Google's Next AI Bet Isn't on Chatbots. It's on Agents That Do the Work. - Firethering
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Wednesday, May 20, 2026
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HomeTechGoogle's Next AI Bet Isn't on Chatbots. It's on Agents That Do...
Google’s Next AI Bet Isn’t on Chatbots. It’s on Agents That Do the Work.
By Mohit Geryani
May 20, 2026
Last updated: May 20, 2026
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For the last three years, Google has been playing catch-up in the chatbot race. ChatGPT arrived, Gemini followed, and the conversation quickly became about which AI could answer questions better, faster, and more accurately.
Google I/O this week suggested the company is done competing on chat alone.
Gemini 3.5 Flash launched Tuesday, and Google barely framed it as a conversational product. Instead, the company focused on coding pipelines, autonomous research, multi-agent coordination, and one demo that stood out across the industry: building an operating system from scratch with minimal human input.
The model can reportedly operate autonomously for hours. Google says it’s up to 4× faster than other frontier models, with an optimized version reaching 12× faster speeds at similar quality.
What 3.5 Flash is built for
Play
The speed numbers Google is citing aren’t marketing. They’re architectural decisions that only make sense if you’re building for agents rather than conversations.
A chatbot doesn’t need to be 12x faster than its predecessor. A response that takes two seconds instead of 24 seconds doesn’t meaningfully change the experience of asking a question and reading an answer. But in an agentic workflow where multiple AI instances are running in parallel on different components of the same task, latency compounds. Slow agents create bottlenecks. Fast agents create throughput.
Gemini 3.5 Flash was co-developed with Antigravity, Google’s agentic development platform, specifically so agents would have what DeepMind’s chief technologist Koray Kavukcuoglu described as "a native environment where they can live, work, and execute." That’s a different design philosophy than building a model and then figuring out what to do with it afterward. The model and the environment were built together with agents in mind from the start.
The benchmarks back the direction. Kavukcuoglu told reporters ahead of I/O that 3.5 Flash outperforms Gemini 3.1 Pro on nearly all benchmarks including coding, agentic tasks, and multimodal reasoning. A Flash model beating the previous generation’s Pro model on capability benchmarks while being significantly faster is the kind of result that makes the agentic bet look credible rather than aspirational.
Gemini 3.5 Flash
The OS demo
The demonstration that got the most attention at I/O was Google engineer Varun Mohan showing agents spawning off inside Antigravity to work on separate components before coming together to build a full operating system.
It’s easy to dismiss demos like this. Labs have been staging impressive controlled environments for years and the gap between what works in a keynote and what works in production is well documented.
What makes this one worth paying attention to is the coordination pattern. Multiple agents running simultaneously on distinct subtasks, merging outputs into a coherent whole. That’s the architecture that makes long-horizon agentic work possible. A single agent working sequentially hits context limits and coherence problems on complex tasks. A fleet of specialized agents working in parallel and combining results is a fundamentally different approach.
Google says 3.5 Flash is already producing actual results for partners outside the demo environment. Banks and fintechs automating multi-week workflows. Data science teams surfacing insights in complex environments. They’re production claims, and production claims are where the actual thing gets told over the next few months.
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