All You Need To Know About Agents

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All You Need to Know About Agents

You are probably tired of hearing the mantra ‘2025 is the year of agents.’ It’s literally every tech and non-tech CEO’s only answer to basically any uncomfortable AI question.

But here’s the thing: no one has a fricking clue of what an agent really is. But today, we are solving just that by answering:

What are agents really?

What lies underneath this overused term?

What is the implementation framework you should always follow?

What are its main components?

What are the current limitations they must overcome?

And what are the most likely implementations of agents in 2025?

Today, we answer all that in the only way we know: explaining complex thoughts in simple words. By the end of this newsletter, you will be more prepared than the average CEO to confront this exciting new AI field and, crucially, how to call bullshit on their claims and those of “AI agent” companies trying to sell their latest lame product.

While absent of incomprehensible jargon, this newsletter requires some cognitive effort, so don't bother reading otherwise. However, you won’t be able to claim to be prepared for what’s to come. But if you read to the end, I guarantee you will gain unpayable intuition far beyond what almost any executive thinks he/she does.

And, importantly, the areas where you should be paying attention to.

But these executives, unknowingly, are correct. 2025 is indeed the year of agents. But instead of focusing on agents, you need to focus on yourself. “AIs won’t replace you; a human using AI will” is the most important lesson in AI, and 2025 is the year when you will have to decide on which side you’re on.

And that decision starts today.

Agents 101

First off, we need to provide a basic definition. Yet, the word ‘agent’ has become a rather loose term that people use for very different purposes, depending on how knowledgeable that person is about the space, and their marketing incentives.

Here’s to you what agents are succinctly.

An All-encompassing Definition… that Actually Isn’t.

A poor man’s definition of an agent is as follows:

AI models that can perform actions in your behalf.

Most people knowingly or unknowingly refer to this definition when discussing agents. However, this is not all; far from it.

Agents have a more accurate definition, primarily used for decades since the advent of Reinforcement Learning. Hence, while agents must meet the prior definition, we must complete it.

Agents and RL

The original definition of an agent is any AI model trained through Reinforcement Learning (RL) procedures.

An agent receives an instruction (or goal), observes and interacts with an environment, and executes a series of actions that, hopefully, maximize a cumulative reward. Naturally, the environment's constraints limit the agent's actions.

Simply put, the AI is trained to choose the concatenation of actions that lead to the best possible outcome, with “best” meaning the one that maximizes the expected cumulative reward. In…

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