Holo3: Breaking the Computer Use Frontier

In the world of artificial intelligence, the year 2026 brings a breakthrough that the industry has long awaited: effective and cost-efficient computer control by AI agents. While giants like OpenAI or Anthropic race for parameter counts and computing power, the Holo3 model proves that the key to dominance in the Computer Use segment is not size, but specialized training architecture. A score of 78.85% in the prestigious OSWorld-Verified benchmark places the new unit from H Company in the lead, outclassing existing solutions in terms of system navigation precision.

The most impressive aspect of this premiere is not the score itself, but the efficiency of the model. Holo3-122B-A10B, despite having 122 billion total parameters, utilizes only 10 billion active parameters. This radical approach allows it to compete with powerhouses like GPT 5.4 or Opus 4.6, offering a fraction of their operating costs with higher effectiveness in operational tasks. This is a clear signal to the enterprise sector: the future of autonomy does not belong to monolithic colossi, but to agile, agentic structures.

The Agentic Learning Flywheel

The foundation of Holo3's success is a proprietary training pipeline called the Agentic Learning Flywheel. Instead of relying solely on static datasets, the creators focused on a continuous feedback loop that polishes two critical pillars: visual interface perception and the decision-making process. The model learns from annotated examples of specific task executions while simultaneously developing generalist capabilities that allow it to handle almost any user interface, regardless of its specifics.

This process is based on three stages. First, Synthetic Navigation Data is used—scenarios generated by humans and AI that teach the model how to move through systems. Second, Out-of-Domain Augmentation is applied, which involves programmatically expanding scenarios with unforeseen situations, building the model's resilience to interface errors. The whole process is crowned by Curated Reinforcement Learning, where every piece of data undergoes advanced filtering and reinforcement learning to maximize the effectiveness of actions in a production environment.

Synthetic Environment Factory and Corporate Tests

To prepare the model for the realities of modern business, H Company created the Synthetic Environment Factory. This is a proprietary "gym" for AI, where coding agents build websites and systems from scratch based on scenario specifications. Thanks to this, Holo3 trains in controlled but extremely complex ecosystems that perfectly mirror enterprise-class software. Every task is verified by automated scripts, guaranteeing the purity of training data and the measurability of progress.

Alongside the model, H Corporate Benchmarks debuted—a set of 486 multi-step tasks divided into four categories: E-commerce, Business software, Collaboration, and Multi-App setups. It is in this last category that Holo3 shows its true power. "Multi-App" tasks require the agent to coordinate information between multiple systems simultaneously—for example, retrieving prices from a PDF file, comparing them with an employee's budget in an ERP system, and independently sending personalized emails with a purchase approval decision.

Such operations require not only precise document parsing but, above all, long-horizon reasoning without losing the context of the task. Comparison with Qwen3.5 base models shows that specialized training for "Computer Use" is key—Holo3 achieves higher success rates while maintaining the same standards of localization and grounding as significantly larger units.

Democratization of Access and Open Technology

H Company's strategy in the area of technology sharing deserves special attention. The company does not lock its achievements in a "black box." The Holo3-35B-A3B model (with 3 billion active parameters) has been released on the Hugging Face platform under the Apache2 license. This means the developer community and companies can freely implement these solutions in their own infrastructures, accelerating the adoption of autonomous agents on a global scale.

• Holo3-122B-A10B: The flagship model available via Inference API, optimized for the most demanding corporate processes.
• Holo3-35B-A3B: An open-source model, ideal for lighter tasks and local deployments.
• H Corporate Benchmarks: A new standard for evaluating AI agents in office and multi-application tasks.
• Inference API: Access to models in a subscription model and a free tier for the smaller variant.

For the creative and technology industries, the arrival of Holo3 marks the end of an era where AI was merely a content generator. It becomes a full-fledged tool operator. The ability to shift the burden of repetitive administrative tasks to an agent that not only understands a command but can physically "click" and carry out a process across various software opens the way for the Autonomous Enterprise—a company where the operational layer is fully automated.

Toward Universal Agency

Holo3 is a milestone, but the creators are already announcing the next stage: Adaptive Agency. While today's models masterfully handle interfaces they encountered during the training process, the next generation is intended to have the ability to learn how to operate completely new, non-standard corporate systems in real-time. This is a transition from "using tools I know" to "understanding every tool I see."

In my opinion, the success of Holo3 will finally end the debate over whether only scale matters in AI. This model proves that 10 billion active parameters, properly trained in synthetic "proving grounds," can defeat giants with values counted in trillions of parameters. In the coming months, I expect a wave of implementations where agents like Holo3 will take over the role of intermediaries between complex software and the user, changing the definition of "computer work" once and for all. The era in which we had to learn how to use programs is coming to an end—now it is the programs, thanks to AI agents, that will adapt to our intentions.