In a cement plant operated by Conch Group, one of China’s largest building materials companies, an agentic AI system built on Huawei infrastructure now predicts clinker strength with over 90% accuracy and autonomously adjusts calcination parameters to reduce coal consumption by about 1%. This system no longer acts as a passive, command-response tool—it actively makes decisions, plans, and optimizes operations.
That example captures the shift Huawei is pushing: from fixed AI assistants to agentic systems that operate autonomously within enterprises. To support this, Huawei is investing in new compute architectures, model tooling, vertical adaptation, and enterprise agent platforms.
This article explores Huawei’s strategy and how it is being applied in real industrial settings. We examine infrastructure innovations, model specialization, industrial use cases, and platform evolution, as well as challenges and outlooks.
What Is Agentic AI — and Why it Matters
From Reactive Tools to Autonomous Agents
Traditional AI applications tend to follow a reactive paradigm: “you ask, it answers” or “you supply input, it returns output.” In that model, humans orchestrate the flow.
Agentic AI, by contrast, embeds autonomy, planning, and decision-making into the system. Agents perceive, decide, act, monitor, and adapt—even coordinating multiple sub-agents over time. They can manage workflows, optimize processes, or drive decisions dynamically rather than simply respond to prompts.
Huawei frames this leap as “a major shift in applications and compute.” Agentic systems reshape how computing allocates resources, interacts with systems, and adapts to uncertainty.
The Strategic Imperative for Enterprises
Why is agentic AI important for industrial and enterprise users?
- Operational efficiency: Agents can autonomously adjust parameters, detect anomalies early, and close feedback loops faster than humans.
- Scale and complexity: In complex systems (e.g. manufacturing, energy, infrastructure), coordinating many parameters and sub-systems is infeasible manually.
- Reduced latency: Agents embedded close to operations (edge + cloud) can act in near real-time.
- Human assistance: Agents augment human expertise, taking over monitoring, analysis, and decision tasks so humans focus on oversight and strategy.
Huawei’s Conch example shows these benefits in action: by predicting strength and adjusting calcination automatically, the AI agent reduced fuel consumption while preserving product quality.
Huawei’s Agentic AI Strategy: Infrastructure, Models, Tools, and Platforms
To support agentic AI, Huawei’s approach spans multiple layers—computing, models, domain adaptation, and agent platforms.
Infrastructure: CloudMatrix, MoE Parallelism, Memory-Centric Storage
Agentic systems demand more from infrastructure than static LLM usage. Huawei’s responses include:
- CloudMatrix384 supernodes with MatrixLink interconnect: designed to support hybrid compute (general + intelligent) and scale inference clusters.
- Expert parallelism in MoE models: to avoid idle NPUs during routing, Huawei applies expert-level parallelism in inference, improving single-PU throughput 4–5× over models with naive routing.
- Memory-centric AI-native Storage & Unified Cache Manager: To reduce first-token latency and enable persistent memory, Huawei has built a unified caching technique that moves data between long-term memory and fast working memory, reducing latency by up to 90%.
- Distributed cloud architecture: Huawei Cloud supports multi-scenario deployments (CloudOcean, CloudSea, CloudLake, CloudPond) for edge, regional, and centralized compute.
These architectural innovations aim to support not just model inference, but continuous agent operations and large-scale orchestration.
Models & Vertical Specialization
Building autonomous agents requires foundation models that can reason, plan, and contextualize within domains. Huawei’s efforts include:
- Pangu prediction models: These models integrate heterogeneous data—tabular, time-series logs, and visual inspection—to produce accurate predictions in industrial domains. For example, Conch uses a Pangu-based model to predict 3-day and 28-day clinker strength with ±1 MPa deviation and better than 85% accuracy.
- Mixing domain and operating condition models (e.g. M-MoE architecture): Conch’s AI combines general feature models with domain-specific sub-models (operating condition models) for stronger predictions in process control.
- Incremental training workflows: Huawei supports pipelines that allow models to adapt with vertical data, recalibrate to industry needs, and continuously improve performance—boosting effectiveness by 20–30%.
- Vertical evaluation & scenario templates: Prebuilt evaluation sets aligned with industry KPIs (e.g. energy, yield, reliability) help accelerate deployment.
Platform & Agent Tooling: Versatile & ACT Pathway
To support agent deployment and lifecycle management, Huawei offers:
Versatile agent platform: An enterprise-grade agent platform that automates creation, orchestration, monitoring, and scaling of agent workflows. Users can define business description documents or flowcharts and generate agents in two steps.
High-step workflows (>100 steps): The platform supports complex processes with many decision nodes, branching logic, and integrations.
High-performance sandbox & full-memory cache: To reduce latency and ensure responsiveness, Versatile uses sandbox isolation and caching techniques, reportedly cutting response latency >40%.
ACT pathway (Assess, Calibrate, Transform): Huawei’s framework guiding enterprise AI adoption.
- Assess high-value scenarios and maturities
- Calibrate general models using vertical data
- Transform business operations with scaled agents
This pathway maps strategic adoption steps for businesses.
Ecosystem & Incentives
Huawei also invests in developer and partner ecosystems:
- Incentive programs offering free compute, development credits, agent platform access, and partner support.
- Agent Zone and AI marketplaces via Developer Space / KooGallery, enabling sharing of models, agents, and tools.
- AI security and governance: End-to-end security protection across cloud, network, edge, model, and application layers is built in to ensure trust and compliance in agent operations.
Industrial Use Cases & Results
Huawei and its partners have begun deploying agentic systems in manufacturing, urban management, and business automation.
Cement & Building Materials — Conch Group
This is perhaps the flagship example:
- Conch and Huawei launched a joint AI model for the cement/building materials industry, covering quality control, production, equipment management, and safety across 21 scenarios.
- The AI model predicts clinker strength 3-day and 28-day within ±1 MPa deviation, with >85% accuracy.
- Huawei developed a global optimization model for kiln burning (calculation of key parameters), enabling real-time operational recommendations, which allegedly reduced fuel (coal) usage by ~1% relative to Class A benchmarks.
- In earlier reporting, Huawei had used a Multi-gate Mixture-of-Experts (M-MoE) model combining foundation and condition models.
- This demonstrates a shift from reactive quality control to proactive process regulation.
Urban Management & Cultural Tourism
- Shaanxi Cultural Industry (China) used Huawei Cloud’s data-AI convergence to build a “cultural tourism intelligent brain,” combining heritage, media, location, and historical data. This supports applications like virtual assistants, intelligent planning, content generation, and asset verification.
The project is built on Huawei’s infrastructure and enables smart management for cultural resources. - Dubai Municipality: Huawei worked with Dubai to integrate foundation models, virtual humans, digital twins, and GIS data to improve facility management, city planning, and emergency response systems.
Travel & Business Automation
- Smartcom Travel Agent: Using Huawei Cloud Versatile, Smartcom built a travel agent that integrates company policies, historic travel data, and booking ecosystems. Over 50% of users adopted its recommendations, with bookings completed in <2 minutes. The agent resolved ~80% of issues with ~3 interactions.
These deployments show agentic AI being used not just for prediction, but for real-time decisioning, orchestration, and user-facing automation.
Architectural & Operational Challenges
While promising, agentic systems also bring challenges—many of which Huawei explicitly addresses.
Integration & Data Pipelines
Agents need secure, consistent access to enterprise systems (ERP, SCADA, MES, CRM). Building connectors that respect permissions, currency, and latency is nontrivial. Huawei builds native connectors and unified agent integration in Versatile.
Safety, Governance & Oversight
Autonomous agents can make mistakes, so governance is critical. Huawei includes an AI security protection system across cloud, network, edge, model and application layers.
Auditing, rollback, model version control, sandboxing, and anomaly detection must be part of a trusted stack.
Cost, Latency & Efficiency
Agentic operations may call many sub-models, trigger workflows, and access data repeatedly. Huawei mitigates that via caching, memory-centric storage, unified cache, and low-latency memory systems.
Model Adaptation & Domain Gaps
Foundation models often lack domain specificity. Huawei’s solution is the “Calibrate” step in its ACT pathway—fine-tune with vertical data, incremental training, and hybrid models.
System Reliability & Fault Tolerance
Many industrial systems require high uptime. Agentic systems must handle failures, fallbacks, drift, and cascading dependencies. The infrastructure must support fault detection and recovery.
Strategic Insights & Recommendations
For enterprises considering agentic AI, here are key insights drawn from Huawei’s strategy and deployments:
Start with high-value narrow domains
Use agents to automate critical subsets (e.g. kiln optimization, travel booking) rather than full system takeover.
Invest in data pipelines and domain models early
High-quality data and vertical model calibration underpin reliable agent execution.
Adopt governance and audit from day one
Autonomous agent action must be accountable; include logging, rollback, permission tiers.
Leverage infrastructure designed for agents
Standard cloud infrastructure may fail at scale—memory-centric storage, caching, MoE routing, and agent orchestration layers are important.
Iterative deployment with human oversight
Begin with human-in-the-loop supervision until agents achieve required trust levels.
Plan for evolving agent ecosystems
Over time, agents will need updating, retraining, and extension—architecture for modularity and maintainability.
Align with strategic AI pathways
Huawei’s ACT pathway (Assess → Calibrate → Transform) is a useful conceptual framework for adoption.
Outlook: The Future of Enterprise Intelligence
Huawei’s agentic AI ambition points to a broader shift: the next generation of enterprise systems will be conversational, autonomous, and deeply integrated. Rather than separate AI applications, we will see fleets of agents embedded in operations, planning, and decision flows.
The Conch Cement example illustrates that agent-mediated industrial control is feasible—with measurable gains in fuel efficiency and product quality. As foundational infrastructure (e.g. CloudMatrix), model tooling, agent platforms (Versatile), and governance stacks mature, more enterprises will move from pilot experiments into production.
However, business value will flow not from generic AI agents, but domain-aware, tightly integrated agents solving core pain points. Agentic AI will succeed when it aligns with specific operational challenges, benefits from context, and operates with reliability and trust.
Huawei’s integrated stack—from compute to models to agent platforms—shows one path forward. The true test will be in scaling these systems reliably across many industries, refining agent architectures, and evolving governance frameworks that balance autonomy with control.