Types of AI Agents Explained: How They Work and When to Use Each

Key takeaways

• AI agents are designed for different kinds of work, from fast, rule-based tasks to adaptive, learning-driven workflows.
• Understanding agent types helps you choose better tools, set realistic expectations, and diagnose issues when outputs fall short.
• More complex work often requires multiple agents working together, with clear roles and coordination.
• No single agent can do everything well, and human judgment remains essential.
• You can start using AI agents today by applying them to familiar workflows and iterating based on feedback.

AI agents are changing how people write, research, plan, and get work done. But “AI agent” isn’t a single capability—it’s a broad category that includes tools designed for very different kinds of tasks. Some agents are built for speed and consistency. Others are designed to reason, adapt, and make decisions over time.

When you know how agent types work and what they do best, you can choose tools that fit your goals, design smarter workflows, and get better results. This guide will help you understand the major types of AI agents and how to use them effectively in your daily work, starting with understanding what exactly an AI agent is.

Table of contents

• What is an AI agent?
• What are the main types of agents?
• How do the different types of AI agents compare?
• How do AI agents work together?
• Why understanding the types of AI agents matters
• Common mistakes when using different AI agent types
• What are the limitations of different agent types?
• How to start using AI agents
• Making AI agents work for you
• Types of AI agents FAQ

What is an AI agent?

An AI agent is a system that can take action to achieve a goal by observing its environment, making decisions, and acting on them. That agency separates them from simpler forms of automation.

A practical example of this is Grammarly’s AI agents. Unlike most AI assistants, these specialized writing agents are not automated and don’t need a prompt to work—think of them as always-on collaborators and a personal team of helpers that can proactively offer dynamic, real-time suggestions as you work, helping you at every stage of the writing process. Integrated seamlessly within your workflow, Grammarly’s AI agents deliver relevant, context-aware feedback based on the type of writing and the audience you’re writing for, getting you unstuck while allowing you to focus on high-impact thinking.

How do AI agents differ from chatbots or assistants?

Chatbots and AI assistants are primarily designed to respond to user input. Once you prompt them, they generate an answer. You need to keep prompting them to get answers; they generally don’t take initative or act without specific instructions.

AI agents, by contrast, are designed to act under certain conditions. They can monitor information, evaluate options, and trigger actions, often without direct user input at every step. Multiple agents can also work together to accomplish a more complex task than a single agent could reliably handle on its own.

How do AI agents work at a high level?

At a high level, most AI agents follow a simple loop: They perceive what’s happening, decide what to do, act on that decision, and, in some cases, learn from the outcome.

The details vary depending on the type of agent, but this core cycle remains consistent. For a deeper look at this process, explore our guide on how AI agents work.

What are the main types of agents?

AI agents come in different types, based on how they decide what to do next. Some react immediately to what they detect, while others plan ahead, weigh trade-offs, or improve their behavior over time.

Common agent types include simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents. Beyond individual agents, systems can also combine agents through multi-agent, hierarchical, or hybrid architectures.

The sections below walks through these agent types and explains how they work, followed by a look at different ways of organizing them to work together.

What is a simple reflex agent?

A simple reflex agent responds to specific inputs with predefined actions. It doesn’t use memory or context, and it doesn’t adjust its behavior over time. The agent reacts to certain inputs but doesn’t consider meaning or intent, making it effective for narrow, repetitive tasks. In practice, this might look like a writing system that flags spelling errors as you type based on known language patterns.

Many simple reflex systems don’t feel like what people typically think of as AI. Your wall thermostat, for instance, is technically a simple reflex agent—it just follows basic “if cold, then heat” logic. While it may feel like a stretch to call this AI, understanding this category helps us appreciate other sophisticated types of AI agents.

What is a model-based reflex agent?

A model-based reflex agent also responds to inputs based on tightly defined rules, but it also takes short-term context into account.

In practice, this might look like a proofreader that ignores further instances of a grammatical issue you dismissed earlier in the document. This tracking helps you get more relevant feedback as you work, but the memory is temporary. Once you close the document and open a new one, it will start flagging that same issue again.

What is a goal-based agent?

A goal-based agent makes choices from multiple available options. Rather than reacting immediately, it considers possible steps and chooses those that best help it achieve a specific goal.

For example, if a project-management agent is given the goal of delivering a report by next Friday, it might create tasks, assign owners, request missing inputs, follow up on overdue items, and flag blockers. As long as the report ships on time, the agent considers the goal met, even if the components, like task assignments or final output approval sequence, weren’t efficient or balanced.

What is a utility-based agent?

A utility-based agent builds on goal-based behavior by asking not just “Did we hit the goal?” but “What’s the best way to get there?” It weighs trade-offs between options and chooses actions that lead to the strongest overall outcome.

Continuing the example above, a utility-based project management agent would go beyond assigning tasks and following up. It would consider factors like workload balance, individual strengths, and competing deadlines, then adjust assignments to keep the project on track without overloading the team. As things change, it can reevaluate and shift its plan accordingly.

What is a learning agent?

A learning agent is an AI agent that improves its decisions over time by learning from feedback and past outcomes. Instead of just asking, “Did I do what I needed to?” or “What’s the best option right now?” it also asks, “What can I do better next time?” to help it learn from the overall experience.

In our project-management example, a learning agent wouldn’t just deliver a report efficiently. It would notice patterns over time, such as recurring bottlenecks, consistently optimistic timelines, or contributors who need more support. The next time a report is due, it applies those lessons by adjusting assignments, timelines, and workflows to make each project run more smoothly than the last.

How do the different types of AI agents compare?

Now that we’ve looked at each agent type individually, it helps to compare them side by side. The table below shows how the five main types of AI agents differ in how they make decisions and the kinds of tasks they’re best suited for.

How do AI agents work together?

In the real world, AI agents don’t usually work alone. More advanced systems often use multiple agents working together to tackle bigger, more complex problems. The main differences between these approaches come down to how they split up the work—and you can often mix and match these strategies depending on what you’re trying to accomplish.

Here are the most common ways AI agents are organized to work together:

• Multi-agent systems: Multiple agents work in parallel or sequence, each handling a specific role. This team-of-equals approach allows them to break complex work into smaller parts, but it also introduces coordination challenges when agents reach conflicting conclusions or duplicate efforts.
• Hierarchical agents: Higher-level agents set goals and direction, while lower-level agents carry out specific actions. This structure is better at managing complex workflows but can be less adaptable when unexpected situations arise.
• Hybrid systems: Different types of agents are combined within a single system. For example, reflex agents might handle routine tasks, while goal-based or learning agents manage more complex decisions. This approach balances speed and adaptability by matching agent types to the work they do best.
• Agent orchestration: AI agent orchestration sits above individual agents and manages how they work together. It determines when agents act, how outputs move between them, when work needs to be reviewed or redone, and where human oversight fits in. Orchestration helps ensure quality, consistency, and efficiency across multi-agent systems.

Why understanding the types of AI agents matters

Understanding how different AI agents make decisions gives you more control over how you use AI in your work. Instead of treating AI as a black box, you can make more intentional choices about the tools you use, how you use them, and what you expect from them.

With this knowledge, you can:

• Match the right tool to the job: Some agents are great at quick, repeatable tasks, while others are better at planning or learning over time. Knowing the difference helps you set realistic expectations and avoid misusing tools.
• Choose tools that fit how you work: Instead of asking a simple agent to handle complex decisions, or expecting a learning agent to be perfect on day one, you can select agents that align with the task at hand.
• Figure out what’s going wrong, faster: Many AI tools rely on multiple agents working together behind the scenes. When something feels off, understanding agent roles helps you identify whether the problem lies in pattern recognition, planning, learning, or coordination.
• Build workflows your team will actually use: When you understand how different agent types complement each other, it becomes easier to build workflows that feel reliable, intuitive, and scale across your team.

Common mistakes when using different AI agent types

When AI agents don’t behave the way you expect, it’s often a sign that the agent doing the work is the wrong type for the task or has been set up incorrectly. Learning to recognize these patterns makes it easier to adjust your approach and get more reliable results.

Here are some common mistakes to watch for, along with ways to address them:

• Using reactive agents for complex work: Simple or model-based reflex agents work well for fast, repeatable decisions, but they struggle with tasks that require planning or prioritization. For more complex work, switch to or pair them with goal-based or utility-based agents.
• Expecting improvement from non-learning agents: Some agents are designed to behave consistently rather than adapt. If results never improve over time, consider introducing a learning agent or adding a feedback loop that allows the system to adjust based on outcomes.
• Seeing inconsistent outputs: Agents that weigh trade-offs may produce different results even with the same inputs, especially if they rely on large language models (LLMs), which introduce a degree of randomness. When consistency is critical, reflex agents are often a better choice.
• Overrelying on agents for judgment calls: Goal-based and utility-based agents can support decision making, but even well-defined goals may leave out important context. Be sure to maintain human oversight, especially before acting on an agent’s output.
• Blurring responsibilities between agents: In systems with multiple agents, failures often occur at handoff points. Clearly define which agent is responsible for each decision and how outputs move between them to improve reliability and coordination.

What are the limitations of different agent types?

No single type of agent can do everything well, and some actions should probably be left to humans. Understanding their limitations helps you use agents more effectively and design workflows that play to their strengths.

Here are some common limitations to keep in mind:

• Rule-based agents don’t adapt on their own: Simple and model-based reflex agents are reliable because they’re designed to produce consistent outcomes. This makes them ideal for repeatable tasks, but it also means they require manual rule changes to handle new or more complex situations. For adaptive behavior, a different agent type is needed.
• Dependence on clear objectives: Goal-based and utility-based agents perform best when goals, constraints, and trade-offs are clearly defined. When objectives are vague or priorities conflict, these agents can struggle to produce useful results.
• Learning agents are only as good as the feedback: Learning agents improve based on the signals they receive. Inconsistent, infrequent, or contradictory feedback can slow learning or reinforce the wrong behaviors.
• Coordination overhead in multi-agent systems: You can address more complex workflows by combining agents, but now you’re also managing handoffs, dependencies, and new ways things can break. Without clear coordination, the added complexity can quickly outweigh the benefits.
• Ongoing need for human involvement: No agent type fully replaces human judgment. The more nuanced the work gets, the more you’ll need people reviewing outputs, providing guidance, and intervening when necessary.

Treat these limitations as design considerations, not roadblocks. When you account for how different agents work, where they need support, and how they’re coordinated, AI systems become easier to trust and more effective to use.

How to start using AI agents

Now that you know what the main types of AI agents are meant to do and various ways to coordinate them, you can start using them more intentionally. You don’t even have to build one to start—they’re likely already in the tools you already use.

Here’s a simple process to get you started:

• Start with a familiar workflow: Choose tasks you already know well, like reviewing drafts, coordinating projects, or tracking follow-ups. Identify where an agent could help and what kind of behavior you want it to handle.
• Match the setup to the agent’s role: If you need fast, predictable responses, such as catching words or phrases forbidden by your brand guidelines, configure a simpler agent with clear rules and boundaries. If you want an agent to plan, weigh options, or adapt over time, expect a more complex setup, and build in extra guidance and review.
• Evaluate the results: Review outputs regularly and adjust instructions or agent configuration as needed. If you are using a learning agent, corrections, approvals, and rejections all serve as feedback that shapes future behavior, so be deliberate and consistent.
• Design with multiple agents in mind: Once you’re comfortable with a single agent, try stringing them together, with each agent handling a different part of the job. Being explicit about who does what makes the system easier to adjust and debug when issues arise.
• Stay involved where judgment matters: Even well-configured agents can make mistakes, especially as tasks evolve or conditions change. Regular check-ins and human oversight help ensure outputs remain accurate and appropriate.

Starting with tools you already trust, and learning how their agents behave in real workflows, makes AI agents easier to adopt and more valuable over time.

Making AI agents work for you

Just as people on a team bring different skills to their work, each type of AI agent has its own strengths and limitations. You would not ask an accountant to design graphics or a copywriter to forecast market demand, and the same principle applies to AI. Using agents effectively means choosing the right one for the task and understanding what it is designed to do well.

Grammarly is one example of how multiple specialized AI agents work together to enhance your day-to-day workflows. These specialized agents coordinate behind the scenes to deliver dynamic, context-aware suggestions based on what you’re writing, who you’re writing for, and what you want to achieve. As you use them across your apps, documents, and wherever you do your most important tasks, Grammarly’s AI agents help refine elements like tone, conciseness, and logical progression—so you can express your ideas more clearly and confidently without losing focus on the work that matters most.

When agents are matched thoughtfully to their roles, they can also work together to handle more complex tasks than any single agent could manage alone. By understanding trade-offs such as consistency, adaptability, and speed, you can set realistic expectations, design better workflows, and get more value from AI agents overall.

Types of AI agents FAQ

What are some common types of AI agents?

Common types of AI agents include simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents. These names are based on the different ways agents make decisions, from reacting to inputs to planning, optimizing, or improving over time.

Which type of AI agent is most commonly used today?

Simple and model-based reflex agents are the most commonly used types of AI agents today. They power many everyday features that apply rules consistently or use short-term context without needing to learn or adapt over time.

Do I need technical skills to use AI agents?

You don’t need technical skills to use AI agents in modern tools. Many products embed agents behind the scenes, allowing you to benefit from their decision-making without configuring or managing them directly.

What’s the difference between AI agents and AI chatbots?

AI agents make decisions and take actions to achieve a goal on their own, while AI chatbots primarily respond to user prompts. Chatbots focus on conversation, whereas agents are often designed to monitor information, evaluate options, and act across workflows.

Does Grammarly have agentic AI and AI agents?

Yes. Grammarly uses agentic AI through a system of AI writing agents designed to actively support you across the entire writing process. Instead of responding only to one-off prompts, Grammarly’s AI agents work continuously alongside you—helping you plan, draft, revise, and refine your writing.

These agents use context such as your goals, audience, and intent to take informed actions and deliver more relevant, consistent guidance—so you can communicate more clearly, confidently, and effectively.

Check out our agent hub to learn more about Grammarly’s AI agents.

Which type of AI agent is best for beginners?

Simple and model-based reflex agents are best for beginners. They behave consistently, are easy to understand, and work well for clearly defined tasks that don’t require ongoing feedback or tuning.

Can different types of AI agents work together?

Yes, different types of AI agents often work together within the same system. Many tools combine agents with different decision styles and coordinate them through architectures or orchestration to handle more complex tasks.