The Expectation vs The Reality
I walked into Google Cloud NEXT ‘26 expecting what most of us did bigger models, better demos, and another round of “AI is getting smarter” announcements. And to be fair, that part delivered. Gemini improvements were everywhere, benchmarks were higher, and the demos looked smoother than ever.
But as I moved past the surface-level excitement and started paying attention to the deeper conversations, something felt off.
It wasn’t that the announcements were underwhelming. It was that they were pointing to something much bigger than what was being said out loud.
By the end of the event, I wasn’t thinking about models anymore.
I was thinking about architecture.
And more importantly, I was thinking about how wrong we’ve been building AI systems until now.
The Realization: It Was Never Just a Model Problem
Over the past couple of years, I’ve built what many developers have built—agents that try to do everything. You give them tools, expand their context, refine prompts, and hope they behave consistently.
And in demos, they usually do.
But in real-world systems, the pattern repeats itself:
they slow down, they become unpredictable, and they’re difficult to trust.
At some point, you stop blaming the model.
You start questioning the system around it.
That’s the shift NEXT ‘26 triggered for me. It made it clear that the problem wasn’t intelligence—it was how we were structuring it.
The Quiet Death of the Monolith Agent
One of the biggest insights didn’t come as a headline announcement, but it was everywhere if you connected the dots.
The idea of a single, all-powerful agent is fading away.
In its place, we’re seeing the rise of micro-agents—small, focused systems that each handle a specific responsibility.
This felt very familiar.
It’s the same transition we went through when we moved from monoliths to microservices. Back then, we learned that putting everything into one system makes it fragile and hard to scale.
Now we’re learning that lesson again—but this time with intelligence itself.
Instead of one agent trying to monitor costs, enforce policies, generate fixes, and communicate results, we split those responsibilities. Each agent becomes simpler, faster, and more reliable.
And more importantly, the system as a whole becomes easier to reason about.
When Agents Started Talking to Each Other
Once you break intelligence into smaller pieces, the next obvious question is: how do they work together?
This is where Agent2Agent (A2A) becomes important.
At first, it doesn’t sound revolutionary. But the more you think about it, the more it feels foundational.
We’ve spent years building integrations through APIs—writing glue code, handling edge cases, and maintaining fragile connections.
A2A shifts that entirely.
Instead of calling endpoints, agents discover capabilities and delegate tasks dynamically. The system becomes less about predefined integrations and more about collaboration between intelligent components.
It reminded me of how HTTP changed the internet. Suddenly, everything became composable.
That’s what A2A feels like for AI.
The Moment Autonomy Started Feeling Safe
One of the biggest concerns I’ve always had with agents is simple: what happens when they act incorrectly in production?
Until now, the answer has been to limit them—restrict their actions, reduce their scope, and keep humans in the loop.
But the introduction of the GKE Agent Sandbox changes that equation.
Agents can now generate and execute code inside an isolated environment. They can test their decisions, validate outcomes, and only then apply changes.
That’s a completely different level of capability.
It’s no longer about giving agents predefined tools. It’s about allowing them to create and use their own tools—safely.
For the first time, autonomy doesn’t feel risky.
It feels engineered.
The Insights Most People Overlooked
While most of the attention went to models, the most important shifts were happening deeper in the infrastructure.
Virgo Network: Solving the Coordination Problem
One of the less talked-about innovations was the Virgo network.
At scale, AI systems don’t fail because of compute—they fail because of communication. When multiple agents need to collaborate, the network becomes the bottleneck.
Virgo addresses this by turning the network into a high-speed fabric designed specifically for AI workloads.
It’s not flashy, but it’s critical.
Because intelligence at scale isn’t just about thinking—it’s about coordination.
BigQuery as a Reasoning Engine
Another shift that stood out to me was how BigQuery is evolving.
We’ve traditionally moved data into AI systems for processing. But that approach is expensive and often unreliable.
What’s emerging now is the opposite: bringing reasoning directly to the data.
Agents operate within BigQuery, analyzing relationships and structured information without unnecessary data movement. This makes systems more efficient and significantly more accurate.
It also reduces one of the biggest issues with AI—hallucination—by grounding decisions in real, structured data.
A Real Scenario That Changed How I Think
To make all of this more concrete, I started thinking about a real-world use case.
Imagine a sudden spike in cloud costs.
In a traditional setup, you’d get an alert, open dashboards, investigate logs, identify the issue, and then fix it manually.
It’s reactive and time-consuming.
In this new architecture, the flow is completely different.
An agent monitoring data in BigQuery detects the anomaly and understands it. It passes the task to another agent through A2A. That agent checks policies and constraints. A third agent generates a fix, tests it in the sandbox, and applies it safely.
And instead of overwhelming you with information, the system presents a simple, context-aware interface asking for approval.
Everything happens seamlessly.
No dashboards. No digging.
Just a decision.
That’s when it really hit me—this isn’t just automation.
It’s autonomous systems working together.
The Hype vs What Actually Matters
There’s a lot of excitement right now around “vibe coding”—the idea that you can describe an application and have it generated instantly.
And while it’s impressive, it also feels misleading.
Because building software has never just been about writing code. It’s about maintaining systems, ensuring reliability, and managing complexity.
That’s where the real focus should be.
What actually matters—and what will define the future—is inference economics.
If every decision an agent makes is expensive, these systems won’t scale. But if inference becomes cheap enough, multi-agent architectures become practical.
That’s the real unlock.
Not just smarter models—but affordable intelligence.
The Future of Interfaces Feels Different
One of the most interesting ideas I came across was A2UI (Agent-to-User Interface).
It challenges a fundamental assumption we have as developers—that interfaces are static.
In this new model, they’re not.
The interface is generated dynamically by the agent, based on the context of the task. If you need to approve something, the agent creates exactly the UI you need in that moment.
Nothing more. Nothing less.
No dashboards. No navigation.
Just the interaction you need.
And then it disappears.
This changes the role of frontend development entirely.
We’re no longer building fixed interfaces—we’re designing systems that generate them.
What I’m Taking Back From NEXT ‘26
I didn’t leave Google Cloud NEXT ‘26 thinking about better models.
I left thinking about better systems.
This shift isn’t about chatbots or assistants anymore. It’s about building systems that can observe, reason, act, and collaborate reliably.
And maybe the most important realization is this:
We’re no longer trying to make machines respond better.
We’re learning how to make them work together.
Final Conclusion: This Isn’t an Evolution — It’s a Reset
Walking away from Google Cloud NEXT ‘26, one thing is hard to ignore:
This wasn’t about improving what we already had.
It was about replacing it.
For years, we’ve been trying to make AI fit into our existing patterns—APIs, dashboards, monolithic systems, and tightly controlled workflows. We wrapped intelligence inside familiar structures and called it innovation.
But what NEXT ‘26 made clear is this:
AI doesn’t fit into those systems. It reshapes them.
We’re moving from:
- single agents → coordinated micro-agent systems
- API integrations → capability-driven collaboration (A2A)
- static interfaces → dynamic, generated experiences (A2UI)
- model-centric thinking → system-level design
And perhaps most importantly:
We’re shifting from software that responds…
to systems that decide and act.
That’s not a small step forward.
That’s a different category entirely.
The Mindset Shift That Matters
If there’s one mindset change developers need to make, it’s this:
Stop thinking in terms of features.
Start thinking in terms of autonomous flows.
Because the value is no longer in:
- what your system can do
It’s in:
- what your system can handle without you
The New Question You Should Be Asking
The old question was:
“How do I build this feature using AI?”
The new question is:
“What network of agents can solve this problem end-to-end?”
That’s a much more powerful question.
And a much harder one.
Final Line
The era of chatbots is over.
The era of coordinated intelligence has begun.
And the developers who understand this shift early…
won’t just build better apps.
They’ll build the systems everything else depends on.