Huawei Pushes Boundaries with Agentic AI: From Cement Plants to Smart Cities

Huawei’s Vision for Autonomous AI

In a cement plant run by China’s Conch Group, an artificial intelligence system powered by Huawei Cloud predicts the strength of clinker—the key component in cement—with more than 90% accuracy. It then automatically fine-tunes production parameters to cut coal consumption by about 1%, a task that once required decades of human expertise.

This real-world success story captures Huawei’s growing ambition in agentic AI—a new generation of artificial intelligence that can plan, decide, and act with limited human input. At the Huawei Cloud AI Summit in Shanghai, Zhang Yuxin, Chief Technology Officer of Huawei Cloud, described the shift as “a major change in both applications and compute,” marking the move from reactive systems to autonomous decision-makers.

More than 1,000 leaders from government, business, and technology attended the summit, exploring how Huawei’s AI strategy spans everything from infrastructure and foundation models to specialized industry tools and agent platforms.

Powering AI Autonomy with New Infrastructure

Building agentic AI systems demands massive computing power and seamless coordination between different AI components. Huawei Cloud’s answer is its CloudMatrix384 supernodes, linked by a high-speed MatrixLink network. Together, they form a hybrid compute architecture that blends general-purpose and intelligent computing to handle heavy AI workloads.

This setup also tackles performance bottlenecks in Mixture of Experts (MoE) models—a design used in many modern AI systems—by introducing “expert parallelism inference,” which reduces idle processing time and improves speed. Huawei claims this can deliver 4–5x faster inference compared with existing models.

The infrastructure features AI-Native Storage, built specifically for data-heavy AI tasks, improving both model training and deployment efficiency.

One of Huawei’s key partners, ModelBest, shared that its MiniCPM models—used in cars, smartphones, and personal computers—achieved 20% better energy efficiency and 10% higher performance when trained on Huawei Cloud’s compute systems.

From Foundation Models to Industry Applications

Huawei Cloud’s AI development process revolves around three layers:

1. A comprehensive data pipeline for managing collection and preparation,
2. An incremental training workflow that fine-tunes foundation models for specific industries, and
3. A smart evaluation platform that benchmarks performance automatically.

According to Huawei, this incremental workflow can boost model accuracy by 20–30% through continuous optimization of training data and configurations.

These methods are already finding use cases beyond manufacturing. In China’s Shaanxi province, Huawei partnered with the Shaanxi Cultural Industry Investment Group to build a “trusted national data space for cultural tourism.” The initiative merges history, film, and heritage datasets to power AI tools for asset verification, copyright management, and creative industry development.

Meanwhile, Dubai Municipality has used Huawei Cloud’s technology to connect foundation models, digital twins, and virtual humans for city planning and emergency response—showing how agentic AI can support smarter urban systems.

Enterprise-Grade AI Agents in Action

Huawei Cloud’s Versatile platform is designed to help companies build and deploy AI agents that integrate seamlessly with business operations. These enterprise-grade agents go beyond simple chatbots—they’re capable of handling complex workflows across industries.

For example, Conch Group’s cement AI agent now predicts material strength and optimizes fuel use, improving energy efficiency and production stability. Xu Yue, Assistant to Conch Cement’s General Manager, said the system has transformed cement production “from relying on traditional expertise to being fully data-driven across all processes.”

In another case, Shenzhen Smartcom developed a corporate travel agent using Huawei Cloud. The system combines flight data, company policies, and employee travel histories to generate personalized recommendations. Over half of its suggestions are accepted by users, and most bookings are completed in under two minutes.

The Road Ahead for Agentic AI

Huawei’s push into agentic AI reflects a broader industry trend toward autonomous systems that can manage complex decisions without human oversight. While early results—like efficiency gains in manufacturing and city management—are promising, widespread adoption still hinges on robust infrastructure, reliable data pipelines, and governance frameworks.

As Zhang Yuxin emphasized, the real challenge now isn’t proving what agentic AI can do—it’s ensuring these systems deliver consistent, measurable business value at scale.

For enterprises exploring the next generation of automation, Huawei’s ecosystem of compute, models, and tools offers a glimpse into a future where machines don’t just assist—they act with intent.