Huawei’s Agentic AI Powers Industrial Automation

In a notable advancement reported at the recent Huawei Cloud AI Summit in Shanghai, an agentic AI system deployed at a Conch Group cement plant is demonstrating the transformative potential of autonomous decision-making. Built on Huawei’s AI infrastructure, the system accurately predicts clinker strength with over 90% accuracy. More crucially, it autonomously adjusts calcination parameters, resulting in a 1% reduction in coal consumption. This level of autonomous optimization, traditionally requiring decades of human expertise, highlights the maturation of agentic AI beyond simple command-response interactions.

Huawei is positioning itself at the forefront of this shift, developing agentic AI platforms capable of independent planning, decision-making, and execution. This approach, articulated by Huawei Cloud CTO Zhang Yuxin, hinges on a comprehensive strategy encompassing robust AI infrastructure, advanced foundation models, specialized toolsets, and versatile agent platforms. The Shanghai summit, attended by over 1,000 leaders from various sectors including finance, shipping, chemicals, healthcare, and autonomous driving, showcased practical applications of this framework.

The key distinction lies in the autonomous operation of these systems. While traditional AI applications respond to pre-defined commands within rigid workflows, agentic AI systems operate more independently, dynamically adapting to changing conditions and optimizing processes in real-time. This represents a fundamental shift in enterprise computing architecture, requiring a rethinking of resource allocation and system interaction. As Zhang noted, the challenge for enterprises is to build and deploy infrastructure capable of supporting this level of autonomy.

Infrastructure Bottlenecks Spur Innovative Architectures

The computational intensiveness of agentic AI, particularly the training and inference of large language models (LLMs), has exposed limitations in conventional cloud architectures. The demand for compute cycles and high-bandwidth data transfer is pushing the boundaries of existing infrastructure.

Huawei Cloud’s response is the CloudMatrix384, a network of interconnected supernodes linked via a high-speed MatrixLink network. This forms the basis of a hybrid compute system that integrates general-purpose and AI-optimized processing capabilities. Specifically, the architecture tackles bottlenecks inherent in Mixture of Experts (MoE) models through expert parallelism inference. This technique aims to minimize NPU idle time during data transfer, reportedly boosting single-PU inference speeds by a factor of 4-5 compared to other popular architectural approaches.

The system also incorporates an AI-Native Storage solution tailored for AI workloads, designed to enhance both training and inference throughput. This infrastructure forms the foundation for companies like ModelBest to demonstrate practical use cases. Li Dahai, CEO of ModelBest, stated that their MiniCPM series of models – encompassing foundation models, multi-modal capabilities, and full-modality integration – leverages Huawei Cloud AI Compute Service to achieve a 20% improvement in training energy efficiency and a 10% performance increase compared to industry benchmarks. The MiniCPM models are finding applications in diverse areas such as automotive systems, smartphones, embodied AI, and AI-powered PCs.

Tailoring Foundation Models for Industry Verticals

A significant hurdle in widespread AI adoption is adapting general-purpose foundation models to the specific nuances of individual industries. Huawei Cloud is addressing this through a structured training methodology that comprises data pipelines, incremental training workflows, and smart evaluation platforms.

The incremental training workflow claims a 20-30% performance increase through automated adjustments to data and training parameters based on key model features and industry-specific requirements. A Smart evaluation platform streamlines the benchmarking process, allowing companies to quickly assess performance against industry or internal standards, focusing on both accuracy and speed. This allows for a rapid feedback loop, essential for continuous model refinement.

Shaanxi Cultural Industry Investment Group’s partnership with Huawei showcases the application of these methodologies in the cultural tourism sector. By leveraging Huawei Cloud’s data-AI convergence platform, they consolidated disparate cultural tourism data, encompassing historical archives, film assets, and intangible heritage resources. This resulted in the creation of a “trusted national data space for cultural tourism” on Huawei Cloud, powering applications such as asset verification, copyright transactions, enterprise credit enhancement, and creative development. This collaboration spawned the Boguan cultural tourism model, which supports AI-driven tools including a cultural tourism intelligent brain, smart management assistant, intelligent travel assistant, and an AI-powered short video platform.

Dubai Municipality’s collaboration with Huawei Cloud demonstrates a similar pattern, integrating foundation models, virtual humans, digital twins, and geographical information systems to improve urban system management. These tools aim to enhance city planning, facility management, and emergency response capabilities.

Enterprise-Grade AI Agents Take Center Stage

A critical divergence exists between consumer-facing AI assistants and enterprise-grade agentic AI systems. Enterprise systems demand seamless integration within existing workflows, the ability to handle complex scenarios, and adherence to stringent operational standards surpassing those of consumer applications designed for quick interactions.

Huawei Cloud’s Versatile platform is positioned to address this gap, offering infrastructure for businesses to create agents tailored to specific production needs. By integrating AI compute, models, data platforms, tools, and ecosystem resources, the platform aims to streamline agent development throughout deployment, release, usage, and management phases.

The Conch Group’s implementation within its cement manufacturing operations provides concrete performance metrics. By developing what they describe as the cement industry’s first AI-powered cement and building materials model, Conch Group is seeing tangible benefits. The resulting agents can predict clinker strength at 3 and 28 days with predictions deviating less than 1 MPa from actual values, indicating over 90% accuracy. Moreover, for cement calcination optimization, the model provides process parameters and solutions to cut standard coal usage by 1% compared to stringent energy efficiency standards.

Smartcom, a travel management company, has developed a travel assistant utilizing Huawei Cloud Versatile that aims to provide end-to-end services. Kong Xianghong, CTO of Shenzhen Smartcom and Director of Smartcom Solutions, stated that the system analyzes travel industry data, company policies, and individual trip histories to generate customized recommendations. According to Smartcom, employees adopt more than half of these suggestions. The AI agent also resolves 80% of customer support issues within an average of three interactions using predictive question matching.

The Future of Autonomous AI

The applications discussed at the summit reflect an industry-wide trend toward agentic AI systems capable of increasingly autonomous operation within clearly defined parameters. This move from reactive tools to systems capable of planning and executing complex tasks independently represents a substantial architectural shift in enterprise computing.

However, this transition necessitates significant investments in infrastructure, sophisticated data engineering, and careful integration with existing business processes. The performance metrics observed in early deployments – including manufacturing efficiency gains, improvements to urban management, and streamlined travel booking processes – provide valuable benchmarks for organizations considering similar implementations. As agentic AI matures, the industry focus appears to be moving from a demonstration of technological capability to addressing the challenges of operational integration, establishing governance frameworks, and demonstrating measurable business outcomes. These 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 functioning as generic automation tools.