This is a submission for the Google Cloud NEXT Writing Challenge
At Google Cloud NEXT ’26, something clicked for me — and it honestly wasn’t what I expected.
It wasn’t a new model, a faster API, or one of those polished demos that look great but don’t really change how you build things.
It was the realization that I was still thinking in the wrong abstraction.
While Google was showing systems that operate over time, coordinate across tools, and make decisions with context, I caught myself still thinking in endpoints, requests, and features.
That gap is where the real shift is happening.
We Didn’t Just Get Better AI — We Got a Different Layer of Software
For years, even as AI got better, our mental model didn’t really change.
Most systems still worked like this: user sends a request, system processes it, returns a response. Even with LLMs, we mostly just swapped out deterministic logic for probabilistic outputs and called it a day.
But what was presented at NEXT doesn’t really fit that anymore.
These systems don’t just respond. They keep context over time, coordinate multiple agents, and keep doing things even when no one is actively interacting with them.
That doesn’t feel like “AI inside your app.”
It feels more like something that’s just… running.
Source: Google Cloud NEXT ’26 — Official Announcement
From Output to Execution
The biggest shift is easy to miss, but once you notice it, you can’t unsee it.
We’re moving away from systems that are judged by how good their output looks, toward systems that are judged by what they actually do.
Generating a nice answer is one thing.
Actually executing a task across multiple systems — with permissions, constraints, and changing context — is a completely different problem.
And you feel that difference immediately when you try to build something like this.
Because suddenly it’s not about “did the response look right?”
It’s about “did the system actually do the right thing?”
You’re Not Just Writing Code Anymore
This is the part that hit me the most.
If you take this seriously, your role as a developer shifts.
You’re not mainly writing endpoints, functions, or UI flows anymore.
You’re defining responsibilities. You’re deciding who (or what) is allowed to do what, how decisions move through the system, and what should happen over time when different parts interact.
At some point it stops feeling like assembling logic…
…and starts feeling like designing behavior under constraints.
Multi-Agent Systems Look Clean — Until You Build Them
On paper, multi-agent systems look almost too clean.
You split things up nicely: one agent plans, another evaluates, another executes. Each has a clear role, everything is modular, everything makes sense.
Until you actually try it.
Because then you realize: complexity didn’t go away. It just moved.
Instead of one complex system, you now have multiple smaller systems that need to agree with each other.
And they don’t always do that.
You can easily end up in situations where:
- one agent thinks something is ready to execute
- another thinks it still needs clarification
Both are “right” in isolation. The result is still wrong.
No crash. No error. Just weird behavior.
That’s a very different kind of problem.
Context Is the Real Bottleneck Now
For a long time, we all focused on models. Bigger, smarter, faster.
But lately it feels like the bottleneck is somewhere else.
Context.
Not just having data, but having the right data, in the right shape, shared consistently across everything involved.
Because if different parts of the system operate on slightly different context, things start drifting fast.
Without a solid context layer, agents don’t really “understand” anything. They just make reasonable guesses.
With it, they start to behave in a way that actually feels grounded.
Memory Changes the Nature of the System
Stateless systems are simple. Every request is its own thing.
Stateful systems are… not.
As soon as you introduce memory, everything changes a bit. The system starts carrying history. Decisions are influenced by things that happened before, sometimes in ways that aren’t obvious anymore.
That’s powerful, but also a bit uncomfortable.
Because now you’re not debugging a single execution anymore.
You’re trying to understand a chain of decisions that led to a certain outcome.
Governance Becomes a Core Design Problem
Another thing that becomes obvious pretty quickly: once systems can act, control becomes critical.
Not just “secure your API” kind of control.
Actual decision control.
Who is allowed to do what?
Which actions are valid?
What happens if something goes wrong?
This is where identity, permissions, and traceability stop being “enterprise stuff” and become core to the system.
Without that, autonomous systems aren’t just powerful — they’re kind of dangerous.
Debugging Becomes About Decisions, Not Code
This is probably the weirdest shift.
In normal systems, something breaks and you trace it back to a line of code.
Here, everything can technically work — and still be wrong.
The issue isn’t that something failed. It’s that different parts of the system interpreted the situation differently or acted on slightly different context.
So you’re not really debugging code anymore.
You’re debugging decisions.
Where Most Teams Are Still Thinking Too Small
Right now, a lot of implementations still treat AI as a feature.
Something behind an endpoint. Something inside a UI.
But that framing feels… outdated.
Because the real shift is deeper.
The system itself becomes the AI. The UI is just one surface.
What actually matters is what’s happening behind it — how agents coordinate, how context flows, how decisions are made over time.
That’s where things get interesting.
Final Thought
Google Cloud NEXT ’26 didn’t just introduce new tools.
It introduced a different way of thinking about software.
Not as something that reacts to input…
…but as something that acts, coordinates, and evolves over time.
The real question isn’t whether you’ll use AI in your system.
It’s whether you’re ready to build systems where behavior — not just code — is the main thing you design.
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