This Will Change Your View on Agents

Rarely, if ever in my long writing career, have I been more excited about something than the thing I’m writing about today.

Because, finally, I get it. I get agents, and I get software (of its future, to be more exact). And I can assure you that, after reading, you will see things the same way I do.

That is, different from the rest.

Last week, we took a look at an agent’s first leg: how to provide the agent with the right context. This week, we’re looking at the other side, how to enable action.

But I’ve decided to take it a step further and show you, quite literally, what the future of software looks like.

I’m building a finance app for myself, but I’ve also made it ‘agentic’, meaning my personal agent is a first-class citizen of this app, once again proving that only when you get your hands dirty, you truly get to understand how things really are.

The app has a little bit of everything I’ve learned in AI over the years. Fully built with AI, context-engineering enabled, fully programmable by the user, using embeddings search for pretty cool use cases, AI-powered with a fully AI backend, and more. The app adapts to my constraints and evolves with me, creating a fully customized experience.

It’s too early to understand what I mean, but let me put it this way: software as we know it is dead.

At this point, I’m comfortable telling you that what you’re about to see is nothing you’ve ever seen before.

So the goal of today is two-fold:

Clarify. Explain why most agent limitations today are not the AI’s fault.

Open your eyes. Settle for good the concept of ‘agentic software’ and what it actually means, because guess what, even top AI Labs are thinking about this wrong.

What we’re seeing today has many implications for markets, for startups, for the AI industry as a whole, and, of course, most importantly, for you. I’m going to make you a builder, and that journey starts today.

The Tools side, more than it seems

Recalling our vision of what an agent is: an AI model that has some knowledge of the world and the context you provide, and, based on all of that, takes action on our behalf.

It’s literally a math equation: f(knowledge + context) = action.

And while today the knowledge component is largely out of our control because it’s dependent on AIs we don’t train (in the future, everyone will retrain their agents), the context component is definitely under our control, which is why context engineering has become such a popular term and why I dedicated an entire article last week to it.

In fact, agents are not so different in spirit from a standard chatbot; the difference between ChatGPT and your personal secretary is really nothing beyond the fact that the latter can take action via the use of “tools.”

But what is an agent tool? No, but seriously, what…

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