Systems Talking to Systems: The Part of 2026 That Still Amazes Me

The biggest shift I have felt in 2026 is systems talking to systems.

AI agents. Software working with other software. A request becoming a series of actions while I watch.

I know computers have been communicating with one another for decades. APIs, scripts and integrations are not new. What feels different to me is being able to explain what I want in ordinary language and watch an agent work through systems I would otherwise struggle to navigate.

I keep watching Codex work for me and having the same reaction:

“Wait. What? How did you just do that?”

The AWS feeling

AWS has always felt intimidating to me. There is a lot there, and I have never felt that I properly understood the whole place.

When I have something to do there, I can describe the task to Codex and watch it work through it. Instead of my first job being to understand every menu and service, I can start with the outcome I need.

That changes how the work feels.

I still need to know what I am asking for. I still care about what changes. But the distance between “I need this done” and “I can see how to approach it” feels much smaller.

I have had that same sense of surprise around Google Cloud and other systems. These are environments where I would normally expect to spend a long time finding my bearings. Watching an agent find a path through them is something else.

Even the small things get me. It opens a URL directly, and I am sitting there thinking, “That is going to be a 404.” Then the page opens.

Such a small moment. Such a strange feeling.

The work between the systems

When I say “systems talking to systems,” I mean more than software exchanging data. I mean an agent helping turn an intention into work across the tools involved.

As a builder, a lot of the effort is in the transitions. Understanding what one system expects. Finding the right place in another. Connecting the pieces. Working out why something that looked straightforward is not behaving as expected.

That work can absorb so much attention before you even get back to the feature or the business problem you started with.

Watching an agent move through those steps makes me think differently about what I can attempt. Something that used to feel like a wall begins to feel like a task I can describe, investigate and work through.

I am not saying every hard problem has suddenly become easy. I am describing a change in my own sense of possibility.

And then it starts testing

The part that both impresses me and makes me laugh is what happens after the initial implementation.

I ask for a feature, explain what I want it to do, and watch the first version take shape. Then it starts testing. And testing. And testing.

In my head, the feature took thirty seconds and the tests took an hour.

That is how it feels, not a stopwatch result. The same goes for the “three thousand tests” reaction: I am watching the checks multiply and wondering where it thought of all those cases.

“Man, where did you get all these tests?”

There is something fascinating about seeing a machine keep working through possibilities after the part I thought of as the main job appears to be finished.

Of course, the number of tests is not the same as proof that a feature is right. What matters is whether the checks cover the behavior people need, whether failures are understood, and whether the result actually works. But the willingness to keep checking is one of the things that makes the experience so striking.

A different starting point for building

Sometimes the speed makes me reach for ridiculous comparisons: work that feels as though it should take a large engineering team months, suddenly moving in front of me.

That is amazement speaking. It is not a measured claim that an agent can replace fifteen engineers or compress every six-month project into a minute.

The useful change is more concrete. I can begin with a goal, explain the constraints, and get help working through unfamiliar systems. I can spend more of my attention deciding what is worth building and whether the result makes sense.

For me, that is the practical lesson: try delegating a real, bounded task that has been slowed down by an unfamiliar tool. Describe what should be true when it is finished. Be clear about what the agent is allowed to change. Then look at the work and the evidence together.

You learn more from watching a specific task unfold than from asking whether AI can “do everything.”

I am still amazed

If I call this the best thing that happened to humanity in 2026, understand the spirit in which I mean it. That is a builder talking after watching something he finds extraordinary, not a ranking of every human achievement this year.

What I mean is that this has been one of the most exciting changes I have personally experienced.

I can explain an intention. An agent can help carry it through software. Systems that once felt difficult to approach become places where I can make progress.

I still have to think. I still have to decide. I still have to care about the result.

But I keep watching the work happen and thinking:

“What? Are you serious?”

Systems talking to systems. That is the part I cannot stop thinking about.

What unfamiliar system would you most like help working through?

Adapted from my voice note with AI-assisted editing. The experiences and reactions are mine; the illustrations are conceptual, not product screenshots.