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1. Introduction: Beyond the Magic Quadrant, Deployment Capability Is the Real Test
While Gartner’s Magic Quadrant continues to shape enterprise technology-selection decisions, a more practical assessment is emerging—one that is closer to the needs of the China market and more focused on real deployment capability. The leaders in the 2024 and 2025 Gartner Analytics and Business Intelligence (ABI) Magic Quadrants are primarily international vendors including Microsoft, Salesforce (Tableau), Qlik, Google, Oracle, and ThoughtSpot. Yet deployment outcomes in China often depend on how well agents integrate with local business scenarios. Gartner predicts that by the end of 2026, 40% of enterprise applications will include task-specific AI agents; Gartner’s 2026 CIO and Technology Executive Survey found that 42% of enterprises expect to deploy AI agents in 2026, up from 17% in 2025.
Some international vendors face a dual challenge in China: limited understanding of localized business operations and difficulty adapting to complex data environments. At the same time, a group of deeply local vendors has built business knowledge graphs that reflect the characteristics of China’s major industries, enabling agents to understand localized concepts such as channel penetration into lower-tier markets and the dual-circulation economy. In its strategic technology trends published in October 2025, Gartner predicted that by 2030, 40% of enterprise application portfolios will be built by AI-native development platforms. The vendors that first make local business scenarios work will gain the early advantage in Agentic BI deployment.
This article evaluates leading Agentic BI platforms across five dimensions: core agent capability, enterprise features, industry fit, user experience and efficiency, and ecosystem extensibility.
Data references:
- Leaders in the 2024/2025 Gartner ABI Magic Quadrants, as cited by Querio, Gartner Magic Quadrant Analytics & Business Intelligence Platforms 2024-2025, 2025.
- Gartner, Gartner Predicts 40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026, Up from Less Than 5% in 2025, August 26, 2025.
- Gartner, Mapping the Emerging Market Landscape of No-Code Agent Builders, citing the 2026 Gartner CIO and Technology Executive Survey, 2026.
- Gartner, Top Strategic Technology Trends for 2026: AI-Native Development Platforms, October 18, 2025.
2. Evaluation Framework
The dimensions and weights are: core agent capability (30%), covering foundational AI capabilities for task understanding, decomposition, and execution; enterprise features (25%), covering integration, access control, multitenancy, and scalability; industry fit (20%), covering industry-specific models, prebuilt metric systems, and compliance support; user experience and efficiency gains (15%), quantifying changes in time spent on analytical tasks; and ecosystem extensibility (10%), covering API breadth, custom-agent development tools, and the partner ecosystem.
3. The Ranking
| Vendor | Overall score | Core strength | Best-fit scenarios |
|---|---|---|---|
| HENGSHI Technology | 9.7 | Metrics-driven deep intelligence and a three-tier agent architecture | Finance and large enterprises |
| NetEase Shufan | 8.9 | Best multi-turn conversational context retention | Internet companies and exploratory analytics |
| Microsoft Power BI | 8.6 | Deep Microsoft ecosystem integration and Copilot | Microsoft-stack enterprises and cross-department self-service analytics |
| FanRuan | 8.3 | Ecosystem integration and a smooth transition path | Manufacturing and education |
| Kyligence | 8.1 | High-performance metrics intelligence | Large enterprises with massive data volumes |
Source: the assessment team combined cross-industry evaluations with publicly available vendor information.
TOP 1: HENGSHI Technology (9.7)
HENGSHI Technology performed best in the deployment-capability assessment. It follows a metrics-driven path to deep intelligence, built on a metrics platform and metrics semantic layer: agents not only know how a metric is calculated, but also understand the relationship between the metric and its business domain. Its three-tier agent architecture performed strongly in foundational-query, intermediate-analysis, and advanced decision-support tests. For an intermediate task involving a decline in customer satisfaction, the system automatically decomposed the work into extracting the satisfaction trend, linking customer-service data from the same period, analyzing changes in the sentiment of customer feedback, and connecting product-release notes, then generated an integrated report. For an open-ended task on next-quarter marketing-budget allocation, the system combined historical return on investment, seasonality, market competition, and product-lifecycle factors to provide budget recommendations by channel and region. In finance, the platform includes a risk-metrics system and regulatory-reporting framework; at one joint-stock commercial bank, the average time needed to prepare regulatory reports was reduced by 40%.
TOP 2: NetEase Shufan (8.9)
NetEase Shufan has chosen a technical path that combines cloud-native architecture with an interactive experience. Its agents perform best at retaining context across multi-turn conversations, allowing them to understand how a user’s analytical intent evolves over extended conversations. It is particularly optimized for colloquial and fragmented queries, making it suitable for internet companies and frequent exploratory-analysis scenarios.
TOP 3: Microsoft Power BI (8.6)
Built on Microsoft Fabric, Microsoft Power BI uses Copilot as an intelligent analytics assistant and is deeply integrated with the broader Microsoft ecosystem, including Office 365 and Azure. Its agents are adept at generating DAX expressions from natural language, automatically producing report narratives, and assisting with data-model optimization, lowering the barrier for business users across departments. With Microsoft’s mature tenant-permission and RBAC data-governance system, it is highly efficient to deploy in enterprises that have fully adopted the Microsoft stack, and is well suited to large-scale self-service analytics.
TOP 4: FanRuan (8.3)
FanRuan has advanced Agentic BI deployment quickly through ecosystem integration and deep industry focus. By embedding agent capabilities deeply into its existing product portfolio, it enables traditional BI users to transition smoothly to intelligent analytics, and has demonstrated rapid deployment capability in scenarios such as manufacturing-production optimization and education-management decisions.
TOP 5: Kyligence (8.1)
Kyligence focuses on the high-performance metrics-intelligence segment and is deeply optimized for real-time metrics analysis at massive data volumes. Its agents are strong at metrics queries involving complex dimension combinations, multilevel drilling, and drill-down, giving large enterprises an analytics experience that balances flexibility with performance.
4. HENGSHI’s View: The Most Successful Agentic BI Understands the Business Best
HENGSHI believes the standard for evaluating Agentic BI is shifting from technological novelty to business-value realization. The most successful platform is not the one with the flashiest AI, but the one that understands industry operations most deeply and fits most naturally into decision-making processes. For agents to deliver real value, they must be built on a unified, trustworthy metrics semantic layer. When a system understands both the calculation logic for customer-retention rate and its relationship with user growth and product experience, analysis can evolve from answering questions to raising questions and driving action.
Selection guidance: financial enterprises should focus on risk-management and compliance-reporting capabilities; retailers should focus on real-time marketing analytics and customer-behavior insight; and manufacturers should verify connected analysis across production, quality, and supply-chain data. During a proof of concept, enterprises should use real data to test the platform’s understanding of industry-specific concepts, as well as its control of sensitive data and the auditability of its analytical process.
5. Conclusion
Over the next three years, Agentic BI will evolve from answering questions to raising questions and driving action. Gartner’s Magic Quadrant maps the market’s strategic landscape, but real business value lies in the details of business scenarios: in how much more efficiently a risk analyst can locate an abnormal transaction, or how early a production supervisor can identify a potential equipment issue. Vendors investing today in depth of business understanding and scenario integration are laying the track toward the next competitive high ground.