In today’s article we’ll dive into the fascinating world of AI agents. As AI agents become a more integral part of our Lives. It’s evolving from Simply responding to our commands to understanding and acting autonomously.
So let’s unpack what AI agents are, how they work and why they might be a game changer in your life and career. What is an AI agent? An AI agent is a piece of software designed to perform tasks autonomously unlike traditional software that follows strict rules.
AI agents make decisions based on their understanding and interactions with the world. They use Technologies like large language models such as GPT from open AI, Claude from anthropic or Gemini from Google to process and understand information and determine the best course of action.
Imagine having a digital assistant, instead of giving them an order and saying ask this person if they’re available on this date and then send them a calendar. Yes they would perform the task but you’re given a kind of preset list of instructions of what they need to do.
Instead you could give something a goal and more ambiguous and say hey I need to book some time with Joanne whenever I’m free in the next month or so can you go about organizing the schedules then the AI agent can go and take that understand the objective and then think of a list of other things it needs to do step one check your calendar for availability step two check Joann’s calendar for availability step three determine the amount of time step four and so on.
So they act more autonomously and can understand an objective rather than follow a very specific set of rules. AI agents are different from large language models. While agents use models like GPT for understanding and generating language, agents can do much more than you see traditional language models predict responses. That is because LLMs are trained based on data that is static because they were trained on the internet and a bunch of other resources but at a specific Moment In Time
Large language models don’t interact with the world beyond their training data, for example chat GPT knows information only up until its last update, as of today they are able to fetch real time data from the internet but don’t go beyond the scope of data they are trained on.
AI agents are essentially sophisticated problem solving machines that can plan, execute and learn from their actions. They are made up of several components in particular the ability to plan, the ability to interact with tools, the ability to have memory and store knowledge and then lastly the ability to execute actions.
So let’s take a look at each one, planning everything starts with a goal when it’s researching a market Trend or perhaps drafting an email. An agent Begins by defining what needs to be achieved it then creates a detailed plan breaking down the goal into manageable tasks much like the Chain of Thought approach in prompt engineering.
This means that agents not only knows what to do but also how to approach each task for optimal results, ultimately it takes the human training and predefined triggers kind of out of the process and comes up with them on its own, secondly interacting with tools unlike basic language models, AI agents can interact with a variety of tools this is part of their interacting with the external world around them they can browse the internet access databases and use apis to gather information or perform tasks.
This integration allows them to extend their capabilities far beyond just being a static data set thirdly they can have memory or access external knowledge. Agents can also be equipped with specific and specialist knowledge for example your company’s data or market research that perhaps is not publicly available. They use techniques like retrieval augmented generation i.e RAG which is integrating external resources and leveraging in the ability to go and capture this information and then bring it into the language model’s responses.
It effectively enhances the responses with more up-to-date or relevant information, for example if you go to a startup’s website and type in a question, instead of the language model just trying to answer your very specific customer question with what it was trained on in this generic model. it’s going to go and use retrieval augmented generation to search the database of possible questions and answers from that company’s help desk integrate that answer into the LLM response for a more up to-date and accurate response
Lastly, AI agents can execute actions so they can write reports or make emails and even manage other software applications. We are also entering a world where agents can start communicating with other agents who have been specifically trained to perform certain things. This autonomous execution is what sets them apart from more passive Technologies and is really where people are thinking wow, what can we do?
Where we can automate work to the extent where we just explain what we want to happen and the rest is taken care of, now the future of AI does pose some risks because now AI agents represent significant advancements in how we interact with technology but they can’t act independently. The fact that they come up with their own plan of tasks that they must execute and then can act upon those tasks autonomously could pose a threat.
So there is an element of control and interaction that humans have to maintain over this process to really get good quality results. Some people think that as models go from GPT 4 to GPT 5 and so on their reasoning capability is going to be far more improved and help the output of AI agents become higher quality.
AI agents are differ from LLMs, they can plan, they can interact with tools, store memory and access other knowledge and also execute actions on your behalf. So if there’s anything else you would like to know just drop a message down in the comments and I would be happy to respond with that thank you very much if you haven’t already please subscribe.
