From Chatbot to Agent: Google’s New Default for Work
Google I/O 2026 marked a clear pivot: Gemini is no longer just a conversational model, but the backbone of a new agentic era. Powered by Gemini 3.5 models and the Antigravity framework, Google is repositioning Gemini AI agents as autonomous digital coworkers that can sit inside products and quietly orchestrate complex workflows. In Search, that vision is already visible. Information agents can be configured to run in the background around the clock, surfacing results when they’re actually needed and even helping users take the next step, rather than just returning links. For Google, this is more than an interface refresh—it’s a strategic shift toward agentic AI capabilities that turn its existing services into action-oriented platforms. As these agents gain persistence and memory, they start to look less like tools you query and more like systems that continuously manage ongoing tasks on your behalf.
Information Agents and Generative Interfaces in Search
Search is becoming the clearest showcase of Gemini AI agents in action. Google’s new information agents are personalized assistants you configure once, then leave running to monitor queries, opportunities or topics and notify you at the right moment. They are designed for long-running, loosely defined tasks—like tracking competitive news, monitoring grant calls or scouting datasets—without constant human babysitting. On top of that, Google is layering agentic coding capabilities into Search itself. With Gemini 3.5 Flash and Antigravity, Search can dynamically assemble custom layouts, interactive visuals and even persistent dashboards tailored to a single complex query. These dashboards function like lightweight, auto-built apps specific to a workflow, and can be revisited over time as the underlying task evolves. This generative UI approach turns Search from a static results page into an autonomous workflow automation surface where layout, logic and data views are all orchestrated by agents.
Gemini for Science: Agents for Hypotheses, Experiments and Papers
For researchers, Google introduced Gemini for Science, a suite of AI-powered research tools meant to compress entire segments of the scientific method. It begins with Hypothesis Generation, where Gemini digs across millions of scientific papers to propose new ideas or challenges, accompanied by verified, clickable citations to keep claims grounded. Once a promising hypothesis emerges, the Computational Discovery tool takes over as an "agentic search engine" that can automatically propose and simulate thousands of possible tests and experiments, far beyond what a human could manually enumerate in the same time. To tackle information overload, Literature Insights acts as an ever-present reading assistant, scanning scientific literature and distilling it into structured reports, infographics or even audio and video summaries. Together, these Gemini AI agents aim to streamline data analysis, hypothesis testing and literature review into a more continuous, automated research workflow.
Reinventing Scientific Workflows with Agentic Capabilities
Gemini for Science is also about stitching isolated steps into end-to-end, autonomous workflow automation. Google’s new Science Skills tool exemplifies this: it can reach across more than 30 major life science databases and tools, executing what used to be intricate, manual cross-platform tasks in minutes rather than hours. Instead of manually exporting, cleaning and reconciling data from multiple resources, an agent can plan and execute the whole sequence: identify relevant datasets, apply the right filters, run analyses and present results in a digestible format. Because these systems reason about sequences of actions, they can minimize redundant calls, which reduces token consumption and repetitive work. For labs and research teams, the implication is a shift from micromanaging tools to supervising agents—verifying outputs, steering directions and focusing human attention on interpretation and decision-making rather than the mechanical steps in between.
The Enterprise Stakes: Agents as Automation Platforms
Embedding Gemini AI agents deeply across Search and specialized domains like science positions Google as an emerging competitor to traditional enterprise automation platforms. Instead of scripting workflows in dedicated automation suites, organizations could delegate recurring, cross-application tasks to Gemini agents that already live inside their productivity and cloud environments. An information agent might track market signals, while a research agent surfaces relevant studies and a Science Skills agent orchestrates complex data queries—all with a shared understanding of user goals and context. Because these agents can handle planning, memory and multi-step execution, they reduce the need for brittle rule-based automations and manual glue work between systems. As access expands through Google AI Pro, Ultra subscriptions and Google Cloud, enterprises will increasingly decide whether to build on conventional automation stacks, or embrace Gemini’s agentic layer as the new fabric connecting their knowledge, workflows and decision-making.