I didn't expect Google to make one of the more interesting announcements in the AI agent space by bringing a competitor's models into the picture.
But that's exactly what happened.
On October 8, at Gemini at Work 2026, Google introduced its new Gemini agent. It can work across business applications, write and run code, and coordinate complex tasks.
That's interesting on its own. But one detail caught my attention.
The Gemini agent can use Anthropic's Claude models alongside Google's own Gemini models.
Yes, Google is building an AI agent that doesn't have to rely exclusively on Google's AI models.
And I think that says something important about where AI development is heading.
So, what exactly did Google announce?
Think about how most of us interact with AI today.
We open ChatGPT, Gemini, or Claude, describe a problem, get a response, and then figure out what to do with that response.
Even when AI writes useful code or analyzes a document, we usually handle the next steps ourselves.
Google's new Gemini agent is trying to move beyond that.
Instead of simply responding to prompts, it's designed to complete tasks across the tools people already use.
According to Google's announcement, the agent can:
- Work across Gmail, Docs, Sheets, Drive, and Calendar.
- Write and execute code.
- Connect with third-party tools and business systems.
- Coordinate specialized AI agents for different tasks.
- Maintain context across devices and longer-running workflows.
- Select different AI models depending on the task.
The last point is what makes this particularly interesting.
Wait... Gemini can actually use Claude?
Apparently, yes.
Google confirmed that its new agent can orchestrate tasks using both Gemini and Anthropic's Claude models.
That doesn't mean Claude is replacing Gemini, or that every Gemini product automatically gives users access to Claude.
The important distinction is that the agent and the underlying AI model are separate parts of the system.
The agent manages the workflow.
The model handles the reasoning or generation required for a particular step.
Google's approach allows the system to choose between supported models based on what it needs to accomplish.
For example, imagine an agent working on a software project.
It might need to:
- Understand the requirements.
- Read documentation.
- Generate or modify code.
- Review the changes.
- Prepare a summary for the developer.
Different models may perform differently across these tasks.
Instead of forcing every step through one model, a multi-model system can select a more suitable option.
That's the idea behind model orchestration.
And personally, I find that more interesting than another benchmark comparison.
Why this matters for developers
We've spent the last few years comparing AI models.
Which one writes better code?
Which one has stronger reasoning?
Which one is faster?
Which one costs less?
Those questions still matter, of course.
But when you're building an actual product, choosing the best model isn't always as simple as picking the one at the top of a leaderboard.
A model that's excellent at complex reasoning might be unnecessary for a basic classification task.
A faster, cheaper model might be perfectly suitable for summarizing short documents.
And a coding-focused task might benefit from a different model than the one used for general conversation.
This is why I think multi-model development deserves more attention.
1. Less dependence on one model provider
Imagine building an application where every AI feature depends on a single provider.
If pricing changes, performance drops, or a particular model becomes unavailable, your application might be affected.
Having a model-independent architecture gives developers more flexibility.
It doesn't eliminate vendor lock-in completely. The orchestration platform itself can become a dependency.
But it potentially makes changing the underlying model easier.
2. Better cost management
Not every user request needs the most expensive model.
For example, a support application might handle thousands of simple questions every day but only occasionally receive a complicated technical issue.
Using an expensive reasoning model for every request could become costly.
A routing system could use a lightweight model for straightforward tasks and a more capable model when needed.
Of course, routing decisions also have a cost. You need to account for latency, token usage, and the complexity of evaluating different models.
Still, it's a promising design pattern.
3. More freedom to experiment
This might be my favorite part.
Developers shouldn't have to rebuild entire applications every time a better model becomes available.
Ideally, we should be able to test multiple providers, compare their outputs, and choose what works best for our specific use case.
Not what works best in a generic benchmark.
What works best in our actual application.
A simple example of how model routing works
Let's say we're building an AI-powered developer assistant.
It handles documentation questions, code reviews, and complex debugging problems.
A simplified routing system could look something like this:
// Conceptual example, not Google's actual API
function selectModel(task) {
switch (task.type) {
case "documentation":
return "fast-model";
case "code-review":
return "coding-model";
case "complex-debugging":
return "reasoning-model";
default:
return "general-model";
}
}
async function handleTask(task) {
const model = selectModel(task);
return await runModel(model, task.prompt);
}
Nothing revolutionary about the code itself.
The interesting part is the architecture.
Instead of building your entire application around one model, you build a system that can work with different models.
In production, things get more complicated.
You'd need authentication, provider adapters, retry handling, structured outputs, cost tracking, and proper evaluations.
You'd also need to make sure that switching models doesn't silently break features that depend on specific output formats or tool-calling behavior.
But the basic idea remains useful.
The application should own the workflow. The model should be a replaceable component whenever practical.
There's another part of this announcement worth discussing
Google isn't just talking about switching models.
The new Gemini agent is also designed to coordinate multiple specialized agents.
This is where things get a little more complicated.
Imagine asking an AI assistant to prepare a technical report about your application.
One agent could analyze code changes.
Another could examine issue-tracking data.
A third could prepare a summary.
The main agent would coordinate the work and bring the results together.
That sounds convenient.
But anyone who has built even a relatively simple automation knows that connecting several systems introduces new ways for things to fail.
What happens when one agent returns incorrect information?
What if two agents reach conflicting conclusions?
What if an agent accidentally gets access to information it shouldn't have?
These aren't small implementation details.
They're exactly the problems developers need to solve before autonomous agents can be trusted with important workflows.
The part I'm still skeptical about
I'll admit that the idea of AI agents coordinating tools and models is exciting.
But I'm not convinced that adding more agents automatically makes a system better.
Sometimes a straightforward script with a few API calls is more reliable than a complicated agent workflow.
And sometimes a single model is perfectly adequate.
There's also the question of transparency.
If an AI platform automatically chooses a model, how much visibility does the developer have into that decision?
Can we inspect which model handled a particular task?
Can we measure the cost and latency of each step?
Can we reproduce the same workflow when debugging a failure?
Can we control where sensitive information is sent?
For enterprise applications, those questions are just as important as raw AI performance.
Model orchestration is useful only when developers can understand, evaluate, and control it.
Is this the future of AI development?
I think it could be part of it.
Not because Google has suddenly solved AI agents, and not because using multiple models is a completely new concept.
Developers have already been experimenting with model routing and multi-agent systems.
What's interesting is seeing a major AI provider explicitly position its own agent as something that can operate across competing models.
It suggests that the competition may gradually shift.
Instead of competing only on who has the smartest model, companies may also compete on who can build the most useful systems around those models.
Systems that understand context, connect to real tools, execute tasks reliably, and give developers meaningful control.
That's a much bigger engineering challenge than building a chatbot interface.
And it's probably where some of the most interesting developer problems will emerge.
Final thoughts
The biggest takeaway from Google's announcement isn't simply that Gemini can use Claude.
It's that the AI model and the AI application are becoming increasingly separate decisions.
For developers, that could mean more flexibility when designing AI-powered products.
But it also means we'll need to get better at orchestration, evaluation, security, and understanding how our systems behave when things go wrong.
I'm curious to see how well Google's approach performs outside controlled demonstrations.
Because supporting multiple models sounds great on paper.
Making them work together reliably in production is the real challenge.
What do you think?
Would you rather build an application around one powerful AI model, or use multiple specialized models for different tasks?
I'd love to hear how other developers are approaching this.
Sources
- Google Cloud — Welcome to Gemini at Work 2026: Introducing the Gemini agent
- Google Cloud — The new Gemini Enterprise: One platform for agent development
Based on Google's October 8, 2026 announcement. The code example is conceptual and does not represent Google's official implementation.
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