Types of AI Agents (With Examples)

If you’ve been exploring how to bring AI into your work, you’ve probably heard terms like AI agents, autonomous agents, or reflex agents floating around. This post will explain what these terms mean, and how these different agent types show up in the tools marketers and SaaS teams are already using today.

Types of AI Agents

Simple Reflex Agents

These are the most basic type of AI agents. They act only based on the current input they receive—no memory, no learning, no goals.

How they work:
They follow an “if-this-then-that” model.

Real-world example:
A motion sensor that turns on a light when someone walks by.

Where you may see them at work:

• Email automation rules in platforms like HubSpot. For example, "If a user clicks a link, tag them as engaged”.
  
  
• Chatbots that deliver pre-set answers based on keywords.
  
  
• Basic lead routing tools that assign contacts to reps based on territory or business size.
  
  

Model-Based Reflex Agents

These agents are a bit smarter. They build an internal model of the world to make more informed decisions.

How they work:
They track previously acquired information to avoid repeating mistakes and respond more intelligently.

Real-world example:
A Roomba that remembers where your furniture is and avoids it next time.

Where you may see them at work:

• Conversational AI bots that remember previous interactions to personalize follow-ups.
  
  
• Email platforms that optimize send times based on past behavior.
  
  
• Lead scoring models that update dynamically based on user behavior on your website or product.
  
  

Goal-Based Agents

These agents don’t just react—they act with specific goals in mind.

How they work:
They evaluate different possible courses of action, and choose the one that brings them closer to their goal.

Real-world example:
A GPS that recalculates the best route to your destination if you miss a turn.

Where you may see them at work:

• Sales and marketing engagement platforms that prioritize next best actions to reach pipeline goals.
  
  
• Campaign builders that A/B test different paths to optimize conversions.
  
  
• SEO tools that guide you toward creating content that ranks.
  
  

Utility-Based Agents

These agents go beyond goals—they aim to maximize value (a.k.a. “utility”) while achieving goals.

How they work:
They balance tradeoffs—like cost vs. speed vs. quality—and optimize accordingly.

Real-world example:
A travel booking site that recommends a flight that’s not just the shortest but also the cheapest and most convenient.

Where you may see them at work:

• Programmatic ad platforms that optimize across channels to get the best ROI.
  
  
• Customer journey optimization tools that choose when and where to show the right message.
  
  
• Pricing tools for SaaS that adjust plans dynamically based on usage, competitor pricing, or user segment.
  
  

Learning Agents

These agents learn over time—improving performance based on feedback or results.

How they work:
They evaluate past actions, learn what worked (or didn’t), and adapt. While similar to a model reflex agent, the difference is these agents actively improve over time, versus just using their model to act smarter.

Real-world analogy:
A chess-playing AI that gets stronger every time it plays a new opponent.

Where you may see them at work:

• AI copywriting tools that adjust tone and format based on brand style and past edits.
  
  
• Ad platforms that optimize bidding and targeting over time.
  
  
• Customer success tools that learn what actions predict churn and recommend retention strategies.
  
  

Agentic AI (Modern Autonomous Agents)

But first, a note on terminology:
All of the above agents are technically autonomous in the traditional academic sense–they act independently in response to their environment. But in today’s conversations, “autonomous agents” or “agentic AI” usually refers to a new class of AI that can plan, reason, and execute complex tasks independently—often using tools, APIs, or chaining multiple steps together without being told what to do at each step.

Agentic AI is the most advanced form of AI agents used in practice today. These agents can operate independently toward a high-level goal, often across systems.

How they work:
They take a goal, break it into subtasks, use tools or external data, and make decisions on their own as they go. They're not just reactive—they're proactive.

Real-world example:
An AI agent that can research a competitor, write a positioning memo, and schedule an email marketing send—without you guiding each step.

Where you may see them at work:

• Agent.ai agents that automate complex workflows like competitive research, campaign reporting, or customer onboarding.
  
  
• GPT-style agents that plan and execute content strategies, manage CRMs, or launch sequences in HubSpot.
  
  
• Growth agents that monitor metrics like sign-ups or conversion rates and autonomously suggest or implement tests.
  
  
• Product-led growth (PLG) assistants that monitor in-app behavior and personalize onboarding or upsell offers in real time.

Get Started Using Agents

We’re at the beginning of a shift. AI agents are no longer just scripts or chatbots—they’re becoming more like digital coworkers who can take on meaningful work in marketing, growth, customer success, and beyond. Understanding the different types of agents available to you and what they’re capable of helps you spot the next big opportunity to get more value out of your tools and time.

You may find after reading this article you’re already using simpler AI agents, and didn’t even know it. Hopefully you’re also inspired to experiment with using some more advanced agents that can give you a real edge and eliminate multi-step tasks. If you have a few minutes right now, it’s a good time to check out the Agent.ai marketplace and tinker with some agents and see which ones might inspire you to fold in more agentic AI into your day-to-day.