Google used I/O 2026 to make a clear statement about where consumer and developer AI is heading next: from chatbots that answer questions to agents that can plan, create and act across everyday workflows.
The company framed the event as the start of an “agentic Gemini era”, centred on new Gemini models, the Gemini app, Google Antigravity for developers, AI experiences in Search, and more multimodal creation tools. For readers following AI closely, the important point is not simply that Google announced more AI features. It is that Google is trying to connect models, apps, developer tools and consumer services into a broader agent platform.
Background: why agentic AI is becoming the next big AI battleground
For the past two years, the public AI conversation has been dominated by model upgrades: bigger context windows, better reasoning, faster image generation and cheaper API access. Those improvements still matter, but the market is shifting toward what the models can actually do for people.
Agentic AI refers to systems that can break a task into steps, use tools, remember context, make recommendations and help complete work with less manual prompting. Instead of asking an AI to draft a plan and then doing every step yourself, an agent may help research, organise, build, compare, book, code or revise across a series of actions.
That is why Google’s I/O 2026 message matters. Google is not only competing with OpenAI, Anthropic, Microsoft and Meta on model quality. It is also competing on distribution: Search, Android, Workspace, YouTube, Chrome, cloud services and developer platforms.
What Google announced at I/O 2026
Google’s official I/O 2026 coverage highlighted new Gemini models and agentic experiences across its products. The company said it is releasing two new models: Gemini Omni and Gemini 3.5.
Gemini 3.5 Flash focuses on fast agentic work
One of the most practical announcements is Gemini 3.5 Flash, described by Google as the first model in its latest series combining “frontier intelligence with action”. Google says the model is available through Google Antigravity, the Gemini API in Google AI Studio, and Android Studio.
That positioning is important. Flash models are typically expected to be faster and more cost-efficient than heavyweight flagship models. If Gemini 3.5 Flash can handle longer, multi-step tasks at lower latency and cost, it could become attractive for developers building assistants, workflow tools, internal business automations and customer-facing AI features.
Gemini Omni pushes multimodal creation
Google also introduced Gemini Omni, which it describes as a model that can create from any input and edit through conversational language, beginning with video. In plain English, this points to AI systems that can understand and manipulate text, images, video and other media in a more unified way.
For creators and marketers, this could reduce the friction between brainstorming, editing and publishing. For businesses, it may lead to faster training content, product explainers, support material and internal communications. For users, it suggests more natural ways to tell software what they want changed, rather than learning specialised editing tools.
Google Antigravity moves from coding help to coding agents
Google also used I/O to promote Google Antigravity, an agent-first development platform. The broader message is that AI coding is moving beyond autocomplete and chat assistance. The next stage is agents that can help act on a software task: explore a codebase, propose changes, run steps, and support longer development workflows.
This is where the developer market is especially competitive. OpenAI’s Codex, Anthropic’s Claude tools, Microsoft GitHub Copilot and Google’s own developer products are all trying to become the default AI layer for software work. Google’s advantage is that it can connect models with Android Studio, Google AI Studio, Gemini APIs and cloud infrastructure.
Why this matters for users
For everyday users, the shift to agentic AI could make AI less like a search box and more like a digital helper that works across tasks. Google highlighted agentic experiences in the Gemini app and Search, including more proactive assistance and information agents.
If executed well, this could help users compare options, plan purchases, summarise complex information, create media, prepare for school or work, and keep track of routine tasks. The value is not just better answers. It is fewer steps between a user’s goal and a finished result.
However, this also raises the bar for trust. The more an AI system can do, the more important it becomes that users understand what it is doing, what data it can access, when it is acting on their behalf and how to stop or correct it.
Practical impact for businesses and developers
For businesses, Google’s announcements point to three near-term opportunities.
First, customer support and internal help desks may become more capable. Agentic models can potentially handle multi-step troubleshooting, policy lookup, ticket drafting and follow-up workflows.
Second, marketing and content teams may gain faster multimodal production. Gemini Omni-style capabilities could make it easier to turn a campaign idea into drafts, visuals and video variations, although human review remains essential.
Third, developers may use Gemini 3.5 Flash and Antigravity-style tools to build more complex AI features without relying only on manual orchestration. A model designed for long-horizon agentic tasks can reduce the amount of glue code needed to make an AI workflow useful.
For Australian businesses and startups, the practical takeaway is simple: agentic AI should now be evaluated as a workflow technology, not just a content generator. The best use cases will be repetitive, measurable and reviewable, such as document processing, reporting, support triage, sales research, compliance checklists and developer productivity.
Risks and limitations to consider
Agentic AI also comes with real risks. Models can still misunderstand instructions, invent details, take inefficient paths or make confident mistakes. When an AI tool is only drafting text, errors are usually easier to catch. When it is acting across tools, the consequences can be larger.
Businesses should be careful with permissions. An AI agent should not automatically have access to sensitive systems, customer records, payments, production databases or publishing tools unless there are strong approval steps and audit logs.
There are also privacy and transparency concerns. If AI systems become more proactive, users need clear controls over what information is used, how long it is retained and whether outputs are grounded in reliable sources.
What to watch next
The next question is how quickly these I/O announcements become stable, widely available products. Watch for Gemini 3.5 pricing, API limits, enterprise controls, Android integration, Workspace features and how Google positions Antigravity against GitHub Copilot, Codex and Claude-based coding tools.
It will also be worth watching whether users actually adopt proactive AI agents in daily life. Many people like AI when it answers a direct question. Fewer are comfortable when software starts taking initiative. The winners in agentic AI will likely be the products that combine useful automation with clear user control.
Conclusion
Google I/O 2026 shows that the AI race is moving into a more practical phase. The headline is not just newer Gemini models. It is Google’s attempt to make Gemini an action layer across search, apps, devices and developer workflows.
For users, that could mean AI help that feels more useful and less manual. For businesses, it is a signal to start testing agentic workflows carefully. For developers, Gemini 3.5 Flash, Gemini Omni and Google Antigravity are worth watching because they show where AI product design is heading: fewer isolated prompts, more connected action.
Sources
- Google Blog: I/O 2026: Welcome to the agentic Gemini era
- Google Blog: 100 things we announced at I/O 2026
- Google Blog: Google I/O 2026: News and announcements