering a desired outcome and evaluating different actions based on how well they help achieve that goal. Unlike reflex agents that act purely on current input or state, goal-based agents use search and planning techniques to weigh potential future states and choose the most effective path forward. 

This forward-looking behavior allows the agent to adapt to complex environments, balance trade-offs, and pursue objectives even when obstacles arise. Goal-based agents are particularly useful in dynamic, unpredictable scenarios where simple rule-following or reactive behavior is not enough.

Examples of how goal-based agents are being applied include:

7. Apple Maps

Apple Maps and other GPS navigation apps act as goal-based agents when they plan a route to a user’s chosen destination. Given the goal (e.g., arriving at a specific address), the system considers various factors such as current location, traffic conditions, road closures, and estimated travel times to determine the optimal route. If conditions change, like a traffic jam or missed turn, it reevaluates and replans based on the same goal, ensuring the user continues moving efficiently toward the desired endpoint.

8. Automated customer service chatbot

Goal-based AI chatbots used in customer service are designed to achieve specific objectives, such as resolving a billing issue or helping a user reset their password. The bot doesn’t just react to keywords; it uses decision trees or natural language understanding to guide users through a process that leads to resolution. It may ask clarifying questions, pull in relevant data, and offer different solutions, all while working toward the goal of solving the user’s problem.

9. Siri and Alexa

Virtual assistants like Siri or Alexa act as goal-based agents when responding to user commands. If a user says, “Remind me to call John at 5 p.m.,” the assistant interprets the request, sets the appropriate reminder, and triggers an alert at the specified time. The system’s goal is to fulfill the user’s intent, and it selects actions, such as accessing the clock, creating a reminder, and sending a notification, to achieve that objective efficiently and accurately.

Utility-based agents

A utility-based agent is an AI system that makes decisions based on a calculated measure of “utility,” or how desirable a particular outcome is relative to others. Rather than simply achieving a goal, these agents aim to maximize overall satisfaction, efficiency, or performance by weighing trade-offs and choosing the option with the highest expected benefit. 

Utility functions allow the agent to handle uncertainty, prioritize competing objectives, and make more nuanced decisions than a goal-based agent. This makes utility-based agents especially effective in environments where multiple outcomes are possible and not all are equally valuable.

Examples of how utility-based agents are being applied include:

10.  Waymo

Waymo, formerly known as the Google Self-Driving Car Project, is a great example of utility-based AI agents. Waymo and other self-driving cars used for ride-hailing services, like those in a robo-taxi fleet, act as utility-based agents when they select which passenger to pick up or which route to take. It may evaluate factors such as distance, traffic congestion, passenger ratings, and fare value, assigning utility scores to each option. The vehicle chooses the route or rider that maximizes profitability and efficiency, balancing factors beyond just reaching a destination.

11. Wealthfront

Wealthfront is one of several great examples of AI-powered automated investment tools or robo-advisors. These platforms use utility functions to build portfolios tailored to an investor’s preferences and risk tolerance. These platforms weigh potential returns, market volatility, and diversification strategies to recommend asset allocations that best align with the user's financial goals. Rather than merely selecting safe or high-yield investments, the agent seeks to maximize long-term utility, typically defined as a balance of growth, risk management, and liquidity.

12. Smart energy management system

In smart homes or buildings, energy management systems use utility-based reasoning to optimize power usage. For instance, the system might decide whether to run the dishwasher now or later based on current electricity prices, appliance load, and occupancy patterns. By assigning utility values to different scheduling options, it selects the timing that minimizes cost, maximizes energy efficiency, and maintains user comfort.

Learning agents

A learning agent is an AI system that improves its performance over time by learning from experience, adapting its behavior based on feedback, success, or failure. Unlike reflex or goal-based agents, learning agents are not limited to predefined rules or static models; they can modify their internal processes to handle new situations, optimize decisions, and refine strategies. 

These agents typically include components such as a learning element, a performance element, a critic (for feedback), and a problem generator (for exploration). This structure allows them to operate in complex, evolving environments where rules may change, patterns may shift, and adaptability is essential.

Learning agents are being applied in the following types of applications:

13. Email spam filter

Modern email spam filters act as learning agents by continuously updating their models based on user feedback and new data. When users mark messages as spam or not spam, the system refines its understanding of what constitutes unwanted content, improving its ability to catch malicious or irrelevant emails. Over time, the filter becomes more accurate and personalized, adapting to new spamming techniques and individual user preferences.

14. Netflix and Spotify

Recommendation systems used by streaming platforms like Netflix or Spotify learn from user behavior to suggest content that aligns with individual tastes. These agents analyze viewing or listening history, user ratings, search patterns, and even pause or skip behavior to adjust their recommendations. As users continue to interact with the platform, the agent learns which types of content lead to greater engagement, optimizing its future suggestions accordingly.

15. Autonomous drone navigation

Drones used in delivery, agriculture, or search and rescue can operate as learning agents when navigating complex environments. By using reinforcement learning, these drones adapt their flight paths based on obstacles, wind conditions, terrain changes, and prior outcomes. Each mission helps refine the drone’s strategy, allowing it to improve efficiency, safety, and reliability with every new flight.

Autonomous agents

An autonomous agent is an AI system that operates independently in a given environment, continuously perceiving and acting without direct human intervention. These agents make decisions based on their goals, knowledge, and context, often adapting in real time as situations change. 

Unlike simple or reflex-based agents, autonomous agents integrate elements of planning, learning, and self-direction, enabling them to function effectively over extended periods. They perform tasks reliably, even in dynamic or unpredictable environments, and are often found in real-world applications where 24/7 adaptive operation is essential.

Autonomous agents are being deployed in the following kinds of instances:

16. Autonomous delivery robots

Delivery robots used on sidewalks or in office complexes are excellent examples of autonomous agents. These robots navigate paths, avoid pedestrians, and adapt to unexpected obstacles while independently transporting goods to customers. Equipped with sensors, GPS, and AI navigation systems, they operate without direct human control, making decisions on the fly to complete delivery tasks reliably and efficiently.

17. Robotic process automation (RPA) bots in finance

In financial services, autonomous RPA bots can perform repetitive back-office tasks such as invoice processing, data entry, and transaction validation. These bots monitor digital systems, make rule-based decisions, and act across multiple applications without constant oversight. They can operate around the clock, handle large volumes of work, and adapt workflows based on evolving data or conditions.

18. Mars rovers

NASA’s Mars rovers, like Perseverance or Curiosity, are highly sophisticated autonomous agents that explore the Martian surface with minimal real-time guidance from Earth. They analyze terrain, avoid obstacles, conduct scientific experiments, and send data back to mission control, all while operating independently for long durations. Their autonomy is crucial due to the communication delay between Earth and Mars, which makes real-time human control impractical.

Multi-agent systems

A multi-agent system (MAS) consists of multiple interacting AI agents that work within a shared environment. These agents can be cooperative, competitive, or both, and each one may have its own goals, knowledge, and capabilities. 

Rather than operating in isolation, agents in a MAS communicate, negotiate, and coordinate to solve problems that are too complex or large for a single agent to handle efficiently. Multi-agent systems are especially valuable in distributed environments where real-time responsiveness, scalability, and collaboration are essential.

Multi-agent systems are deployed in applications such as:

19. Autonomous drone swarms

In military surveillance, agriculture, or disaster response, drone swarms use multi-agent coordination to cover large areas efficiently. Each drone operates as an independent agent but shares data with the others, adjusting its path or behavior based on collective inputs. This coordination allows the swarm to avoid collisions, maximize coverage, and dynamically respond to changing conditions in real time.

20. Smart grid energy systems

Smart grids use multi-agent systems to manage distributed energy resources such as solar panels, batteries, and consumer demand across neighborhoods or cities. Individual devices act as agents that monitor usage, supply, or pricing and then negotiate with other agents to balance load and minimize energy waste. This decentralized approach improves resilience, reduces peak loads, and supports more efficient energy distribution.

21. Gaming NPCs

In large-scale online games, non-player characters (NPCs) often function as a multi-agent system. Each NPC may have its own behavior, goals, and reactions, but they also interact with one another and with players to create dynamic, lifelike environments. For example, guards may communicate to coordinate a search, or groups of characters may change tactics based on player choices, enhancing the realism and complexity of the game world.

The future of AI agents

The future of AI agents is set to be more autonomous, more adaptive, and more deeply integrated into the systems we rely on every day. As machine learning, natural language processing, and real-time data processing continue to advance, AI agents will evolve from task-specific assistants into context-aware collaborators capable of understanding complex goals, making nuanced decisions, and learning continuously from their environments. 

In everything from customer service to logistics, finance, and healthcare, agents will not only automate routine tasks but also anticipate needs, flag risks, and provide strategic insights, operating seamlessly across systems, devices, and platforms. 

We can also expect to see broader adoption of multi-agent ecosystems, where fleets of intelligent agents interact with one another to manage complex networks like smart cities, energy grids, and decentralized supply chains. These agents will increasingly be embedded with ethical reasoning, explainability, and human-centric design to ensure transparency and trust. 

As generative AI continues to shape how we create and communicate, agents may become proactive creative partners, generating content, writing code, or designing experiences in response to high-level human intent. Ultimately, the future of AI agents is not just about doing more with less, but about unlocking entirely new ways to think, work, and solve problems.

Smarter decisions start with smarter AI agents

As AI agents become more advanced and widespread, they’re transforming how businesses operate—driving efficiency, enabling smarter decisions, and powering real-time automation. But to get the most out of AI agents, organizations need unified access to data, context, and visibility into performance. That’s where Domo comes in. With Domo’s modern data experience platform, you can connect your data across systems, embed intelligence into workflows, and monitor AI agent activity in real time, all in one place. 

Whether you're deploying chatbots, automating operations, or enhancing analytics, Domo gives you the tools to make AI agents smarter, faster, and more impactful. Learn more about how Domo powers intelligent business with AI agents.