Understanding how AI agents work

Do you argue with ChatGPT?

I know I’ve found myself doing it a few times! And when I do it looks like this: CAPS LOCK, a lot of !!!!!!!!!!! and words like: why don’t you get me? Are you stupid? Try again! No no, and NO! STOP!…

You know the drill—you ask for something, it gives you an answer, and suddenly, it’s not what you wanted. So, what do you do? You try rephrasing. You add a little more detail. Maybe even throw in a “please” and “thank you” for good measure (he he). But nope—still not quite there. So once again, CAPS LOCK,a lot of !!!!! … And yet, on the other side, there it is—still polite, still trying to help, never losing its cool. That’s usually the moment I remember… yep, definitely not a human I’m talking to. 

Now, don’t get me wrong—we’ve got a pretty good thing going. It’s my number-one work buddy, my go-to assistant, and honestly… I can’t imagine a day without having a little chat with it! Even if it drives me up the wall sometimes, it saves me a lot of time and I know it’s got my back when it counts.

But there are some moments where I wonder if it’s really listening. That is why it is so important to learn how to use it, how to train it, how to communicate with it. And to do that, you need to learn how it works first. So, let’s do that. Let’s learn. 🙂 

This month at Solveo, we’ve been focusing on AI agents and how they are transforming the way we interact with technology. If you missed our first blog about how AI evolved into agents, you can read it here. In this post, we’ll look closer at what makes AI agents work and how they operate.

Once again, what is an AI Agent?

Okay, let’s give it a definition again, but from a different perspective now. An AI agent is a sophisticated tool that combines multiple capabilities powered by large language models (LLMs). These agents can perform a wide variety of tasks, from simple actions like answering questions and finding information to more complex activities like writing detailed articles, conducting in-depth research, managing schedules, photo generation, designing, videos, creating personalized recommendations, automating customer support, processing large datasets, and even making decisions in real-time based on changing conditions. Everyone can agree that AI agents represent a major step forward in artificial intelligence, making LLMs more versatile and dynamic in their applications.

The role of function calling in AI Agents

One of the key components that allow AI agents to operate is function calling. Function calling involves sending an input to an LLM (such as GPT-4 or Google Gemini) and receiving an output generated by the model. This simple process is the core of how AI agents interact with language models and perform tasks.

Here’s a breakdown of the key elements in function calling:

1. System Message
2. User Message
3. Assistant Message

Let’s take a closer look at each component

1. System Message: This is the instruction that helps the LLM understand what it needs to do. It sets the context for the task and can define how the model should behave. For example, it could tell the model to act as an expert in a specific field, helping it respond appropriately to the user’s request.
2. User Message: This is the message the user sends to the system, asking it to perform a task. It could be a direct question, a prompt, or a request for information.
3. Assistant Message: This is the output the LLM generates based on the system and user messages. It’s the AI’s response or the result of the task the user requested.

And here’s a simple example of how function calling works

Let’s look at another example where the task is to write a short description of a favorite book:

• System Message: You are an AI assistant skilled at writing brief and compelling book descriptions.
• User Message: Write a description of “To Kill a Mockingbird” by Harper Lee.
• Assistant Message: “To Kill a Mockingbird” is a powerful novel about racial injustice in the American South. Through the eyes of young Scout Finch, readers witness the struggles of her father, Atticus, as he defends an innocent man wrongly accused of a crime. The book explores themes of empathy, morality, and the loss of innocence.

In this scenario, the system message provides guidance to the AI, letting it know that it should write brief, yet impactful, book descriptions. The user message specifies the book to focus on, and the assistant message is the description that the AI generates based on those inputs.

This process illustrates how the AI follows the given instructions, tailoring its response to the task at hand. It may be a LinkedIn post, short tweet, Reddit comment, copy for Instagram Carousel, writing a book summary, or even composing a full blog post, website copy etc. The system, user, and assistant messages work together to enable the AI to perform the task accurately.

Simple function calling forms the backbone of many common applications, such as chat interfaces and text summarization. These straightforward uses highlight the essential strengths of large language models (LLMs): they can respond to questions, clarify information, and condense lengthy content into concise summaries. These applications showcase the fundamental capabilities of LLMs when integrated through function calling, making them accessible for daily tasks and communication needs. However, while this process can handle many simpler functions effectively, it also reveals some of its limitations when it comes to handling more complex tasks that require multi-step reasoning, advanced problem-solving, or continuous learning.

Key Characteristics of AI Agents

• AI agents are autonomous; they can perform tasks without constant human help, making their own decisions based on programming and inputs from their surroundings.
• They are reactive, responding to real-time data and changes around them, allowing for flexible interaction and adjustment.
• AI Agents have reasoning and decision-making abilities, using smart algorithms to analyze data, consider options, and choose actions to reach specific goals.
• They are capable of learning through methods like deep learning and reinforcement learning, continually improving based on past experiences and new information.
• Many AI agents are communicative, using natural language processing (NLP) to interact effectively with users or other systems.

Functions of AI Agents

AI agents perform various functions to make them more capable and effective in different tasks.

• They start by gathering information from their surroundings, like reading data from sensors or tracking user actions. This data helps them understand what’s happening in their environment, making it easier to respond in ways that are useful and relevant.
• After understanding the data, AI agents can take specific actions based on what they’ve learned. This could mean updating a system, answering questions, sending notifications, or even controlling physical devices, depending on the task they’re handling. They act in a way that’s intended to meet a specific goal or solve a problem.
• AI agents analyze past data to make educated guesses about what’s likely to happen next. They learn from patterns in past actions and outcomes, which helps them adjust their approach over time. For example, if they notice that one type of response is more helpful, they’re more likely to use it again in similar situations.
• When facing complex issues, AI agents work through different options to find the most effective solution. They look for ways to optimize processes or make tasks simpler and more efficient. This problem-solving ability helps them adapt to new challenges and find solutions even when things don’t go as planned.

Workflow of an AI Agent

An AI agent typically follows a structured workflow. 

• It starts by setting a clear goal, like automating customer support or analyzing market trends.
• It then gathers relevant information from users or other systems through APIs or direct input. 
• The agent processes this data to find insights and patterns. 
• Based on its analysis, it decides on the best action to take, using specific algorithms suited to its task. 
• Finally, it carries out the action, like responding to a question or updating system settings.

Conclusion

So, what do you think? 

Was this blog created by AI, or a human has written it? 

Does it really matter if a blog is written by AI or a human?

Is there a difference as long as it’s useful, easy to follow, and keeps you reading? 

I don’t think so either. But, want my opinion? The best work comes from the teamwork between AI and humans, bringing out the best of both worlds. And yep, that’s exactly how this blog came together.