AI Agents

Artificial Intelligence, AI, is a big part of everyday life. Whether it’s used to solve a simple issue query, such as “how to cook a 5-Cheese Ziti Pasta” to more complex requests such as “code me a website”, AI models are being used everywhere. One of the most interesting types of AI is called an AI agent, which became more prominent as time passed and more developments were made. An AI agent is a computer program that can make decisions and take actions to reach a goal. It works kind of like a helper that can think, learn, and respond to situations. 

The difference between an AI model with Agents and without Agents includes the use of real-time information. AI models are trained using Large Data Sets, which are limited to a certain point in time. Agents allow real-time queries to be resolved as well. Recently, I was looking for plane tickets for Spring Break, and instead of scouring the internet for the perfect flight, I asked an AI model to find real-time prices in my budget and a span of 4 days in which the weather is sunny. A wait of a few seconds saved hours of my time. 

To understand how AI agents work, let’s dive into the ticket scenario. First, we look at what is happening around us. Then we look at what is available to us. After that, we come up with a plan, and finally, we take action. AI agents follow a similar process. They observe, plan, decide, and act. The first step is observation. An AI agent collects information from its environment. This could be data from a website, sensors in a robot, or user input like typing a question. For example, a chatbot reads the message you send it. Next comes looking at available tools. The Agent looks at the tools and resources available for it to use to best solve the query. An example of this ranges from an API for the weather to a live updating API for stocks. Next comes decision-making. The AI agent uses rules, patterns, or learned knowledge to figure out what to do. Some agents follow simple instructions written by programmers. Others use machine learning, which means they improve over time by learning from data. This helps them make better choices in the future. After deciding, the agent takes action. This could be sending a reply, moving a robot, or recommending a product. The action is meant to help achieve a goal, such as answering a question or completing a task.

In our case, the AI understood what was going on/what was asked of it. The AI then asked the Agent to execute the task, and the agent looked at the tools available to it; the agent should have had weather APIs and Flight Price Tracking information to give the most up-to-date information. The Agent then looks for the required flight in the time frame with the correct weather and returns the best choice. The result is then printed for me, and then I go get my wallet. 

Many AI agents also have a feedback loop. This means they learn from the results of their actions. If something works well, they remember it. If something fails, they adjust. Over time, this makes them smarter and more useful.

Today, AI agents are used in many areas, including customer service, healthcare, gaming, and transportation. They help save time, reduce errors, and make systems more efficient.

In the future, AI agents will likely become even more powerful. They may handle more complex jobs and work more closely with humans. Understanding how they work helps us use them wisely and prepare for the changes they bring.