5 of the Most Useful Types of AI Agents for Businesses

5 Types of AI agents for businesses:

Maybe you’ve heard of AI agents before. If you haven’t, AI agents are AI tools capable of performing tasks on their own, without being specifically prompted to do each one. The idea is that they can handle simple, repetitive business tasks to save time.

If you’re considering using AI agents in your own business, you’ll find that there are numerous types of agents available, and you may be unsure which to choose. Those types differ in how much context they use, how independently they make decisions, and whether they can improve over time, so the right fit depends on the complexity of the tasks you want them to handle.

To help you out, we’ve put together this overview of the five main types of AI agents for businesses, with specific examples of each one.

Read on for the full overview!

5 types of AI agents for businesses

There are several different AI agent types available to businesses like yours. Get more details about each of those types — along with specific AI agent examples — below!

1. Simple reflex agents

The most basic type of AI agent is a simple reflex agent. Simple reflex agents use a minimalist “if/then” logic, taking specific, preset actions in response to particular triggers. Although this might not quite fit what you imagine when you think of AI, it’s the foundation for many automation tools.

Examples of simple reflex agents might include Gmail’s email filtering system (or a similar tool), which sorts emails based on specific keywords or senders. Another example might be a monitoring system designed to track server loads or equipment conditions, automatically shutting down machinery if danger is detected.

2. Model-based reflex agents

Model-based reflex agents are a more advanced version of the simple reflex agents we just looked at, and they enter more into the territory of what we would typically think of as AI.

These agents are built to handle partial observability, using AI models — not dissimilar to those used by chatbots — to retain a limited understanding of context. For example, you might use a customer support chatbot that’s able to remember the last few questions and give more context-aware answers as a result.

For a more specific example of a model-based reflex agent, you could look at a tool like NetSuite Inventory Management. Though not primarily an AI agent, this ERP platform does feature some agentic capabilities that allow it to analyze inventory trends, decide when replenishment is needed, and even initiate procurement actions.

3. Goal-based agents

Goal-based agents are assigned a specific goal, and then use planning and search capabilities to help them find the right sequence of actions to achieve that goal. When people talk about agentic AI, they’re most often talking about tools that are at least this advanced.

An example of a goal-based agent would be a travel booking AI agent identifying the cheapest flight path to a destination, or a logistics agent planning a delivery route. A specific tool that fits into this category is AutoGPT, which is directly built and marketed as an agentic platform that runs on OpenAI’s GPT model.

4. Utility-based agents

Utility-based agents build on goal-based agents, but take it a step further. These tools prioritize actions that maximize utility, enabling them to handle conflicting goals or uncertain outcomes. That’s vital for businesses looking to optimize their processes.

As an example, you might use a marketing campaign agent to help you allocate your budget across different channels to maximize your return on investment (ROI). Or, you might use a trading agent designed to balance risk and potential profit. On some level, Google Ads Smart Bidding might even be classified as a utility-based agent.

5. Learning agents

Learning agents, which constitute the most advanced category of agentic AI, are designed to improve their performance over time based on feedback. The interesting thing about this is the fact that agentic learning capabilities can actually be added to any of the previous AI agent types on this list.

Some learning AI agent examples might include a personalized recommendation engine that learns from user clicks, or a fraud detection system that automatically adapts to new scam patterns over time.

A sales forecasting system is one example of this type of system in action — it can learn from historical pipeline and revenue data over time to improve its predictions.

How to choose the right AI agent type for your business

The best type of AI agent for your business depends entirely on what you’re trying to do. If you’re trying to pick which one to use for a given task, consider the following factors:

• Workflow complexity: How complicated is the workflow you want to automate?
• Context requirements: How much context will the agent need to perform the task well?
• Decision-making level: Does the agent need to make decisions or weigh tradeoffs?
• Ongoing improvement: Should the agent’s performance improve over time?
• Degree of autonomy: How much autonomy do you trust the agent with?

Answering these questions will help you judge which type of agent to use. By looking at how each option’s capabilities match up with your needs, you can identify which type of agent would be the most effective for the task you’re trying to automate.

Get help setting up your AI agent with consulting services from WebFX

If any of the AI agent examples on this page have piqued your interest, you may be considering setting up an AI agent or two for your business. If so — and particularly if you want to set up AI agents for your marketing — consider taking advantage of WebFX’s deep marketing expertise to help you along.

With our AI agent development and consulting services, you can ensure your agents are implemented smoothly and safely, and that they’ll bring the greatest benefit for your company. Interested in working with us? Just give us a call at 888-601-5359 or contact us online today!