New point of tension in the AI race: transition from dialogue to autonomy
The end of 2025 is shaping an interesting technological node: the industry is approaching a release that may be not just an update to the Google Gemini line, but a change in paradigm in how companies use artificial intelligence. The expected version Gemini 3 has not yet been officially presented, but indirect signs, insider analytics and changes in the behavior of version 2.5 indicate a major leap towards agent autonomy.
If earlier models developed around the idea of "deep and natural dialogue", now there is a growing demand for AI that is capable not only of reasoning, but also of acting. Not just explaining a solution, but applying it in practice: managing interfaces, performing multi-step tasks, correcting its own mistakes, interacting with code bases or internal company systems.
It is assumed that this direction will become the core of the Gemini 3 architecture: combining multimodal analysis, long-term planning and instrumental control within a single model. Not as add-ons, but as a basic capability.
Architectural expectations: what could become the foundation of Gemini 3
Despite the lack of final specifications, the logic of the development of previous versions and leaks related to testing in production allow us to identify several potential core blocks.
Firstly, experts expect a deeper integration of the Deep Think mode — a mechanism that shifts the model from superficial responses towards structured internal analysis. It is assumed that recursive planning, self-assessment and correction of intermediate results will be built into inference by default. This may increase accuracy in scientific, engineering and analytical tasks.
Secondly, a significant part of the industry expects an expansion of the concept of radical multimodality. If Gemini 2.5 already demonstrated the ability to work with video, images, audio and text in a single architecture, it is logical to assume that version 3.0 will enhance work with 3D structures, geodata and dynamic scenes — which is important for robotics, logistics and real-time systems.
Thirdly, the anticipated preservation of Google's leadership in the field of multi-million context is discussed. In combination with improved attention mechanisms, this may mean that the model will be able to work not just with large documents, but with full code repositories, documentation archives or multi-year datasets.
Finally, an increase in the model's dependence on proprietary TPU v5p infrastructure, tailored for MoE architectures is expected. This may give a boost to response speed and stability in complex, multi-step tasks.
Agency as a key bet: what could change with the appearance of a full-fledged Agent Mode
One of the main factors of expectation is the possible expansion of the functionality of Agent Mode and Computer Use mode. Even today, Google is experimenting with the model being able not only to suggest, but also to independently perform actions in the interface: click buttons, fill out forms, navigate pages, analyze visual elements on the screen.
If these mechanisms are brought to a mature version in Gemini 3, businesses will receive a completely new class of digital tools — autonomous AI agents, which act according to the cycle:
- Plan — modeling strategy;
- Act — performing specific steps;
- Observe — analyzing the result;
- Revise — adjusting behavior.
Potential scenarios in which such agents can manifest themselves:
- Working with legacy systems through an interface, without the need for an API.
- Automation of complex back-office processes with dozens of branches.
- Large-scale testing, migrations and refactoring of systems in a development environment.
- Comprehensive analysis of multimodal data — video, documents, tables.
This is only justified expectations, but if they are confirmed, this will be a step towards the transition from «smart assistants» to digital operational employees, capable of solving real tasks at the level of instrumental interaction.
Comparative context: the confrontation of two philosophies — Google and OpenAI
The upcoming release is inevitably being compared to the already released GPT-5.1 lineup, which focused on warmer communication, adaptive thinking, and personality customization. OpenAI is strengthening its position in the «AI as a conversationalist» and «AI as a creative partner» segment.
Google, judging by available data, is going in the opposite direction: creating a utility operator, not a persona. If GPT-5.1 strives to be more human-like in its reactions, then Gemini potentially aims to be a more efficient system — analytical, instrumental, autonomous.
In the context of B2B, this difference is particularly noticeable. Presumably, companies will choose between two archetype models:
- OpenAI — when communication, UX, creativity and personalization are priorities.
- Google — when process automation, large-scale work with code and documents are required.
Potential impact on the industry: from logistics to software development
Although Gemini 3 has not yet been released, it is already possible to describe the industries where its architectural features (if confirmed) are capable of changing process economics. Several examples of practical directions:
Financial operations. Automated checks, compliance, reconciliations, analysis of large arrays of transactions, building complex reports with a visual component.
Logistics and transport. Calculation of optimal routes, analysis of video and maps, forecasting failures, adaptation of supply chains in real time.
Software development. Automation of testing, bug fixes, migrations, documentation generation, refactoring of large monorepos based on multimodal analysis.
Manufacturing and IoT. Understanding 3D structures, analysis of video from production lines, predictive diagnostics.
Practical business preparation: what can be done right now
Despite the fact that Gemini 3 has not yet been officially presented, companies can already prepare for its appearance — without the risk of premature investments. The most pragmatic strategy is to work with what is already available, creating a foundation for accelerated implementation after the release.
- Conduct an audit of processes and data. Identify areas where there is a high proportion of manual actions, work with interfaces, or complex decision-making chains.
- Start pilots based on the current generation of models. Gemini 2.5 already contains elements of agency and multimodality — they can be used as a testbed for the future transition to 3.0.
- Assess the potential economics of implementation. At this stage, it is important to understand which processes will be economically justified when transitioning to agentic AI. It is convenient to use
→ EasyByte neural network development cost calculator — it helps to assess the expected resources, complexity of integrations and potential ROI before the start of the project. - Form an architectural approach and security framework. If the company does not have internal ML expertise, it is useful to define the principles of working with the model, access restrictions and the format of human participation (human-in-the-loop) in advance. You can contact specialists for a preliminary consultation. For example, at
→ EasyByte expert free consultation you can analyze the structure of processes, the level of data maturity and create a roadmap for implementing future agentic solutions. - Prepare a secure test environment. Create sandboxes for experiments — separate environments, test repositories, data copies. This will accelerate the transition to agentic mechanics after the release of Gemini 3.
This approach allows companies to quickly adapt after the release, without waiting for market reaction and without wasting time on building infrastructure ex post facto.
📌FAQ: frequent questions regarding the upcoming release of Gemini 3
Question: How accurate are the rumors about the release and can early characteristics be trusted?
Answer: There are no official specifications yet, but the combination of leaks, the behavior of current Google models, and changes in hosting infrastructure suggest that many of the discussed features have an architectural basis.
Question: Is it worth starting integration projects before the Gemini 3 release?
Answer: Yes, but in a pilot format. Many architectural principles (multimodality, large contexts, agent mechanisms) are already available in 2.5, and mastering them will accelerate the implementation of 3.0.
Question: Can Gemini 3 completely replace RPA platforms?
Answer: If expectations for Computer Use are confirmed, AI agents may close part of the classic RPA scenarios. However, the maturity and manageability of such approaches will require time.
Question: How safe is it to implement agent AI in workflows?
Answer: Security depends not on the model, but on the implementation architecture: sandboxes, access, audit logs, human-in-the-loop — these are mandatory elements for any systems that have access to data or interfaces.
Question: Is a sharp jump in reasoning quality to be expected?
Answer: If Deep Think is integrated into the basic pipeline, the increase in logical coherence and accuracy may be noticeable, especially in complex multi-process tasks. But the real effect can only be assessed after the release.