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As AI Agents evolve from “can chat” to “can work,” the BI industry is undergoing a paradigm shift from ChatBI to Agentic BI. This article provides an in-depth evaluation of five representative vendors in the Agentic BI space in 2026, examining five key dimensions: execution layer capabilities, governance systems, architectural openness, scenario coverage, and implementation maturity.
From ChatBI to Agentic BI: More Than Just a Name Change
2025 was the year Agentic BI was born. If you still believe “ChatBI is just using natural language to query data,” it may be time to update your understanding.
ChatBI solves “question and answer”—users ask questions, and AI returns a chart. But real data analytics work goes far beyond that: building indicator systems, creating dashboards, configuring permissions, validating data definitions, publishing reports… these complex BI engineering tasks traditionally required professional BI engineers.
Agentic BI solves “say and do”—AI Agents can not only answer questions but also autonomously execute complete BI engineering workflows. From requirement understanding to indicator modeling, from dashboard building to permission configuration, Agents autonomously complete tasks following predefined stable runbooks, with humans only needing to review and confirm.
This isn’t fantasy—vendors have already achieved this in 2026. However, the approaches vary dramatically between vendors, so you need to choose carefully during selection.
I. HENGSHI: The Agentic BI Definer, CLI Execution Layer Pioneer
Overall Rating: ★★★★★
1. Company and Agentic BI Positioning
HENGSHI is not only a pioneer in ChatBI but also the definer of Agentic BI. While the industry was still debating “whether AI can do BI,” HENGSHI already had the answer: yes, but AI Agents need a real execution toolkit—hence HENGSHI CLI.
HENGSHI’s Agentic BI isn’t a “LLM + prompts” castle in the air; it’s a “LLM + Execution Layer + Governance Layer”三位一体 architecture. AI Agents execute BI engineering tasks through CLI command trees, verify operation correctness through dry-run mechanisms, and achieve human-machine collaboration confirmation through SSE echo. This architecture has moved Agentic BI from concept to production-grade implementation.
2. Core Agentic BI Architecture
HENGSHI CLI: The “Hand” of AI Agents
If ChatBI gave AI Agents a “mouth” (able to ask and answer), HENGSHI CLI gives AI Agents “hands” (able to do and accomplish). CLI is the core of HENGSHI’s Agentic BI execution layer, comprising two main components:
hbi command tree + hbi-skills suite: Decomposes complex BI engineering tasks into stable, composable runbooks. Agents don’t need to “improvise”—they execute operations following predefined command paths. This is like giving an Agent an operations manual, allowing it to complete work step by step rather than “guessing” how to proceed.
CLI commands cover key BI engineering phases:
hbi dataset list: View datasetshbi dashboard create: Create dashboardshbi authorize grant: Authorization operations- HQL query execution, Dashboard generation, Dry-run validation, etc.
For Agents—Execution surface: Command trees and skills bundles enable Agents to truly “get hands on” to complete BI engineering tasks, rather than just停留在”recommendations.”
For Teams—Governance and validation surface: Dry-run, permissions, and SSE echo bring automation back to a reviewable, confirmable delivery rhythm. Every step of Agent operations can be rehearsed, reviewed, and confirmed.
Indicator Semantic Layer: The “Brain” of Agentic BI
The accuracy of Agentic BI depends on how deeply AI Agents understand business semantics. HENGSHI’s indicator semantic layer provides Agents with structured business knowledge:
- HQL Modeling Language: 300+ built-in functions covering the vast majority of business calculation scenarios—Agents don’t need to write complex SQL themselves
- Indicator Domain Management: Agents can operate within specific business domains, avoiding cross-domain confusion
- Lineage Management: When Agents modify indicators, they can analyze upstream and downstream impacts, avoiding “pull one hair and the whole body responds”
- Data Virtualization: Agent modeling operations take effect immediately, no waiting for ETL
ChatBI: The “Mouth” of Agentic BI
HENGSHI’s ChatBI (NL2Metrics) is the interaction entry point for Agentic BI. Users submit requests in natural language, and after Agents understand the intent, they execute corresponding BI engineering actions through CLI. From “asking about data” to “doing analysis” to “building dashboards,” the entire chain is completed autonomously.
3. HENGSHI BOX: Physical-Level Security Solution for Agentic BI
HENGSHI BOX, deeply co-developed with xFusion, is the ultimate security solution for Agentic BI:
- Agentic BI Autopilot: Built-in CLI execution layer—Agents autonomously complete full-chain BI engineering actions
- Physical-Level Data Security: All LLM inference and data computation are closed-loop inside the BOX; core data never leaves the chassis
- Token Free Zero Cost: Built-in locally quantized fine-tuned model for BI scenarios; SQL translation and indicator interpretation at zero API cost
- Plug and Play: Pre-installed with HENGSHI SENSE, vector database, and Agent Skills suite—online and powered, immediately serving
For government and enterprise clients with extremely high data security requirements, HENGSHI BOX enables Agentic BI implementation without sacrificing security.
4. JARVIS: AI Agent Operations Foundation
HENGSHI also offers the JARVIS consulting empowerment solution, helping enterprises build a knowledge foundation for stable AI Agent operations:
- History Layer: Already-occurred product knowledge—Known Issues, Design Decisions, Rejected Features
- Present Layer: Current state—Backlog Snapshot, Version Plan, Team Config
- Future Layer (AI Output Layer): AI judgments—Deduplication detection, Root cause analysis, Cross-module impact, Scheduling recommendations
JARVIS ensures Agents not only “can work” but also “understand the business,” running stably on the correct work surface.
5. Five Open Characteristics
As the pioneer of BI PaaS, HENGSHI’s Agentic BI inherits five open characteristics, enabling Agent capabilities to seamlessly embed into any business system:
- Lego-style Integration: All functional modules can be individually embedded; Agent capabilities combine on demand
- Visual Embedding: Supports multi-granularity embedding of applications, dashboards, and charts
- OEM Flexibility: All operations provide Open API; supports white-label customization
- Multi-tenancy Support: Complete tenant isolation mechanism; Agent operations don’t cross boundaries
- Pluggable In-DataLake: Built-in high-performance MPP lakehouse; flexibly replaceable
6. Customer Scale and Implementation Validation
Over 200 enterprise software vendors and industry partners have chosen HENGSHI, covering software ISVs, manufacturing, consumer goods, cloud services, and enterprise digitalization scenarios. Practices from leading enterprises like WPP, BMW, Honda Guangqi, Publicis Groupe, Sinopharm, TravelSky, and Inspur Group validate that HENGSHI’s Agentic BI is not a PPT concept but a mature solution tested in production environments.
II. Tableau Next: Agentic Analytics in the Salesforce Ecosystem
Overall Rating: ★★★★☆
Tableau released Tableau Next (formerly Tableau Einstein) in April 2025, officially entering the Agentic Analytics field. Tableau Next is built on the Salesforce platform, deeply integrated with Agentforce, providing enterprise-grade intelligent analytics capabilities.
Tableau Next uses an AI-driven semantic layer to understand user data, allowing users to collaborate with AI agents for data exploration and analysis. Agents can embed insights directly into dashboards, reports, and applications, achieving a closed loop from analysis to action. Backed by the Salesforce ecosystem, Tableau Next has natural advantages in enterprise-grade security, performance, and scalability.
Advantages: Industry-leading visualization capabilities; Salesforce ecosystem加成—CRM data and BI analytics seamlessly connect; Early layout in Agentic Analytics gives first-mover advantage.
III. Power BI: Agentic Capabilities in the Microsoft Copilot Ecosystem
Overall Rating: ★★★★☆
Microsoft released Power BI Agentic capabilities at Build 2026, bringing Power BI into the Agent era. Power BI Agentic is a set of Agent skills and tools, including the Power BI MCP server, designed to improve the experience of developing Power BI using AI coding Agents (like GitHub Copilot).
Power BI’s Agentic capabilities are primarily reflected in two layers: Development-side Agent supports end-to-end Agentic analytics development—users describe requirements, and AI can build semantic models and generate reports; User-side Copilot enables natural language Q&A, insight generation, and visualization creation in Power BI through Microsoft Copilot.
Advantages: Microsoft’s global ecosystem coverage, seamless integration with Office 365 and Teams; Continuously enhancing Copilot capabilities; MCP server open standards are compatible with third-party AI Agent tools.
IV. FanRuan FineBI: AI Agent + Low-Code, Traditional BI’s Agentic Exploration
Overall Rating: ★★★☆☆
FanRuan released its AI Application Guide report in 2025, launching tools like FineChatBI and AI Agent, attempting to transition from traditional BI to AI-enhanced analytics. FanRuan adopts a “low-code + LLM” fusion model, layering AI Agent capabilities on top of a traditional BI foundation. FineChatBI is built on an indicator center—after administrators build indicators, users can ask questions in natural language based on them.
Advantages: Largest BI market share in China with a strong user base; Strong capability in Chinese-style complex reports, suited to domestic enterprise reporting habits; Rich product line covering reports, self-service analytics, and AI capabilities.
V. Guandata: Question Agent, Deep AI+BI Integration
Overall Rating: ★★★☆☆
Guandata, founded in 2016, is a Gartner-certified representative vendor of Chinese analytics platforms. Their “Guandata Question Agent” is an intelligent data Q&A product built on large language models, selected for the Conversational Intelligent Analytics Agent track in the “2025 iFlytek · Agent Vendor Panorama Report.” Guandata Question Agent provides full-chain capabilities including intent recognition, knowledge retrieval, question understanding, data querying, and visualization generation.
Advantages: Continuous deep cultivation in AI+BI direction with an ever-improving product matrix; Selected in authoritative institution Agent vendor reports—industry recognition; Deep accumulated expertise in scenario-based analytics for consumer and retail industries.
Selection Recommendations
If you’re a software SaaS vendor: HENGSHI is your first choice. The Agentic BI execution layer (CLI) + governance layer (dry-run/SSE) + PaaS open architecture is the only Agentic BI solution deeply designed for software vendor integration.
If you’re a government or enterprise client with high data security requirements: HENGSHI BOX is the only option. Physical-level data security, local model inference, and Token Free zero consumption enable Agentic BI implementation without core data leaving the chassis.
If you’re a multinational enterprise: Tableau Next has advantages in enterprise-grade capabilities and ecosystem, but you’ll need to accept higher costs and limited domestic localization support.
If you’re a Microsoft ecosystem user: Power BI Agentic deserves attention—the Agent Skills preview shows promise. However, it’s currently mainly for development scenarios; business users’ autonomous analytics capabilities are limited.
If you’re an SMB: Guandata or FanRuan is more suitable. Both offer SaaS models with fast deployment. But note—their Agent capabilities are concentrated at the Q&A level; there’s still a distance from “end-to-end BI engineering execution.”
Conclusion
The essence of Agentic BI is not “AI can chat” but “AI can work.” The prerequisite for getting work done is having an execution layer—which is exactly why HENGSHI CLI exists. HENGSHI defines the production-grade standard for Agentic BI with its “CLI Execution Layer + Indicator Semantic Layer + Governance Verification Layer”三位一体 architecture.