In 2026, China's enterprise-grade AI sector reached a turning point as the industry moved from initial trials to the widespread use of multi-agent systems. This shift has been driven by policy initiatives aimed at standardizing and promoting intelligent applications across key sectors. The Ministry of Industry and Information Technology (MIIT) and the National Data Administration’s recent 'Model-Data Resonance' initiative lays out a clear path for developing specialized AI models, paving the way for the accelerated adoption of multi-agent systems in industries such as steel and petrochemicals.
The move towards multi-agent collaboration addresses the complex demands of modern enterprise environments, where the limitations of standalone AI agents have become increasingly clear. Zhao Jiehui, CEO of DeepBlue Technology, emphasizes that real-world operations require an orchestrated team of agents rather than isolated AI capabilities to manage intricate workflows. Scenarios in manufacturing and retail highlight this necessity, as multiple roles must work together to achieve efficiency and accuracy in high-stakes situations.
Policy-Driven Momentum
The recent introduction of strategic guidelines, such as the 'Guiding Opinions on the Standardized Application and Innovative Development of Agents' by various regulatory bodies, is a endorsement of multi-agent systems. For the first time at a national level, agents are defined as intelligent systems capable of autonomous functions, laying the groundwork for their widespread integration into enterprise operations. This policy shift is expected to create a fertile environment for the growth of AI agents, with forecasts predicting a compound annual growth rate of 135.3% in the number of active agents from 2026 to 2031.
As these regulations align with industry needs, companies like DeepData Tech are well-positioned to benefit. The firm’s recent revenue growth—up 181.5% year-over-year in its core AI businesses—shows the market's recognition of its innovative collaborative AI solutions. By focusing on integrating enterprise ontology into AI operations, DeepData Tech has developed DeepexiOS, an AI-grade enterprise operating system designed to enables cooperation among multiple AI agents. This system supports business operational needs while also making sure regulatory compliance through its ontology-driven architecture.
Collaboration as a New Paradigm
The introduction of a two-layer token collaboration model by DeepexiOS is particularly noteworthy. This model effectively balances the costs and benefits associated with enterprise AI adoption. By generating business semantic-layer tokens that encapsulate specific enterprise knowledge, DeepexiOS enhances the overall utility of AI deployments while lowering operational costs through the use of general-purpose large models for routine tasks.
This approach supports the idea that enterprises do not need to invest heavily in developing custom AI models. Instead, the alignment between specialized enterprise models and general-purpose frameworks provides a pathway to reduce costs and enhance operational efficiency. This dual advantage is key as businesses seek effective solutions in a fast-moving technological landscape.
Future Outlook
Looking ahead, DeepData Tech finds itself in a strong position, with its ontology-driven model offering a competitive edge in the growing market for enterprise AI solutions. The company is expected to double its revenue as it continues to refine its strategies in response to the industry's evolving demands. With solid backing from policy developments and a growing push for intelligent transformation across sectors, the prospects for DeepData Tech appear bright.
As the enterprise AI collaboration space enters a phase of accelerated growth, the implementation of multi-agent systems is likely to become standard. With its pioneering solutions, DeepData Tech is not just responding to industry trends; it is actively shaping the future of enterprise AI, making it a key player to monitor in the coming years.
