The future of artificial intelligence is no longer just about responding to commands; it’s about systems that think, plan, and act on their own. This transformative shift, towards what is known as agentic AI, marks a significant leap in enterprise computing. These intelligent agents are designed not merely to assist, but to autonomously drive efficiency and innovation across industries.
A prime example of this evolution can be seen in heavy industry. In a cement plant operated by Conch Group, an agentic AI system built on Huawei infrastructure now predicts the strength of clinker with over 90% accuracy and autonomously adjusts calcination parameters to cut coal consumption by 1%—decisions that previously required human expertise accumulated over decades. This exemplifies how Huawei is developing agentic AI systems that move beyond simple command-response interactions toward platforms capable of independent planning, decision-making, and execution.
The Evolution of AI: From Reactive to Agentic
Huawei’s approach to building these agentic AI systems centres on a comprehensive strategy spanning AI infrastructure, foundation models, specialised tools, and agent platforms. This integrated framework is designed to empower machines with true autonomy.
Zhang Yuxin, CTO of Huawei Cloud, outlined this framework at the recent Huawei Cloud AI Summit in Shanghai, where over 1,000 leaders from politics, business, and technology examined practical implementations across finance, shipping ports, chemical manufacturing, healthcare, and autonomous driving. His insights underscore a pivotal moment in AI development.
The distinction matters because traditional AI applications respond to user commands within fixed processes, while agentic AI systems operate with autonomy that fundamentally changes their role in enterprise operations. Zhang characterised this as “a major shift in applications and compute,” noting that these systems make decisions independently and adapt dynamically, reshaping how computing systems interact and allocate resources. The question for enterprises becomes: how do you build infrastructure and platforms capable of supporting this level of autonomous operation?
It’s not just about automating tasks, but about automating intelligence itself. This capability allows businesses to achieve unprecedented levels of operational efficiency and strategic agility, paving the way for a new era of industrial transformation.
Powering Autonomy: Huawei’s AI Infrastructure and Foundation Models
Infrastructure challenges drive new computing architectures. The computational demands of agentic AI systems have exposed limitations in traditional cloud architectures, particularly as foundation model training and inference requirements surge. Huawei Cloud’s response involves CloudMatrix384 supernodes connected through a high-speed MatrixLink network, creating what the company describes as a flexible hybrid compute system combining general-purpose and intelligent compute capabilities.
This advanced architecture specifically addresses bottlenecks in Mixture of Experts (MoE) models through expert parallelism inference, which reduces NPU idle time during data transfers. According to the company’s technical specifications, this approach boosts single-PU inference speed 4-5 times compared to other popular models, significantly enhancing AI computing performance.
The system also incorporates memory-centric AI-Native Storage designed for typical AI tasks, aimed at enhancing both training and inference efficiency. ModelBest, a company specialising in general-purpose AI and device intelligence, demonstrated practical applications of this robust infrastructure.
Li Dahai, co-founder and CEO of ModelBest, detailed how their MiniCPM series—spanning foundation models, multi-modal capabilities, and full-modality integration—integrates with Huawei Cloud AI Compute Service to achieve 20% improvements in training energy efficiency and 10% performance gains over industry standards. These MiniCPM models have found applications in automotive systems, smartphones, embodied AI, and AI-enabled personal computers, showcasing the versatility of Huawei’s AI platform.
The challenge of adapting foundation models for specific industry needs has driven the development of more sophisticated training methodologies. Huawei Cloud’s approach encompasses three key components: a complete data pipeline handling collection through management, a ready-to-use incremental training workflow, and a smart evaluation platform with preset evaluation sets. This comprehensive approach ensures that complex machine learning models can be fine-tuned effectively for diverse real-world scenarios.
Agentic AI in Action: Transforming Industries
The distinction between consumer-focused AI agents and enterprise-grade agentic AI systems centres on integration requirements and operational complexity. Enterprise systems must seamlessly integrate into broader workflows, handle complex situations, and meet higher operational standards than consumer applications designed for quick interactions.
Huawei Cloud’s Versatile platform addresses this gap by providing infrastructure for businesses to create agents tailored to production needs. The platform combines AI compute, models, data platforms, tools, and ecosystem capabilities to streamline agent development through deployment, release, usage, and management phases, making advanced autonomous AI accessible for enterprise solutions.
Conch Group’s implementation in cement manufacturing offers specific performance metrics. The company partnered with Huawei to create what they describe as the cement industry’s first AI-powered cement and building materials model. The resulting cement agents predict clinker strength at 3 and 28 days with predictions deviating less than 1 MPa from actual results, representing over 90% accuracy. For cement calcination optimisation, the model suggests key process parameters and operational solutions that cut standard coal usage by 1% compared to class A energy efficiency standards.
Xu Yue, Assistant to Conch Cement’s General Manager, noted that the model’s success with quality control, production optimisation, equipment management, and safety establishes groundwork for end-to-end collaboration and decision-making through cement agents, moving the industry “from relying on traditional expertise to being fully driven by data across all processes.”
In corporate travel management, Smartcom developed a travel agent using Huawei Cloud Versatile that provides end-to-end smart services across departure, transfers, and flights. Kong Xianghong, CTO of Shenzhen Smartcom and Director of Smartcom Solutions, reported that the system combines travel industry data, company policies, and individual trip histories to generate recommendations. Employees adopt over half of these suggestions and complete bookings in under two minutes. The agent resolves 80% of issues in an average of three interactions through predictive question matching, showcasing remarkable efficiency in autonomous AI applications.
Beyond heavy industry and corporate travel, other sectors are also seeing the benefits. Shaanxi Cultural Industry Investment Group partnered with Huawei to integrate AI with cultural tourism operations. Using Huawei Cloud’s data-AI convergence platform, they combined diverse data to create comprehensive datasets spanning history, film, and intangible heritage. This led to a “trusted national data space for cultural tourism” on Huawei Cloud, enabling applications like asset verification, copyright transaction, and creative development.
International implementations demonstrate similar patterns. Dubai Municipality worked with Huawei Cloud to integrate foundation models, virtual humans, digital twins, and geographical information systems into urban systems. Mariam Almheiri, CEO of the Building Regulation and Permits Agency at Dubai Municipality, shared how this integration has improved city planning, facility management, and emergency responses, illustrating the broad impact of Huawei’s agentic AI systems.
Conclusion: The Future of Independent Decision-Making Systems
What’s next for autonomous AI? The implementations discussed at the summit reflect a broader industry trend toward agentic AI systems that operate with increasing autonomy within defined parameters. The technology’s progression from reactive tools to systems capable of planning and executing complex tasks independently represents a fundamental architectural shift in enterprise computing.
This transition, however, requires substantial infrastructure investments, sophisticated data engineering, and careful integration with existing business processes. The performance metrics from early implementations—whether in manufacturing efficiency gains, urban management improvements, or travel booking optimisation—provide benchmarks for organisations evaluating similar deployments.
As agentic AI systems continue to mature, the focus appears to be shifting from technological capability demonstrations to operational integration challenges, governance frameworks, and measurable business outcomes. The examples from cement manufacturing, cultural tourism, and corporate travel management suggest that practical value emerges when these systems address specific operational pain points rather than serving as general-purpose automation tools. Embrace the era of autonomous AI to unlock your enterprise’s full potential.
