AI has quickly evolved from a novelty to something many people use every day. The simplest and common uses are to draft messages, summarize documents, or search the web. But a new evolution is underway with the rise of AI agents — AI systems that not only answer questions but act semi-autonomously to carry out tasks.

In simple terms, an AI agent is software that uses AI to pursue a goal and take actions in your digital life with minimal supervision. Instead of responding to one prompt at a time, an agent can read information, decide what to do next, and keep going until it reaches a result or requires your input. Rather than simply asking an AI to “Summarize my emails”, you could tell an agent “Help keep my inbox under control,” and it will read, sort, draft, and even send emails within the limits you’ve set.

That ability to act is what separates AI agents from simpler AI tools such as large language models. Here’s a closer look at what AI agents are, what they’re capable of, the risks involved, and how you can stay secure while using them.

In this article, you’ll learn:

What are AI agents used for?

AI agents are deployed in digital environments and used in both personal and professional settings to boost productivity. 

For everyday use, AI agents can:

  • Monitor your inbox, draft suggested replies, and highlight anything urgent
  • Organize notes or saved articles and generate quick summaries
  • Track price changes for flights or products, and notify you when they drop
  • Manage appointments in your calendar by suggesting meeting times and sending invites
  • Sort and tag photos or files for easy reference

In business cases, AI agents are frequently used to:

  • Help customer support teams triage tickets, classify them, and prepare draft responses
  • Assist sales and marketing teams by summarizing account information and generating personalized outreach for review
  • Take over repetitive back-office tasks, such as extracting invoice data into accounting tools or flagging unusual transactions
  • Assist IT and security teams by scanning logs and grouping related alerts

In all these cases, the practical benefit is clear: AI agents can save time and reduce manual effort by coordinating multiple steps across tools. But while AI agents can boost productivity and automation, they also open the door to new types of mistakes, attacks, and data breaches, as we’ll explore below.

How do AI agents work?

Most people’s primary experience with AI is through a chatbot like ChatGPT, Google Gemini, or Microsoft Copilot. A chatbot is an application that wraps an AI model in a conversational interface. You type a question, the chatbot sends it to the model, and you receive a reply.

AI agents build on that basic idea but add several important components. Most modern agents combine three core parts:

A language model

At the heart of many agents is a large language model (LLM). The LLM is responsible for understanding language instructions, reasoning about them, and generating text. It turns the outcome you set for your AI agent into concrete steps and decisions.

Tool use (or tool calling)

An agent is usually connected to external tools and services, such as email, calendars, databases, and web browsers. The agent can call these tools to read data or take actions, such as fetching recent emails and updating records or meeting invites.

Memory and context

AI agents often retain some form of memory so they can keep track of what has already happened and what still needs to be done. This can include everything from previous tasks to user preferences. Memory helps an agent work across multiple steps, instead of treating every interaction as isolated.

In general, an AI agent follows these steps:

  • You prompt the agent with a goal and relevant context
  • The agent interprets your request and plans a series of steps
  • It uses tools to gather information or take actions
  • Its memory is constantly updated based on what happened and informs what to do next
  • The process repeats until your goal is reached or your response is needed

Put simply, chatbots give you an interface to “talk to” a model and receive answers, but AI agents combine that model with tools and memory so they can actually do work inside your apps and accounts.

Types of AI agents

AI agents can be grouped into various types, depending on their functions and how they make decisions. In general, there are five types of AI agents:

Simple reflex agents

The most basic type of AI agents, simple reflex agents react only to current information and follow preassigned conditions without looking at past context. These are best suited for repetitive and simple tasks, such as filtering emails.

Model-based reflex agents

Similar to simple reflex agents, model-based reflex agents also use conditions to make decisions, but factor in past decisions and situations. This enables them to learn from memories of previous environments and adjust their patterns accordingly. Self-driving cars and robotic vacuum cleaners commonly use model-based reflex agents.

Goal-based agents

Rather than simply reacting to input, goal-based agents make decisions according to a desired objective. They’re more dynamic and advanced, and can evolve and map out new strategies even if obstacles crop up. GPS navigational systems are an example of goal-based agents, where your destination is the intended outcome and your route may change depending on traffic conditions.

Utility-based agents

Utility-based agents weigh up the optimal “values” of multiple outcomes and pick the option that offers the best trade-off according to a chosen metric, such as time, cost, or risk. They are most useful in situations where there are competing priorities — for example, when you need to prioritize tasks.

Learning agents

Designed to improve continuously, learning agents can adjust their behavior based on experience and feedback. This makes them more effective over time as they accumulate and process more data, and are particularly useful for fast-changing roles such as a virtual personal assistant.

How to get started using an AI agent

When you’re just starting out with AI agents, it’s best to be cautious with the tasks you ask them to do, then gradually refine them.

  1. Begin with a low-risk task
    Think of a simple task you want automated, like getting an AI agent to organize a newsletter folder, draft replies you still approve manually, or summarize saved articles. This lets you decide on the best type of agent for the job, and allows you to first observe how it behaves without giving it access to anything sensitive.
  2. Use built-in agent features first
    Many tools now include basic agent capabilities, such as “smart” inbox assistants, document organizers, or support bots. Using these is often safer than creating a custom agent, particularly if you don’t have much experience in coding and development.
  3. Add access step by step
    When you do connect an agent to your accounts, start with limited, read-only access. Only allow it to send emails, update records, or make changes once you are comfortable with how it performs.
  4. Review and adjust consistently
    Pay attention to what the agent gets right and where it struggles. Most systems let you fine-tune settings or narrow scopes so the agent stays focused on the tasks where it adds the most value.

After you become more familiar with how agents work and behave, you can move from small, personal use cases to more complex and integrated workflows — with appropriate safeguards in place.

Why AI agents mean new risks

Any system that can act on your behalf can also make mistakes on your behalf or be abused by someone else, and AI agents are no exception. Here are some common risks of AI agents:

Larger attack surfaces

Because AI agents generally have access to apps such as email, cloud storage, calendars, and dashboards, this makes them a bigger target. If an attacker can influence that agent, they can potentially move through all those connected systems and easily access confidential data.

Prompt injection and malicious content

Many agents routinely parse web pages, documents, and emails as part of their work. Attackers can hide instructions in that content, tricking agents into leaking data or bypassing safeguards because AI agents generally aren’t able to discern between genuine and faked instructions.

Over-privileged access

It may be tempting to give an agent broad access to maximize efficiency: full inbox control, production databases, or many internal tools at once. But this also means greater risk: if an over‑privileged agent is compromised or misbehaves, the damage will be far more severe.

Data leakage and compliance issues

Many agents send prompts and documents to third‑party AI services. If those providers store data or use it for training, you may be sharing more than you intend, with implications for personal privacy and regulatory requirements. 

But all of this is not to say that using AI agents is inherently dangerous; it just requires more care and the right tools and practices to keep you safe.

How to stay safe while using AI agents

The goal is not to avoid AI agents altogether, but to use them in ways that respect your privacy and minimize impact should things go wrong. Whether you are experimenting with AI in your personal life or rolling out agents at work, these steps can help:

  1. Limit what agents can access. Give each agent a narrow, clearly defined scope instead of broad access. For example, let a personal agent read from a specific email label rather than your entire inbox, or give a finance agent access only to test data until you trust its behavior.
  2. Always require human approval for sensitive interactions. Tasks that involve money, security settings, or sharing data outside your organization should require explicit approval. An agent can prepare payments, drafts, or reports, but a person should review and confirm anything high impact.
  3. Understand where your data goes. Before connecting an agent to real accounts or documents, check which providers it uses, where data is processed, how long it is stored, and whether it is used to train models. Opt for tools that give you clear, privacy‑respecting controls.
  4. Treat agents like privileged software. Log what they do, review it regularly, and be ready to revoke access quickly if something looks wrong. In organizations, that means knowing which agents exist, what systems they touch, and how to turn them off instantly.

This is where Proton Pass makes a difference. Proton Pass is an end‑to‑end encrypted password manager that also offers AI access tokens, allowing you to control and monitor which credentials your agent has access to.

Instead of sharing usernames, passwords, and API keys with your AI agent on an ad hoc basis, Proton Pass access tokens grant limited access to specific items or vaults. You can issue separate tokens for different agents, ensuring all your credentials stay encrypted and always under your control. Whenever an agent uses an item, it creates an audit log including the reason for access, so you can review and monitor your agent’s activity.

Used together, these practices let you tap into the benefits of AI agents while keeping risk contained.

Work and automate AI agents safely with Proton Pass

AI agents are a natural next step in how we use AI. They move beyond answering questions to actually helping with the work you do every day, in your inbox, files, and critical systems. That makes them particularly powerful — and something that deserves the same protections as any other sensitive software.

You can dramatically reduce the risks that come with this new wave of automation by limiting what each agent can access, keeping humans involved in important decisions, understanding where your data is processed, and refusing to share raw credentials. Adding a privacy‑first password manager like Proton Pass gives you a secure way to manage the credentials agents use, with AI access tokens providing precise control over which tools can access which vaults.

And as is the case with any kind of technology that requires confidential information, the most important thing to keep in mind when using AI agents is to build good security habits. Start small, keep access limited, and use tools that always put you in control of your data and your passwords.