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Access to artificial intelligence (AI) tools is rapidly evolving. In addition to predictive AI and generative AI, there’s a growing set of tools and features for businesses that incorporate AI agents.
An AI agent performs and completes tasks on its own based on a series of prompts and whatever it’s programmed to do. These already existed in some forms, like robotic, self-cleaning vacuums. You can integrate AI agents into any business to optimize your workstream, but they may also impact how people discover or buy from your site in the future.
Read on to learn what an AI agent is and how it differs from other AI technology, use cases, and what to consider when using AI agents for your business.
What is an AI agent?
An AI agent is a type of artificial intelligence system that works autonomously, making decisions based on its context and surroundings. AI agents can understand and respond to tasks by making decisions or solving problems to do so. For example, a self-driving car uses an AI agent to navigate.
Generally AI agents only need one prompt to complete their task, unlike an AI chatbot. They don’t need to be prompted with new instructions to make adjustments or reach their end goal. In the robotic vacuum example, it's pre-set to clean a specific surface after scanning the area. Every time it’s used, that’s the path it will take or area it will clean.
AI agents use a few technologies to do this. Similar to other AI technologies, such as generative AI, AI agents use natural language processing (NLP) and large language models (LLMs) to process information and make decisions. Some advanced AI agents can work through an issue by learning and adapting, trying out new solutions until they achieve their stated goal.
AI agents vs. chatbots
AI agents and chatbots use some of the same technology but work in different ways. A chatbot is a more fixed AI solution that’s good for handling conversation-style requests. They use pre-defined rules or training to formulate responses. AI agents are more flexible, because they evaluate their circumstances and make decisions based on their surroundings.
For example, chatbots in customer service often have questions or prompts for a user, like, “Do you need information about shipping?” If you answer yes to the question, the chatbot will serve you an article, link, or further information on shipping from a brand. This is because an AI chatbot follows rules and matches patterns or uses keyword recognition.
AI agents vs. agentic AI
An AI agent is a specific software program designed to work independently. It’s a singular digital assistant working toward one goal or objective. However, it’s possible for multiple AI agents to work within an AI system. This is called agentic AI.
Agentic AI has the potential to be more powerful, because there are multiple agents working on complex tasks at once. Together, they can tackle more decision-making and examination of their environment.
An AI agent and agentic AI share very common traits and benefits. Both gather information, work autonomously, and make decisions. The key difference to remember is complexity. A single AI agent makes decisions within its specific parameters. Agentic AI can handle complex workflows, multiple decision points, and different interactions to achieve its goals.
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What do AI agents mean for businesses?
The wide variety of AI agents available can change the way you run your business and the ways that customers might interact with your business.
For small business owners and entrepreneurs, agentic AI can be like a virtual personal assistant across your business. Whether you seek out a standalone tool or it’s built into software you’re already using, agentic AI can help you:
Streamline marketing: Analyze shopping behavior on your website to automate and personalize emails to your customers, or draft and update website copy independently.
Support customers: Enhance customer support, like personalized product recommendations or customer service chats, without sacrificing quality or reliability.
Business strategy: Analyze your sales and website data to automate financial tracking and apply strategic recommendations like price changes.
Inventory management: Track stock levels, predict sales demand, and automatically reorder materials or products to ensure you never run out.
It’s also important to be aware of how agentic AI might impact how customers discover your products or services. Agentic AI web browsers could use someone’s browsing and purchase history across brands to make decisions. That means a web browser could recommend products to someone, compare them, and buy them, even if that customer never visits your website themselves.
Consider how you might adapt to that reality by optimizing your site for AI and sharing reviews of your offering.
5 types of AI agents
Not all AI agents are the same. The following are a few different types of AI agents you can integrate into your business, with varying levels of complexity.
1. Simple-based
A simple-based AI agent doesn’t really “know” the world around them, and can only react to a single stimulus or action. Think of the smart prompt-based customer service chat example. It must know what someone is looking to do, and if there are any questions out of the norm, it can redirect from there, but it’s very simple in its task and goals.
2. Model-reflex
A model-reflex AI agent is more sophisticated and autonomous because it can perceive the environment it’s in, whether physical or digital, and fill in gaps or make decisions to complete that space.
A robot in a shipping warehouse is a good example: It uses floorplans, functional rules, and sensors to navigate a route safely and reach its various destinations.
3. Goal-based
Goal-based AI agents are powerful because they can evaluate their circumstances and make choices based on the predicted consequences or reactions to their behaviors.
An inventory management system powered by agentic AI, for example, might have the goal of optimizing your inventory to minimize costs and match customer demand. It would analyze sales data and trends for your various products and adjust inventory orders accordingly to achieve those goals.
4. Learning
A learning AI agent is a great option for businesses. This type of agent will continue to learn and improve through reinforcement to achieve a pre-set goal.
For example, if you sell apparel with a virtual try-on component, your learning AI agent would get to know your customer better through reinforcement of their wants and needs. Over time, it could make more personalized recommendations to each customer, ultimately increasing satisfaction and sales.
5. Utility-based
A utility-based AI agent makes decisions to achieve its objectives by evaluating different scenarios. Consider your GPS navigational systems in your car or on your phone. There are a number of different paths or solutions, and this agent type is great at deciding on which is the best for efficiency and safety.
For business owners, this might come into play with dynamic pricing. An AI agent could constantly evaluate the base cost of goods, demand, competitor pricing, and inventory and automatically adjust your product pricing to ensure the maximum profit margins.
Best practices for using AI agents
No technology is without its weak points. If you’re looking to onboard any AI agents for your business, keep best practices around oversight and security in mind to make the most of any tools.
Set clear goals and objectives. Generally speaking, AI needs to have precise prompts, goals, or objectives to complete any task. Know what you’re using your AI agent for and what it may need for effective decision-making before you get started.
Maintain human oversight. Every task or objective that your AI agent completes should have some kind of human oversight. Monitor responses, choices, or solutions your agent serves up to ensure they’re high quality.
Follow data privacy and security practices. AI needs an enormous amount of data to predict, generate, or automate anything. Don’t make sensitive information or personal customer information readily available to your AI agent.
Potential risks and limitations of AI agents
AI is advancing constantly, but there are still risks and limitations to look out for when using any AI tools.
Keep these limitations in mind:
Lack of context and hallucinations: LLMs can experience “hallucinations,” or fabricated and blended information. If you’re relying on an AI agent to process data or share information with a customer, this is a real concern. Fact-check and provide human support if this happens.
Technical errors: If your AI agent’s goal is to find efficiencies in your operations, it may take that a little too seriously. An AI agent might do whatever it takes to achieve its objective and cut corners that impact customer experience.
Bias: AI is trained on data. The quality of that data and any biases it might have matters. Some human oversight is crucial to make sure your AI agent isn’t producing anything insensitive or overly biased.