How Huawei is building agentic AI systems that make decisions independently

In a cement plant operated by Conch Group, an agentic AI system constructed on Huawei infrastructure now predicts the energy of clinker with over 90% accuracy and autonomously adjusts calcination parameters to chop coal consumption by 1%—decisions that beforehand required human experience gathered over many years

This exemplifies how Huawei is creating agentic AI systems that transfer past easy command-response interactions towards platforms able to unbiased planning, decision-making, and execution.

Huawei’s method to building these agentic AI systems centres on a complete technique spanning AI infrastructure, basis fashions, specialised instruments, and agent platforms.

Zhang Yuxin, CTO of Huawei Cloud, outlined this framework on the latest Huawei Cloud AI Summit in Shanghai, the place over 1,000 leaders from politics, enterprise, and know-how examined practical implementations throughout finance, delivery ports, chemical manufacturing, healthcare, and autonomous driving.

The distinction issues as a result of conventional AI purposes reply to consumer instructions inside mounted processes, whereas agentic AI systems function with autonomy that basically modifications their position in enterprise operations.

Zhang characterised this as “a significant shift in purposes and compute,” noting that these systems make decisions independently and adapt dynamically, reshaping how computing systems work together and allocate assets. The query for enterprises turns into: how do you construct infrastructure and platforms able to supporting this degree of autonomous operation?

Infrastructure challenges drive new computing architectures

The computational calls for of agentic AI systems have uncovered limitations in conventional cloud architectures, significantly as basis mannequin coaching and inference necessities surge.

Huawei Cloud’s response entails CloudMatrix384 supernodes linked by means of a high-speed MatrixLink community, creating what the corporate describes as a versatile hybrid compute system combining general-purpose and clever compute capabilities.

The structure particularly addresses bottlenecks in Mixture of Experts (MoE) fashions by means of skilled parallelism inference, which reduces NPU idle time throughout knowledge transfers. According to the firm’s technical specs, this method boosts single-PU inference pace 4-5 occasions in comparison with different standard fashions.

The system additionally incorporates memory-centric AI-Native Storage designed for typical AI duties, geared toward enhancing each coaching and inference effectivity. ModelBest, an organization specialising in general-purpose AI and machine intelligence, demonstrated sensible purposes of this infrastructure.

Li Dahai, co-founder and CEO of ModelBest, detailed how their MiniCPM collection—spanning basis fashions, multi-modal capabilities, and full-modality integration—integrates with Huawei Cloud AI Compute Service to realize 20% enhancements in coaching power effectivity and 10% efficiency positive aspects over {industry} requirements.

The MiniCPM fashions have discovered purposes in automotive systems, smartphones, embodied AI, and AI-enabled private computer systems.

From basis fashions to industry-specific purposes

The problem of adapting basis fashions for particular {industry} wants has pushed the event of extra refined coaching methodologies. Huawei Cloud’s method encompasses three key parts: a full knowledge pipeline dealing with assortment by means of administration, a ready-to-use incremental coaching workflow, and a sensible analysis platform with preset analysis units.

The incremental coaching workflow reportedly boosts mannequin efficiency by 20-30% by means of computerized adjustment of information and coaching settings based mostly on core mannequin options and industry-specific goals. The analysis platform allows fast setup of systems aligned with {industry} or firm benchmarks, addressing each accuracy and pace necessities.

Real-world implementations illustrate the sensible software of those methodologies. Shaanxi Cultural Industry Investment Group partnered with Huawei to combine AI with cultural tourism operations.

Huang Yong, Chairman of Shaanxi Cultural Industry Investment Group, defined that utilizing Huawei Cloud’s data-AI convergence platform, the organisation mixed numerous cultural tourism knowledge to create complete datasets spanning historical past, movie, and intangible heritage.

The partnership established what they time period a “trusted nationwide knowledge house for cultural tourism” on Huawei Cloud, enabling purposes together with asset verification, copyright transaction, enterprise credit score enhancement, and inventive growth.

The collaboration produced the Boguan cultural tourism mannequin, which powers AI-driven instruments, together with a cultural tourism clever mind, sensible administration assistant, clever journey assistant, and an AI brief video platform.

International implementations reveal comparable patterns. Dubai Municipality labored with Huawei Cloud to combine basis fashions, digital people, digital twins, and geographical info systems into city systems. Mariam Almheiri, CEO of the Building Regulation and Permits Agency at Dubai Municipality, shared how this integration has improved metropolis planning, facility administration, and emergency responses.

Enterprise-grade agent platforms emerge

The distinction between consumer-focused AI brokers and enterprise-grade agentic AI systems centres on integration necessities and operational complexity. Enterprise systems should seamlessly combine into broader workflows, deal with advanced conditions, and meet greater operational requirements than shopper purposes designed for fast interactions.

Huawei Cloud’s Versatile platform addresses this hole by offering infrastructure for companies to create brokers tailor-made to manufacturing wants. The platform combines AI compute, fashions, knowledge platforms, instruments, and ecosystem capabilities to streamline agent growth by means of deployment, launch, utilization, and administration phases.

Conch Group’s implementation in cement manufacturing presents particular efficiency metrics. The firm partnered with Huawei to create what they describe because the cement {industry}’s first AI-powered cement and building supplies mannequin.

The ensuing cement brokers predict clinker energy at 3 and 28 days with predictions deviating much less than 1 MPa from precise outcomes, representing over 90% accuracy. For cement calcination optimisation, the mannequin suggests key course of parameters and operational options that minimize customary coal utilization by 1% in comparison with class A power effectivity requirements.

Xu Yue, Assistant to Conch Cement’s General Manager, famous that the mannequin’s success with high quality management, manufacturing optimisation, gear administration, and security establishes groundwork for end-to-end collaboration and decision-making by means of cement brokers, transferring the {industry} “from counting on conventional experience to being totally pushed by knowledge throughout all processes.”

In company journey administration, Smartcom developed a journey agent utilizing Huawei Cloud Versatile that offers end-to-end sensible providers throughout departure, transfers, and flights. Kong Xianghong, CTO of Shenzhen Smartcom and Director of Smartcom Solutions, reported that the system combines journey {industry} knowledge, firm insurance policies, and particular person journey histories to generate suggestions.

Employees undertake over half of those strategies and full bookings in beneath two minutes. The agent resolves 80% of points in a median of three interactions by means of predictive query matching.

What’s subsequent for autonomous AI?

The implementations mentioned on the summit replicate a broader {industry} pattern towards agentic AI systems that function with growing autonomy inside outlined parameters. The know-how’s development from reactive instruments to systems able to planning and executing advanced duties independently represents a basic architectural shift in enterprise computing.

However, the transition requires substantial infrastructure investments, refined knowledge engineering, and cautious integration with present enterprise processes. The efficiency metrics from early implementations—whether or not in manufacturing effectivity positive aspects, city administration enhancements, or journey reserving optimisation—present benchmarks for organisations evaluating comparable deployments.

As agentic AI systems proceed to mature, the main target seems to be shifting from technological functionality demonstrationsto operational integration challenges, governance frameworks, and measurable enterprise outcomes. The examples from cement manufacturing, cultural tourism, and company journey administration recommend that sensible worth emerges when these systems deal with particular operational ache factors quite than serving as general-purpose automation instruments.

(Photo by AI News )

See additionally: Huawei details open-source AI development roadmap at Huawei Connect 2025

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