What is an AI agent? How it works and ways to use it

AI agents represent a major shift in how people interact with technology. Unlike traditional software that simply follows commands, AI agents can plan, make decisions, and take actions autonomously across multiple tools and services to achieve specific goals. As these systems become more capable and widely adopted, they have the potential to transform everything from personal productivity to business operations.

However, greater autonomy also introduces new risks. Security vulnerabilities, privacy concerns, operational errors, and ethical challenges can all emerge when AI systems are trusted to act on a user’s behalf. Understanding both the benefits and limitations of AI agents is essential to using them safely, effectively, and responsibly.

What are AI agents?

AI agents are autonomous software systems that can understand goals, make decisions, and take actions on your behalf. Unlike traditional AI chatbots, which mainly provide information, AI agents can interact with apps, websites, databases, and other digital tools to complete tasks on their own.

Depending on the permissions they’re given, AI agents can complete multi-step tasks with little or no human involvement, making them useful for both personal productivity and business automation. For example, an AI agent might schedule appointments, send emails, research products, generate reports, or automate repetitive workflows entirely.

How are AI agents different from LLMs, AI assistants, and chatbots?

LLMs (large language models) are AI systems that interpret and generate language. They are the underlying technology that powers many AI tools, including chatbots, AI assistants, and AI agents, but they can’t take actions on their own. AI agents use LLM technology in different ways depending on their purpose and level of autonomy.

Here is an overview of the differences among these types of AI tools:

AI agents can do much more than just answer questions, respond to prompts, or perform one-time instructions. They go a step further by interacting with apps, websites, and other tools to perform actions autonomously. For example, while a chatbot might tell you how to book a flight, an AI agent could actually search for flights, compare options, and complete the booking on your behalf.

How does agentic AI work?

Agentic AI works by breaking a goal into smaller tasks, deciding how to complete them, using digital tools to take action, and adapting when circumstances change. Here’s how that process typically works:

1. Receive a request: The process starts when the user gives the agent a goal, such as “find the best price on this laptop” or “schedule a dentist appointment next week.”
2. Understand the task: The AI interprets the request, taking into account the user’s intent, preferences, and constraints such as budget, location, or timing.
3. Make a plan: The agent breaks the goal into smaller tasks and uses AI reasoning to determine the sequence of actions needed to complete it.
4. Use tools and gather information: The agent collects information by searching the web, accessing connected services, querying databases, or interacting with software through browsers and APIs.
5. Take action: Using the information it gathers, the agent may compare options, fill out forms, send messages, make reservations, or complete other approved actions.
6. Review and adapt: If circumstances change or a step fails, the agent can reassess the situation, modify its plan, and try an alternative approach.
7. Deliver the result: Once the task is complete, the agent reports the outcome and may provide recommendations, next steps, or follow-up actions.

Use cases of an AI agent

AI agents can handle a wide range of tasks, from simple everyday activities to complex workflows that traditionally require human oversight. They have applications across both personal and professional settings, including:

• Personal productivity: Managing calendars, prioritizing tasks, setting reminders, sending emails, and helping organize daily responsibilities.
• Travel and planning: Researching destinations, comparing transportation and accommodation options, building itineraries, buying tickets, and making reservations.
• Personal finance: Tracking spending, categorizing expenses, creating budgets, and providing insights that help users make more informed financial decisions.
• Customer support and service: Answering questions, resolving common issues, and guiding customers through tasks, providing fast, around-the-clock support.
• Business process and workflow: Automating repetitive tasks such as scheduling, document processing, and data entry, while also helping coordinate work across teams and systems.
• Sales and marketing: Qualifying leads, personalizing outreach, and recommending products, helping businesses deliver more targeted and effective marketing campaigns.
• Data analysis and decision support: Collecting information, identifying patterns, and generating insights that support faster, more informed decision-making.
• Industry-specific assistants: Specialized AI agents can be tailored for sectors such as healthcare, finance, education, and manufacturing to assist with domain-specific tasks and workflows.
• IT operations and cybersecurity: Automating routine maintenance and security tasks, monitoring systems for unusual activity, and helping respond to potential threats.

Benefits of AI agents

AI agents offer several advantages over traditional software and basic AI assistants. Because they can reason through tasks, use digital tools, and adapt to changing conditions, they can automate workflows that previously required significant human involvement. This makes them valuable for everything from personal productivity to business operations and cybersecurity.

Here are some of the key benefits AI agents provide.

Automating repetitive tasks

One of the biggest benefits of AI agents is their ability to automate routine and time-consuming work. Unlike traditional software that follows rigid instructions, AI agents can interpret goals, make decisions, and carry out multistep tasks across multiple tools and systems. This allows them to handle activities such as scheduling appointments, researching information, managing customer requests, and completing administrative workflows with minimal human involvement.

Improving productivity and efficiency

By handling repetitive tasks, AI agents free up time for people to focus on higher-value work that requires creativity, critical thinking, or human judgment. Individuals can use agents to streamline everyday tasks, while organizations can use them to reduce manual workloads, accelerate processes, and improve operational efficiency.

Adapting to changing conditions

Unlike traditional automation tools, AI agents can respond to new information and adjust their actions as circumstances change. They can monitor systems, evaluate outcomes, and modify their approach without requiring constant human input. This adaptability makes them useful for applications such as customer support, IT operations, research, and business process management.

Scaling without increasing workloads

AI agents can perform tasks continuously and process large volumes of information simultaneously. Organizations often use them to support customer service, cybersecurity monitoring, and workflow automation because they can operate around the clock without the limitations of human bandwidth. This allows teams to handle growing workloads more efficiently without increasing staffing requirements at the same rate.

Limitations and drawbacks of AI agents

AI agents can save time and improve efficiency, but they also introduce new risks and challenges. Because they can access software, data, and online services with limited human involvement, mistakes can have major real-world consequences. Understanding these limitations is essential for using AI agents safely and effectively.

Cybersecurity and privacy risks

Connected AI agents can become attractive targets for cybercriminals, especially when they have access to accounts, sensitive information, or business systems. Attackers may attempt prompt injection attacks, which use hidden or deceptive instructions to manipulate an agent into revealing data, ignoring safeguards, or taking unintended actions.

These risks are not merely theoretical. In one study, Okta researchers successfully used phishing and prompt injection techniques against the open-source AI agent OpenClaw, manipulating it into exposing sensitive credentials and information. As AI agents gain access to more tools and systems, securing those connections becomes increasingly important.

Errors, hallucinations, and reliability issues

AI agents can misinterpret instructions, make incorrect assumptions, or act on inaccurate information. Like other AI systems, they may also produce hallucinations — confident but incorrect outputs that can lead to flawed decisions or unintended actions.

These issues become more significant when agents are trusted with financial transactions, business operations, research, or other tasks where mistakes can have serious consequences. Human oversight is often still necessary to verify important decisions and outcomes.

Legal and ethical limitations

AI agents can raise difficult legal and ethical questions, particularly when their actions cause harm or violate regulations. Determining responsibility can be challenging when decisions are made through automated systems involving multiple software tools, data sources, and AI models.

Ethical concerns can also arise when AI agents reflect biases in their training data or produce outcomes that appear unfair, opaque, or difficult to explain. In many cases, users have limited visibility into how decisions are made and few mechanisms to challenge or correct them.

Types of AI agents (+ examples)

There are several types of AI agents, each designed to solve problems differently. Some follow predefined rules, while others can learn from experience, adapt to changing conditions, or pursue complex goals with minimal human oversight.

Understanding how these agent types differ can help you choose the right AI system for a specific task, whether it's answering questions, automating workflows, or making real-time decisions.

Rule-based agents (if-this-then-that)

Rule-based AI agents follow predefined instructions that connect specific conditions to specific actions. They don’t learn from experience or adapt to changing circumstances. Instead, they respond the same way whenever the same trigger occurs.

An email automation tool is a common example: messages containing specific keywords are automatically forwarded to a designated team or folder. If the trigger matches, the action is performed.

Reactive agents

Reactive AI agents are a form of rule-based agent that can make decisions based on current conditions rather than simply following preset fixed rules. They continuously monitor their environment and respond to real-time inputs, but they do not retain memory or plan for the future.

For instance, an AI-powered customer service agent might analyze a user's message, identify the intent, and generate an appropriate response based on the current conversation without considering previous interactions.

State-aware (memory-based) agents

State-aware AI agents maintain an internal memory of past events and use that information to inform future decisions. This allows them to recognize patterns, track changes, and respond based on context rather than treating every interaction as entirely new.

They are often deployed as AI customer support agents that can remember earlier parts of a conversation and adjust their responses based on information the user has already provided.

Goal-based agents

Goal-driven AI agents, like those found within agentic browsers, evaluate multiple possible actions and choose the ones most likely to achieve a desired outcome. Rather than simply reacting to events, they actively plan and adjust their behavior as circumstances change.

Travel planning is one of the most common goal-driven AI agent applications. They can independently compare flights, hotels, and transportation options, weigh factors such as price and travel time, and then recommend or book the itinerary that best matches the user’s preferences.

Learning agents

Learning AI agents improve their performance over time by analyzing outcomes and adapting their behavior based on experience. Unlike fixed-rule systems, they can refine their decisions as they gather more data and feedback.

For example, an AI research agent might learn which sources are most reliable, which search strategies produce the best results, and how to complete future research tasks more efficiently.

Voice and Collaborative AI Agents

Voice AI agents interact through spoken language, allowing users to complete tasks through conversation. Unlike traditional voicebots like Siri or Alexa, modern voice agents can do more than answer questions and perform basic functions. For example, a voice agent may answer incoming calls, authenticate users, and route requests to the appropriate service.

Many advanced AI systems also operate collaboratively. Collaborative AI agents share information and coordinate actions with other agents, software systems, or human users to achieve a common objective. Cutting-edge customer support platforms now often combine a voice agent, a chatbot, a scheduling agent, and a human representative to resolve issues more efficiently than any single system could.

How to use AI agents safely

AI agents can save time and automate complex tasks, but giving software greater autonomy also requires greater care. Setting clear limits, protecting sensitive information, and regularly reviewing an agent’s actions can help you benefit from its capabilities while reducing the risk of errors, privacy issues, or unintended consequences.

Here are some best practices to keep in mind:

• Set appropriate permissions: Grant AI agents only the access they need to perform their tasks. Avoid giving broad control over accounts, systems, or devices that aren’t directly related to their responsibilities.
• Limit data access: Restrict access to personal, financial, or confidential information whenever possible. Following the principle of least privilege reduces the impact of errors or misuse.
• Require human approvals: Keep people in the loop for high-impact decisions and activities, such as financial transactions, account changes, or sensitive communications.
• Monitor agent activity: Regularly review what an AI agent is doing, what data it accesses, and how it responds. Ongoing monitoring can help identify unusual behavior and highlight when permissions should be adjusted.
• Keep systems updated: Install software updates, security patches, and AI model updates promptly to help protect against known vulnerabilities and improve performance.
• Secure integrations: Use strong passwords, multi-factor authentication (MFA), and robust authorization controls to protect the systems an AI agent interacts with and prevent unauthorized access to sensitive information.
• Get an agent security layer: Specialized AI agent protection software can help monitor what your AI agent is doing behind the scenes, helping mitigate the risks of your agent taking an action that could compromise your data or personal Cyber Safety.

Use AI agents with greater confidence

AI agents can save time by handling tasks on your behalf, but greater autonomy also comes with greater risk. Norton AI Agent Protection, part of Norton 360, adds a real-time security layer that helps monitor your AI agent’s actions, giving you more visibility and control over what it does.

Whether your agent is browsing the web, accessing files, or carrying out tasks, Norton AI Agent Protection helps detect risky behavior, block known threats, and prompt you to review suspicious actions before they happen. Explore AI agents with greater confidence by adding a trusted security layer from Norton.

FAQs

Is ChatGPT an AI agent?

Not by itself. ChatGPT is primarily a conversational AI assistant powered by an LLM. However, when connected to external tools, apps, or workflows that allow it to take actions on a user’s behalf, it can function as part of an AI agentic system.

Are there free AI agents?

Yes. Many AI platforms offer free AI agents or entry-level agent features for tasks like research, scheduling, and simple automation. More advanced capabilities, higher usage limits, and deeper integrations typically require a paid subscription.

Can I create my own AI agent?

Yes. Basic AI agents can be built using no-code platforms or AI development tools, often with free tiers available. More sophisticated agents may require paid services, with costs varying based on complexity, integrations, and usage.

What does an AI agent do?

An AI agent takes a goal or request and works through the steps needed to complete it. It can gather information, make decisions, interact with software tools, and carry out actions on a user’s behalf. Unlike traditional chatbots, AI agents are designed to perform tasks, not just answer questions.