When you hear the word “agent”, what comes to mind? Does the Jarvis created by Tony Stark come to mind? Or maybe the Red Queen of the Umbrella Corporation? Yes, they are AI from sci-fi movies but don’t worry, the real AI still has a long way to go— at least for the time being.
Right now, AI agents leverage LLMs (Large Language Models) to understand goals and come up with tasks and completions. You can, in fact, use AI agents for automation as you outsource cognitive tasks, making your life a whole lot easier. Talk about working smart, huh?
This technology may come off as foreign territory; you have very limited commercialized options for creating your own AI agents. But be sure to keep an eye out for it as technology advances faster than your dog can zoom around your house.
What Are AI Agents?
When you go to, say, for example, ChatGPT, you enter a prompt, and the AI model calculates a response based on your input. If you are not happy with the response or simply want a different output, you enter a different prompt. This is the process when you interact with an AI; there is always a human initiative for it to work.
AI agents work differently. They are independent entities that can perform tasks and make decisions on their own, without constant human input.
When compared with mainstream automation—where you set up a range of triggers based on data or system states and configure what happens next—AI agents can work in unpredictable environments where there’s a lot of new information. It’s artificially intelligent automation.
Pretty cool, right? They’re pretty much independently functioning machines, so it is like Jarvis but still in the learning phase. We’re getting closer to the moment when artificial intelligence can carry out tasks across any topic or field with complete flexibility and efficiency.
How Does AI Agents Work?
When you input your objective, the AI agent undergoes goal initiation. It sends your input to the core LLM (e.g., GPT-3.5 and GPT-4 currently in use) and provides the initial output of its internal thought process, demonstrating its comprehension of the required task.
Next up, it’s time to make a task list. This will help outline the steps needed to reach the goal and determine the best order for completing them. Once a solid plan is in place, then it’s time to start gathering information.
The agent can operate a computer like you and gather information from the Internet. I’ve also encountered some agents that can link up with other AI models to delegate tasks and decisions, allowing them to utilize image creation, geographical data analysis, or computer vision capabilities.
The AI agent stores and manages all the data, using it to give you feedback and improve its strategy as it goes along.
As items are marked as completed, the agent evaluates its distance from the goal by collecting input from external sources and internal reflection.
The agent will keep iterating, creating tasks, gathering information and feedback, and moving forward without pause until the goal is met.
This outlines the basic steps for a typical AI agent to achieve its objectives. The order of these steps may vary depending on how developers configure their agents. This specifically pertains to the agents used on computers or software platforms, and there are other types of AI agents that we will cover later on.
Real-Life Use of AI Agents:
- When Tesla released their very first self-driving cars, people had mixed feelings at first. It’s new, we get it. But now, they are all the rage in the automotive industry. They serve as a great illustration of AI agents, maneuvering a vehicle from one location to another while adhering to traffic regulations and staying on the road. With advancements in self-driving technology, vehicles may collaborate with each other and urban infrastructure, forming an integrated multi-agent AI system.
- In the future, we can envision smaller human teams working alongside large AI agent teams to enhance company efficiency. Humans will focus on strategy and nurturing relationships, while AI agents handle automation tasks, including interacting with personal or corporate/government AI agents.
AI agents can be employed in almost any task that doesn’t require higher-level strategic or creative abilities. Their reasoning is based on LLMs, so their capabilities are constrained by the model being utilized. As these models advance, AI has the potential to grasp nuances and understand the intricacies of achieving goals more effectively, making them well-suited for increasingly complex tasks down the line.
