Key takeaways
- AI agents are systems that can plan, execute, and complete tasks on your behalf with minimal guidance.
- Unlike chatbots or basic assistants, AI agents can make decisions, take action, and improve through feedback over time.
- AI agents work best as digital teammates that handle repetitive tasks, freeing you to focus on strategic work.
- Human review is still essential, since AI agents can make mistakes.
- You don’t need technical expertise to get started. Start with small, low-risk tasks to build confidence.
Imagine a study partner who never gets tired, a writing coach who never judges, or a sales assistant who always finds the perfect words. That’s the promise of AI agents: tools that can gather critical context, complete tasks, and adapt based on feedback with a growing level of autonomy. Using these tools, you can improve your writing and streamline your workflows—but how exactly do they work and how can you use them in your daily life?
In this article, we’ll explain AI agents, how they work, and share practical examples for how you can use them to elevate your writing, receive personalized feedback, and organize your tasks to stay on track.
Table of contents
- What are AI agents?
- How AI agents differ from chatbots and AI assistants
- How AI agents work
- Understanding the different types of AI agents
- Advantages of using AI agents
- Limitations of using AI agents
- Getting started with AI agents
- The takeaway: Why AI agents matter
- Intro to AI Agents FAQs
What are AI agents?
An AI agent is a software system that can pursue and complete tasks on a user’s behalf once given a goal. Unlike chatbots and AI assistants, which wait for specific instructions, AI agents can plan, execute, and refine their approach based on feedback or new information. Think of them like a digital assistant or teammate that you can delegate repetitive tasks to—so you can focus on thinking, creating, and work that matters most.
Grammarly’s AI agents show how agentic AI can specifically enhance writing and communication. Rather than waiting for one-off prompts, these agents support the full writing process—helping you brainstorm, outline, draft, revise, and refine your work based on context, audience, and intent.
Because Grammarly’s agents work directly within the tools you already use, they provide timely, relevant guidance as your writing evolves. The result is writing support that feels proactive and practical, helping you communicate more clearly while staying focused on the work that matters most.
What does “agent” really mean in AI agents
The “agent” in “AI agent” refers to the system’s ability to act with a degree of agency. Traditional software systems only do what you tell them to do, following their programming step-by-step. AI agents, on the other hand, don’t need to be constantly handheld: Give them a goal, and they’ll determine the steps to get there. Agents are also proactive and can take initiative by proposing actions before you ask.
How AI agents differ from chatbots and AI assistants
AI chatbots and assistants are designed to respond to specific prompts, whether that’s answering a question or performing a task. When using these tools, you need to ensure the prompt provides all the context they need to complete the task. You’ll also need to query them proactively—they won’t plan or complete tasks until you explicitly ask them.
AI agents, on the other hand, can take a goal (e.g., “Improve my essay” or “Summarize these meeting notes”) and decide which steps to take next, adapting as they go. They can gather and use the necessary context, adapt to changes, and even collaborate with other agents to complete multistep goals.
Here’s a side-by-side look at what each can do—and how they fit into your workflow:
| AI agent | AI assistant | AI chatbot | |
| Purpose | Achieve a pre-defined goal by planning tasks and adapting accordingly. | Complete specific user tasks or commands. | Respond to user queries. |
| Capabilities | Autonomously plan and complete tasks, including multistep tasks. | Perform tasks across multiple tools or contexts when instructed. | Respond to user input by retrieving the correct information or completing the requested task. |
| Interaction | Proactive (can operate or initiate actions without direct prompts). | Reactive (supports your workflow but always requires prompts). | Reactive (requires prompting to deliver results). |
| Example | An AI agent that joins your meetings, takes notes, and schedules follow-up action items. | An AI assistant that can draft an email, schedule a meeting, or summarize a document when asked. | A customer service chatbot that answers questions when asked. |
Practical ways to use AI agents in everyday work
AI agents can help you work more efficiently by assisting with complex tasks and handling manual work without being prompted, such as:
- Summarizing information: Turning long meeting transcripts, lectures, or research articles into concise summaries with clear takeaways.
- Providing personalized feedback: Reviewing your writing to highlight unclear arguments, suggest stronger transitions, and recheck revisions for improvement.
- Managing communication: Sending polite follow-ups, scheduling meetings, or drafting outreach messages for networking or client projects.
- Organizing projects and tasks: Prioritizing to-dos and flagging next steps so you can focus on strategic work, not busywork.
How AI agents work
At a high level, AI agents operate in a continuous loop that helps them move toward a goal with increasing effectiveness. Instead of responding once and stopping, agents observe what’s happening, decide what to do next, take action, and adjust based on the results. This cycle allows AI agents to handle multi-step tasks and adapt as conditions change.
The agent loop: perceive, plan, act, learn
Most AI agents follow the same four-step process:
- Perceive: The agent gathers the information it needs to complete the task. For example, it might read your emails to assess the tone, context, and purpose of your message.
- Plan: Using what it’s learned, the agent decides which actions will help reach the goal—such as clarifying confusing sentences, removing uncertain language, or improving transitions.
- Act: The agent carries out its plan by rewriting unclear sections, replacing weak phrases with stronger ones, and smoothing transitions.
- Learn: When you reject an edit or make your own changes, the agent learns from that feedback and adapts its approach for the next task.
This loop repeats until the goal is reached or the task changes.
Key components that make AI agents works
Behind this loop are several core components that enable AI agents to operate with autonomy:
- Perception: This is how an agent understands what’s happening around it. It takes in whatever you give it—emails, meeting notes, spreadsheets—and analyzes that information to gather context. It can also monitor these sources for changes, so when something new appears, it can automatically take action.
- Decision-making: Using large language models (LLMs) and reasoning techniques, the agent determines what to do next to achieve your goal. For example, it might use an LLM to understand what “confident” language looks like and apply its reasoning to identify uncertain phrasing in your email draft.
- Execution: To carry out its plan, the agent connects directly to your tools—like your inbox, calendar, or document editor—through application programming interfaces (APIs) and integrations. This setup allows it to send follow-up emails, add meeting notes, or pull relevant research from another platform on your behalf.
- Memory: The agent remembers what it has learned—your preferences, feedback, and past instructions—so it can make smarter decisions and better match your style over time.
Together, these components allow AI agents to move beyond one-off responses and provide ongoing, goal-oriented support.
Understanding the different types of AI agents
AI agents range from simple rule followers to systems that can plan, learn, and collaborate. They’re often grouped into two tiers: five core types that form the foundation of most AI systems, and advanced agents that build on them to handle more complex challenges.
The core five show how agents evolve from following basic instructions to reasoning and learning on their own—each one adding more awareness and decision-making ability than the last:
- Simple reflex agents: The most basic kind. They follow preset “if X, then Y” rules, but can’t handle situations outside of those rules.
- Model-based reflex agents: A step up from simple reflex agents. They track what’s happened so they can adjust when things change.
- Goal-based agents: Once given a goal, these agents can plan the steps needed to reach it.
- Utility-based agents: These agents aim to reach goals in the best possible way by weighing multiple options—choosing the one that provides the most benefit.
- Learning agents: They learn from experience and use feedback to refine how they perceive, plan, and act.
Advanced agents combine or scale these abilities to collaborate and take on more dynamic challenges:
- Multi-agent systems (MAS): Teams of agents that work together—like bees in a hive—each contributing to a shared goal.
- Hierarchical agents: A structured version of multi-agent systems. One “manager” agent delegates tasks to “worker” agents for greater efficiency.
- Hybrid-composite agents: The most versatile type. They blend reflex, goal-based, and learning approaches to react quickly and think strategically.
Let’s take a closer look at how these agents compare:
| Agent type | What it does | How it works | Example |
| Simple reflex | Executes pre-defined tasks. | Follows “if-then” rules; no memory. | Spell checker that automatically flags typos. |
| Model-based | Executes tasks and adapts. | Stores context to adjust actions as conditions change. | Support bot that updates its responses based on past issues. |
| Goal-based | Plans tasks to reach a specific goal. | Chooses actions that accomplish the goal. | Agent that organizes weekly priorities by deadlines. |
| Utility-based | Chooses the best action between alternatives. | Weighs trade-offs to maximize benefit. | Writing assistant that tailors suggestions to your company’s style guide. |
| Learning | Improves with feedback over time. | Adjusts future actions based on results and corrections. | Writing coach that learns your style as you accept or reject edits. |
| Multi-agent systems (MAS) | Multiple agents collaborate on a shared goal. | Each agent handles part of a task and coordinates with others. | Study assistant system: One agent summarizes, one creates flash cards, and another tracks progress. |
| Hierarchical | Organizes agents in a top-down structure. | Lead agent delegates to specialized sub-agents | Study-planning AI in which a top-level agent designs the schedule, and sub-agents summarize and edit. |
| Hybrid-composite | Combines multiple agent types. | Integrates reflex, goal-based, and learning agents for flexibility. | Personal AI assistant that answers questions, organizes projects, and learns preferences. |
Advantages of using AI agents
By off-loading routine, repetitive tasks to AI agents, you can free up time for work only you can do. Here are some ways agents can help:
- Reduce busywork: Let agents handle time-consuming tasks that pull you away from the meaningful work. Studying for exams? Have one turn your notes into flash cards and quick quizzes.
- Automate manual work: Hand off the repetitive tasks that drain your energy. For example, sales teams can use agents to draft outreach messages and schedule meetings while they focus on building relationships.
- Receive personalized feedback: AI agents can learn your style and adapt to your preferences, so you get suggestions that make sense for you. For example, AI agents can review marketing copy and provide suggestions that align with brand guidelines rather than generic advice.
- Provide proactive assistance: Agents don’t just wait for commands—they can surface helpful suggestions or next steps based on what’s happening in real time. For example, a customer support agent might point you to similar past tickets or recommend solutions that worked before.
- Tackle complex projects: Big projects can feel overwhelming, but agents make them manageable by helping break them down. Writing a research paper or a critical report? One agent creates your outline, another handles research, and a third reviews everything to ensure that your voice shines throughout.
Limitations of using AI agents
AI agents can make work more efficient, but they also come with challenges worth understanding and planning for. Here are some common pitfalls and how to avoid them:
- Hallucinations: Ever seen an AI sound confident but get it totally wrong? Agents can make things up when they’re missing context or using outdated info. Always double-check important details or ask for citations.
- Overtrust: Agents can seem so capable that it’s easy to forget they’re not perfect. Don’t rely on their outputs blindly—especially in sensitive areas. Treat results as drafts that need a human check before they’re final.
- Multi-agent dependencies: When several agents work together, things can get messy. Without clear roles, they might duplicate work or get stuck in loops. Define responsibilities and test workflows regularly.
- Bias: Agents learn from large datasets that can reflect unfair biases. Review what they produce and course-correct when needed.
- Data privacy and security: Agents rely on your info to help you, but sharing too much is like leaving your password manager unlocked. Use trusted tools, limit the use of sensitive data, and review privacy settings.
- Transparency: Agents can provide good answers without explaining how they arrived at them. Don’t just take their word for it—ask for sources or reasoning so you can understand the logic behind their output.
- Skill erosion: Overusing AI can make it easy to lose touch with your own skills. Let agents handle the repetitive stuff while you stay focused on strategy and creativity.
Here’s a quick summary of the main concerns, why they matter, and how to address them:
| Concern | Why it matters | Mitigation tactic |
| Hallucinations | AI-generated responses can sound accurate but be incorrect | Verify key details against reliable sources and request citations |
| Overtrust | Overreliance on AI can lead to mistakes or poor decisions | Treat outputs as drafts and apply human judgment before acting |
| Multi-agent dependencies | Poor coordination between agents causes duplication, inconsistency, or errors | Define clear roles for each agent and test outputs for consistency |
| Bias | Unintentionally reflect bias or produce discriminatory results | Review outputs regularly and provide corrective feedback |
| Data privacy and security | Sharing sensitive information increases risk of exposure or misuse | Use trusted platforms, limit confidential inputs, and review privacy permissions before sharing data |
| Transparency | AI reasoning can be opaque, making results hard to trust or audit | Ask agents to explain their reasoning or show sources |
| Skill erosion | Overreliance weakens your underlying skills and knowledge | Use agents for routine tasks while continuing to develop your skillset |
Using AI agents responsibly
AI agents can be powerful tools for learning and work, but they’re most effective when used intentionally. The goal isn’t to outsource your thinking, but to use agents to spark new ideas and take care of the tedious parts of your work. In practice, it means verifying AI outputs, being mindful of the information you share, and applying your own judgment before acting on or publishing AI-generated content. It’s also important to check your school’s or workplace’s AI policies to ensure you’re complying with established guidelines. For instance, many schools require properly citing AI-generated content, just as you would with any other source.
Getting started with AI agents
If you’re new to using AI agents, the key to success is iteration: starting simple, reviewing carefully, and building trust as you go. Here’s a breakdown of how to do that:
- Identify a repeatable task: To figure out a goal for your agent, think about something you do often, like scheduling meetings, summarizing content, or sending follow-up emails. These everyday tasks are perfect for testing how an agent can make your workflow more efficient.
- Choose a tool: Select one with agents designed for your task (like Grammarly for writing assistance). Ideally, the tool fits into your existing workflow, minimizing setup time.
- Start small: Test the agent on a low-stakes task to understand how it works and what kind of results you can expect.
- Review and refine: Carefully evaluate the output. If the results aren’t quite right, adjust your request or give the agent more context. Even minor tweaks can result in considerably improved outcomes.
- Scale up when it works: Once you’re confident in the results, expand your use—apply the agent to larger writing projects, research summaries, or more complex communication tasks.
The takeaway: Why AI agents matter
AI agents help you move beyond one-off assistance and into meaningful support for real work. By taking on repetitive and time-consuming tasks—and offering guidance on more complex work, such as planning, analyzing, and refining ideas—they free you up to focus on what matters most.
Whether you’re synthesizing information, organizing your thinking, or honing your communication, AI agents work alongside you to reduce friction and feel more in control of your workday. If you’re ready to experience what that kind of support can feel like, you can get started with Grammarly’s AI writing agents today.
Intro to AI Agents FAQs
What is an AI agent, and what does it do?
An AI agent is a software system that acts on your behalf to achieve a goal. It can plan tasks, carry them out, and adjust its approach based on feedback. Unlike AI chatbots or assistants that wait for prompts, AI agents can operate with more independence while still relying on your guidance and review.
Is ChatGPT an AI agent?
ChatGPT is not an AI agent on its own. It’s a conversational model that generates text responses to user prompts but doesn’t take independent action. However, it can power AI agents when integrated with tools or workflows that give it goals, memory, and the ability to act.
What are the five types of AI agents?
The five types of AI agents are simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents. These types build on one another—from basic rule following to reasoning and learning. Here’s how each of them works:
- Simple reflex agents follow preset “if-then” rules.
- Model-based reflex agents use limited memory to adapt to changing conditions.
- Goal-based agents plan actions to reach a specific goal.
- Utility-based agents weigh options to choose the most effective outcome.
- Learning agents improve their performance through feedback and experience.
What are some examples of AI agents?
Common examples of AI agents include:
- Customer service chatbots that answer common customer questions
- Recommendation agents that offer personalized suggestions to shoppers based on past behaviors
- Sales agents that help with lead qualification and sales outreach
- Productivity agents that schedule meetings, track tasks, and summarize notes
- Writing agents that help with drafting, editing, and brainstorming content
Does Grammarly have AI agents?
Yes! Grammarly offers AI writing agents that work alongside you throughout your workflow, in the apps and sites you already use daily. Rather than responding only to one-off prompts, these agents help you plan, draft, revise, and refine your writing using context about your goals, audience, and intent—so you can communicate more clearly, confidently, and effectively.
Check out our agent hub to learn more about Grammarly’s AI agents.
