Types of AI Agents: The 13 Different Archetypes

Lindy runs through 13 types of AI agents, from the simple to the complex, and breaks down every single kind.

Last Updated: September 9, 2026

Table of contents

• Five Primary AI agent Types
• Eight Speciality Agent Types
• Agent Real-World Use-Cases
• Lindy
• Lindy Maps to Agent Types
• AI agent vs AI model
• Tips for choosing AI agents
• Frequently asked questions

You’ll find different types of AI agents built for different tasks. For example, conversational agents reply to texts and chats, planning agents can book meetings, and multimodal agents process voice, text, or images. Various AI agent types can also handle workflows across sales, support, recruiting, and ops. Understanding these categories helps you pick the right kind of agent for your use case.

Quick Note: AI Agents vs Traditional Software

An agent in artificial intelligence is like a digital worker who can reason through a task. In traditional terms, the word agent may refer to a background service or system helper — like a monitoring agent on a server.

Instead of running a pre-defined script, an AI agent can be trained or configured to:

• Interpret messy, unstructured inputs
• Decide what to do next based on context
• Take action, like sending a message, booking a meeting, or updating a database
• Adapt over time based on the knowledge base and prompts you provide

Where traditional scripts are brittle and linear, intelligent agents can handle ambiguity and make decisions. This is a significant shift from rule-based automation tools.

Next, let’s understand the foundational types of AI agents.

The Five Primary AI agent Types

There are five classic types of AI agents that form the foundation for how modern tools work. They are widely used in AI research and are still used in real-world workflows today.

1. Simple Reactive agents

These are the most basic kind of agents. They don’t adapt or have a memory. They just observe input, match it to a rule, and give you an output.

• Example: Auto-reply to emails for “out of office”

2. Reactive Agents, but Model-Based

These agents build a limited internal model of their environment. That means they don’t just react — they also track what they’ve seen before.

• Example: Navigation tools that remember blocked roads

3. Agents with Goals

Goal-based agents evaluate actions based on whether they help achieve a specific outcome. Instead of reacting blindly, they act with purpose.

• Example: A scheduling agent that knows its goal is to book a meeting, and takes steps to make that happen

4. Agents with a Specific Utility

Instead of just working toward a goal, they consider multiple possible actions and pick the one that offers the most utility — like speed, cost savings, or success rate.

• Example: A sales assistant that chooses to contact leads based on fit score and time zone

5. Recursive Learning Agents

These agents can improve over time by incorporating structured feedback, memory, or retraining loops — adapting their behavior based on what’s worked before.

The Eight Speciality Agent Types

While the classic agent types explain the logic behind how agents behave, most of what we see in businesses today are newer, hybridized versions. These AI agents solve tasks and execute workflows across platforms, tools, and teams.

1. Conversational Agents

These are what most people think of first — chatbots, Slackbots, or voice-based agents that respond to natural language.

2. Collaborative Agents

These agents respond to queries and work alongside humans or other agents to complete a task.

• Example: After a meeting, one agent writes the summary, another updates the CRM, and a third notifies the team on Slack

3. Planning Agents

Planning agents take a goal, break it into steps, and then execute those steps in sequence.

4. Autonomous Agents

Autonomous AI agents act independently. You set the task, and they figure out how to do it — sometimes looping through steps on their own.

5. Mobile Agents

Mobile agents move across different systems or environments. They’re not limited to one machine or domain — they operate across networks.

• Example: A security agent that scans different cloud servers or microservices for anomalies

6. Multimodal Agents

Multimodal agents can process and respond to more than just text. They can understand and interpret voice, images, or video.

7. Interface Agents

Interface agents sit inside UIs like sidebars, widgets, or embedded chat.

8. Hybrid Agents

Most real agents today don’t fit cleanly into one box. Hybrid agents combine multiple behaviors like planning, conversational, and utility-based agents to handle more complex flows.

Agent Real-World Use-Cases (By Type)

Teams use these automations to save time, reduce manual effort, and handle complex workflows. Here’s how different AI agent types show up in real work:

Reactive Agent: Auto-routing an email

A reactive agent scans the subject or keywords in an incoming email and routes it to the right folder, person, or system.

Utility Agent: Sales prioritization

Utility agent scores leads based on factors like location, job title, activity, or firmographic fit and pushes top-scoring leads to reps.

Learning Agent: Post-call coaching

A learning agent analyzes call transcripts, identifies gaps, and offers improvement tips based on historical patterns.

Lindy: A Different Kind of Agent

Lindy helps you combine multiple AI agent types into a single workflow. That’s what makes it more flexible and more useful for business operations. To see how these concepts function in practice, explore additional ai agents examples across various industries.

Here’s how it combines different agent types into one:

• Goal-based agents: Lindy operates with outcomes in mind.
• Utility-based agents: It doesn’t just execute tasks, it makes choices that optimize for value.
• Planning agents: Lindy can handle multi-step workflows.
• Conversational agents: Because it works across email, Slack, and voice, Lindy can communicate with users in natural language.
• Collaborative agents: Lindy isn’t siloed.

How Lindy Maps to Common Agent Types

The table below breaks down how Lindy aligns with different types of agents, and what that looks like in practice.

AI agent vs AI model: What’s the difference?

An AI model is the brain behind the AI agents. The agent uses that brain to execute tasks. An AI model is a trained system that can do tasks really well.
Think of the AI model as the brain, and the agent as the body.

So, how do you choose between types of AI agents for business automation? We’ve compiled a few tips for you.

Tips for choosing the right AI agent type for your needs

Not every workflow needs the most advanced AI agent. Here are a few simple questions to help narrow things down:

1. What’s the job you want done?

Are you trying to:

• Route and triage emails?
• Follow up with leads?

2. Does it need to work across systems?

If your workflow involves multiple tools — like Notion, Google Calendar, and Slack — prioritize collaborative agents and have strong integration support.

3. Is this a one-off task or a daily flow?

Your agent choice will depend on the tasks you aim to accomplish with it.

4. Do you need no-code or dev flexibility?

If you want to build without writing code, pick platforms that offer:

• Visual builders

5. What does “good” look like?

Define your version of success. Your success can be:

• Speed of execution

Frequently asked questions

What’s an AI agent?

An AI agent is a program that can understand and interpret inputs around it, make decisions, and take action based on the goal you define.

Which AI agent type is best for business automation?

Most businesses get the most value from utility-based and planning agents.

Are AI agents autonomous?

Some AI agents can be autonomous. Autonomous agents can operate without human input once they’re set up.

What kind of tasks can a reactive agent do?

These agents are great for basic rules-based tasks –– auto-replying to common emails, sorting tickets, or routing messages based on keywords.