AI Agents | Definition, How They Work & Examples
AI agents are computer programs that use AI to accomplish goals. AI agents work autonomously to make decisions, learn, adapt, and improve.
A few examples of AI agents are customer service automation, self-driving vehicles, and robot vacuums.
While QuillBot’s AI Chat isn’t exactly an AI agent, it can still help you automate many tasks, like brainstorming, drafting text, and checking for mistakes.
What is an AI agent?
An AI agent is a software entity that can process information—which sometimes includes perception of its environment—make decisions, and act to achieve specific goals without continuous human input.
An “agent” is “a person or thing that acts on behalf of another” or “a person or thing that takes an active role in producing a specific effect.” Both definitions are true of AI agents, which take an active role in completing tasks on behalf of human users.
AI agents are also sometimes called “AI-driven agents.” A related term is “agentic AI,” although there is a slight difference between an AI agent and agentic AI. Agentic AI is the framework, while AI agents are the building blocks within that framework. For example, a smart home system would be agentic AI, while individual components like the lighting and thermostat would be the AI agents.
The biggest difference between AI agents and other AI technologies is that agents are able to proactively make decisions and carry out tasks autonomously. AI assistants or bots do not operate with that level of autonomy.
| AI agent | AI assistant | Bot | |
| Purpose | Making decisions, solving problems, adapting | Completing specific tasks, assisting with productivity | Automating repetitive or simple tasks |
| Autonomy | High (act independently) | Low (require user input) | None (follow preset scripts) |
| Complexity | High | Medium | Low |
| Goal | Pursue defined objectives | Focus on user-requested tasks | None |
| Capabilities | Can handle multi-step tasks and make decisions independently | Can interact with voice/text interaction and execute basic tasks | Scripted responses and data scraping |
| Interaction | Proactive (can initiate actions) | Reactive (responds to user prompts) | Reactive (triggered by events or input) |
| Learning | Machine learning for adaptation | Some learning capabilities | Limited to none |
| Example | Drone delivery systems, surgical robots, personalized streaming recommendations | Siri, Alexa, Grammar Checker, Paraphraser | Automated chatbots on websites, social media follower bots |
ChatGPT, AI Chat, and other similar tools can also be classified as generative AI (they generate content) or chatbots (you can have a conversation with them).
How do AI agents work?
Large language models (LLMs) are the core of AI agents. Simply put, an LLM is an AI model designed to understand and generate human language. LLMs work like the “brains” of AI agents and provide them with the ability to understand, reason, and act.
AI agents operate in a loop that allows them to adapt and improve over time. This loop consists of four key stages, outlined below.
- Planning: Humans assign an AI agent a goal, and the agent begins planning based on the goal and available data. This plan outlines a sequence of actions the agent will take to accomplish its goal. The planning phase may involve prioritizing tasks, considering constraints, and predicting outcomes. For simple tasks, the planning stage may not be necessary.
- Reasoning: Next, the agent reasons about the situation. Often, the agent will have to acquire and analyze other information using the tools available to it (e.g., APIs, web searches, and even other AI agents). Once it has all the info it needs, the agent draws conclusions, identifies patterns, and makes decisions based on context. Reasoning allows the agent to adapt its plan as conditions change, especially when encountering unexpected obstacles or variables.
- Acting: The agent then executes actions based on its planning and reasoning. Actions might include navigating an environment, controlling physical devices, sending digital messages, or triggering system-level responses. These actions are performed autonomously, without continuous human input.
- Learning: Finally, AI agents learn from the outcomes of their actions. The learning stage may involve machine learning techniques (e.g., reinforcement learning), optimization algorithms, and user feedback. This means agents can improve their performance, efficiency, and decision-making in future tasks.
When the drone receives a delivery request, the agent plans how to achieve its goal (in this case, the delivery of the package). The AI agent analyzes the destination, current weather conditions, and any restricted airspaces it must avoid to calculate the best route.
Next, the drone sets off, but it doesn’t stick to its plan at all costs. Instead, it processes data from its sensors—evaluating real-time conditions in its environment—and reasons to adapt its plan when necessary. A sudden gust of wind or a flock of birds may cause it to change altitude or reroute, and it makes these decisions without waiting for human input.
Once a new course is determined, the drone acts. It executes the updated flight path, adjusting its speed and avoiding obstacles, all while maintaining stability and safety. The AI directly controls the drone’s movements, turning its reasoning into real-world action.
After the delivery is complete, the drone’s AI doesn’t just stop. It learns from the mission, analyzing the data it gathered, such as weather conditions, battery usage, and the success of obstacle avoidance. Using this information, it refines its models, becoming better at predicting challenges and optimizing future deliveries.
Components of AI agents
AI agents are composed of different components, each responsible for a different aspect of their behavior and performance. These are some of the essential elements that make up an AI agent’s architecture:
- Memory: This is where an AI agent stores information about its past actions, interactions, and outcomes. Memory allows for long-term learning and contributes to the agent’s ability to improve over time.
- Persona: The persona or profile module defines the agent’s “identity.” It stores attributes such as the agent’s role, preferences, constraints, and operating parameters. This module can be static or dynamic, allowing the agent to adjust its personality or strategy based on user profiles or environmental changes.
- Interface: The interface is how the AI agent interacts with the external world, whether through text, voice, sensors, or control signals. A well-designed interface ensures smooth communication between the agent and its users or environment.
- Collaboration: AI agents often work as part of a larger system, which may include other AI agents, humans, or external tools. The collaboration module controls how the agent interacts with these entities by managing communication protocols, task delegation, and information exchange.
- Self-refinement: This is the agent’s ability to improve its behavior over time and is crucial for AI agents operating in dynamic environments. This component involves incorporating feedback from actions, analyzing performance metrics, and updating models or strategies.
Types of AI agents
AI agents can be categorized in different ways based on various factors.
By interaction
AI agents can be categorized by how they interact with their environment:
- Surface agents interact directly with users or the environment (e.g., delivery drones or customer service chatbots). Their outputs are visible and immediate.
- Background agents operate behind the scenes, performing tasks that are not directly visible to users (e.g., e-commerce recommendation engines or data analysis services).
By agent number
AI agents can also work alone or together.
- A single agent operates independently to achieve its objectives and is best suited for well-defined tasks (e.g., a robotic vacuum cleaner that maps and cleans a home).
- A multi-agent system involves multiple AI agents working together towards a common goal. By leveraging the different capabilities of each agent, they can take on complex tasks (e.g., a coordinated fleet of self-driven taxis).
By capability
There are also different types of AI agents based on their capabilities.
- Simple reflex agents react to specific inputs with predefined actions or “reflexes,” without considering the wider context (e.g., a thermostat that turns on when the temperature drops below a threshold). These agents do not have memory.
- Model-based reflex agents maintain an internal model of the world using perception and memory (e.g., a robot vacuum that maps a house layout to navigate efficiently). As they receive new information, they update this model.
- Goal-based agents operate with an internal world model and explicit objectives in mind, planning and adapting actions to achieve those goals (e.g., a delivery drone planning and executing a route).
- Utility-based agents plan and make decisions based on a utility function that weighs different options and selects the most beneficial (e.g., an autonomous trading algorithm evaluating risk and reward).
- Learning agents can do the same things as other types but also continuously improve by learning from experience (e.g., personalized e-commerce recommendations). This learning allows them to operate better in unfamiliar environments.
AI agents examples
There are many use cases for AI agents across industries to automate diverse tasks, enhance decision-making, and improve efficiency. A few examples are listed below.
| Use Case | Examples of what these agents can do |
| AI agents for business |
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| AI agents for marketing |
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| AI agents for sales |
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| AI agents for healthcare |
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By handling these functions autonomously, AI agents free up human employees to focus on more important, strategic, and complex work.
For example, a vertical AI agent working in logistics can manage supply chains, plan delivery routes, and manage delays and shortages. It can handle the tasks necessary in that domain but can not complete tasks related to healthcare.
Some people believe vertical AI agents are the next evolution of Software-as-a-Service (SaaS) solutions.
AI agent best practices
AI agents are powerful tools, but they must be carefully managed to ensure reliability, safety, and efficiency. Here are some best practices to follow when using AI agents:
- Activity logs: Comprehensive activity logs enable audit trails, facilitate troubleshooting, and provide transparency in decision-making processes. Moreover, for sensitive industries like healthcare or finance, these logs are critical for compliance and accountability.
- Interruptibility: While AI agents are designed to operate autonomously, it’s important to have the capability to interrupt or pause their activities. Systems should include emergency stop functions, override controls, or built-in human intervention points. This ensures that agents don’t take unintended or harmful actions.
- Human oversight: Human input helps AI agents understand expectations and boundaries, especially in the early stages. Human oversight also makes sure agents align with ongoing strategic goals, ethical considerations, and legal compliance. Oversight can involve regularly reviewing agent outputs, monitoring for anomalies, defining clear escalation procedures for unexpected situations, and requiring human confirmation for high-risk actions (e.g., sending mass emails or financial trading).
- Security and privacy controls: AI agents must handle data securely. Apply encryption, access controls, and anonymization techniques to protect sensitive data and comply with relevant data privacy regulations like GDPR, HIPAA, and so on.
Frequently asked questions about AI agents
- What are AI agents for ecommerce?
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AI agents for e-commerce are software systems designed to automate and optimize many aspects of online retail.
These agents can handle diverse tasks, like managing a live customer service chat, fulfilling orders, processing returns, personalizing shopping experiences, optimizing pricing strategies, and managing inventory.
By leveraging machine learning, AI agents enable e-commerce retailers to work more efficiently, freeing up human employees to focus on important, strategic tasks.
To learn more about AI agents for e-commerce, try asking QuillBot’s AI Chat.
- What are AI agents for creators?
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AI agents for creators are AI-powered tools designed to assist content creators like writers, artists, videographers, and designers.
These agents automate repetitive tasks and enhance creativity. They can handle diverse tasks, like generating or editing content, managing editorial calendars, analyzing audience engagement, or suggesting ways to optimize content.
Using these AI agents, creators are able to focus on important aspects of their industry, like innovation and storytelling.
You can ask QuillBot’s free AI Chat to tell you more about AI agents for creators.
- What are AI agents for software development?
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AI agents for software development are systems designed to automate development tasks, reduce errors, and increase productivity.
These AI agents may generate code, review code for errors, suggest bug fixes, analyze performance metrics, assist with deployment, automate testing, or perform code documentation.
Using machine learning, AI agents can anticipate developer needs and accelerate software development.
To learn more about AI agents for software development, try asking QuillBot’s AI Chat.
- What are AI agents for data analysis?
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AI agents for data analysis are AI-powered tools that autonomously collect, process, and analyze data.
These agents perform diverse tasks, like cleaning and organizing raw data, detecting anomalies, performing predictive modeling, and generating reports or visualizations.
AI agents for data analysis use machine learning to identify trends and provide actionable insights for businesses and researchers. They reduce manual data handling, speed up analysis, and enhance the accuracy of reports.
Try asking QuillBot’s free AI Chat to learn more about AI agents for data analysis.
- What are AI agents for customer service?
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AI agents for customer service are automated systems that handle customer service tasks, freeing up human agents to focus on more complex tasks.
AI agents can interact with customers via different channels, resolve common issues, answer FAQs, and escalate complex cases to human agents.
By using natural language processing and machine learning, customer service AI agents can understand human language, personalize interactions, and improve service quality.
If you want to learn more about AI agents for customer service, you can ask QuillBot’s AI Chat.
