Do you ever feel like there just aren’t enough hours in the day? Maybe the phone is ringing, but you do not have enough employees to pick up the lines. Or perhaps there is a lot of paperwork that will take several hours to sort through.
If you can relate to these frustrations, you are not alone. In fact, this is the main reason that many businesses are now turning to the use of AI agents. AI agents help with these time-consuming but frustrating tasks, and best of all, there are many options to choose from.
But before you jump on board, you need to know what these digital assistants really are, how they work, and whether they’re the productivity superheroes they’re touted to be.
What Exactly Is an AI Agent?
At its core, Artificial Intelligence (AI) agents are basically digital assistants that perform tasks, make decisions, and solve problems with minimal human intervention. For example, if you’ve chatted with a website and received a quick reply, an AI agent was likely behind that response.
Fun Fact: AI agents do not have their own knowledge base. In order to complete subtasks, they tap into external databases, web searches, large language model (LLM) tools, and, yes, even other agents.
How Do AI Agents Work?
AI agents work in a few different ways. Most agents are given a starting persona. It is usually a friendly and helpful persona—for example, think about Siri or Alexa.
This persona can change as they learn more about who or what it is interacting with. For example, the AI may learn from conversations with you that you are a fan of pop music. It then takes this data and applies it to future interactions. This allows the agent to learn from past experiences to make improvements.
AI agents also use tools to help them access information, process data, or control systems. For example, a chatbot on a website could use past interactions to recommend products a user is likely to be interested in.
What Are the Different Types of AI Agents
AI agents aren’t a one-size-fits-all solution. There are six types of AI agents, each designed to operate differently depending on the task at hand. Let’s break them down.
Simple Reflex Agents
If you want an AI agent for easy tasks, a simple reflex agent is what you need. This AI agent type only works with a predefined set of rules, which means if there is anything outside of the specified event-condition-action rules, it will not react.
For example, consider there is an automatic door. A simple reflex agent may be programmed to open the door whenever a person is detected.
Model-Based Reflex Agents
Model-based reflex agents are more advanced than simple reflex agents. They also use sensors and actuators, but they also have an advanced reasoning component. This allows them to predict possible outcomes and build an internal model of supporting data.
For example, an industrial robot arm assembles products based on sensors. The sensor detects parts on the conveyor belt, and the arm picks them up. The machine can predict when future parts will appear and use this to help improve its work.
Utility-Based Agents
Utility-based agents focus on discovering the perfect solution by evaluating a particular utility.
For example, say you have a smart thermostat. The goal of the thermostat is to keep temperatures at comfortable levels without wasting energy. This AI agent factors in user preferences, current conditions, and energy costs. It then makes adjustments accordingly.
Goal-Based Agents
A goal-based agent uses advanced reasoning capabilities so that it can compare different possible paths to achieve its goal. A good example of a goal-based agent is a GPS system for vehicles.
Learning Agents
Learning agents are very complex. They can adjust their decision-making based on their experiences instead of just following set rules or guidelines. This makes it perfect in environments with unpredictable conditions.
A popular example of this is Amazon. When you search the platform and make purchases, Amazon collects data on your behavior. It uses this data to recommend new items it believes you will like. The more information it collects, the more specific it gets.
Hierarchical Agents
Hierarchical agents, as its name implies, work through a tier-level system. How does it work? Well, the higher levels simplify more complex tasks and then pass them to a lower tier. The lower tier then works on these tasks. The higher agents keep track of lower agents’ progress and make sure the goals are reached.
For example, a smart home system manages energy efficiency by monitoring overall home conditions. It could do this through creating smaller tasks like adjusting the thermostat, turning off lights, or managing security.
What Makes An AI Agent Different from Agentic AI and Traditional AI?
AI agents are different from traditional AI and agentic AI systems.
Out of the three, a traditional AI system is the simplest, focusing mostly on input-output tasks. On the other hand, agentic AI systems work towards certain goals and are always adapting based on the information they receive.
| AI Agent | Traditional AI | Agentic AI | |
| Reasoning Power | Reasons based on patterns | Relies on rules and patterns | Complex reasoning power |
| Decision-Making Process | Moderate Autonomy | Rule-based | Autonomous |
| Complexity | Moderate | Low | High |
| Adaptability | Adaptable | Manual Updates | Adaptable |
| Goal- or Task-Oriented | Goal-Oriented | Task-Oriented | Goal-Oriented |
Examples of AI Agents
You’ll find AI agents in finance, content creation, customer service, cybersecurity, and even healthcare. Here is a closer look at how AI agents are used in different industries.
For Content Writing
Whenever you see a product description, blog post, or social media caption, there’s a chance it was created with AI.
One example of an AI agent for content writing is Chatsonic. This tool creates social media content, articles, and other written content. Plus, it can integrate SEO keywords.
Fun Fact: Generative AI and Descriptive AI are different.
In Finance
AI agents in finance can help organize and automate tasks. For example, Orcolus helps sort documents, extract data, identify suspicious activity, and generate cash flow insights. It all works through an adaptable machine learning model that helps improve its work on tasks over time.
In Manufacturing
In manufacturing, AI agents can predicate when maintenance is needed, track inventory, and even manage energy use. It can also help detect errors and improve production quality.
For example, Performix offers manufacturing AI agents that help with areas like quality management, monitoring, automation, analytics, scheduling, and customization on the production line.
In Healthcare
AI agents built for the healthcare industry are specifically made to be HIPAA-compliant. For example, Regal makes a HIPAA-compliant AI agent that automates certain tasks like patient engagement and helps with employee scheduling.
In Customer Service
AI agents can provide customer support in real time, and you most likely have encountered them at some point. One popular example of an AI agent for customer service is Intercom’s Fin AI agent. This AI agent handles most of the frontline support, from chats to emails.
In Cybersecurity
In cybersecurity, AI agents protect your data even if you are moving between multiple work environments. They can also detect threats ahead of time so they can be dealt with before bigger issues arise.
One perfect example is IBM’s cybersecurity AI agents, which run analyses that speed up alert investigations and triage by about 55% and reduce the cost of fraud by as much as 90%.
In Sales
Another way you can use AI agents is for sales. These agents can help with a number of things, like capturing leads and making deals. For example, SalesCloser AI can make discovery calls and demos for you. Plus, it can communicate with potential clients in all different languages and adapt to other new and existing technologies.
What Are the Risks and Benefits of AI?
One of the biggest benefits is its ability to increase productivity and speed up processes that normally take much longer. However, there are also some risks, such as technical limitations, privacy, and ethical concerns.
AI Agents – Yay or Nay?
AI agents’ ability to learn, adapt, and operate with minimal human intervention makes them an obvious choice for many people. However, many people are still skeptical, and there are some risks of relying on them too heavily.
Do you consider AI agents to be useful? Let us know your thoughts in the comments below.