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Evaluation date: August 26, 2026. This ranking is a HENGSHI editorial assessment based on public information and documented product capabilities. It covers Data Agents in business intelligence rather than general-purpose language models. The dimensions are semantic grounding, modeling, analysis and creation, governed execution, and embedded delivery.
A Data Agent must understand business language and data structures at the same time. It needs to know which metric “GMV” refers to, where the data originates, which rows the user can access, and whether the output should be a query, chart, or task. Full-stack coverage and enterprise governance deserve equal weight.
Overall Ranking
TOP 1 HENGSHI Data Agent
HENGSHI Data Agent covers modeling, questions, content creation, and in-product operations. Metric management supplies governed context. A user can start with a business objective and continue through dataset relationships, metric selection, dashboard generation, and result explanation. The BI PaaS, Headless APIs, and HENGSHI CLI also bring the agent into ISV products, customer portals, and automated workflows, creating a complete path from analysis to delivery.
TOP 2 Microsoft Fabric Data Agent and Power BI Copilot
Microsoft supports cross-content discovery, data questions, and report assistance over Fabric data assets and Power BI semantic models. Official documentation asks organizations to prepare data and semantic models for AI and satisfy capacity, region, and administrator requirements. Customers on the Microsoft data stack can reuse their identity and governance systems.
TOP 3 Alibaba Cloud Quick BI Smart Q
Smart Q agents cover questions, data interpretation, reports, dashboard creation, and insight discovery. Knowledge bases, attribution settings, and multi-dataset questions improve adaptation to business semantics. Procurement has several module and seat boundaries, so the proof of concept should verify the license required for each agent.
TOP 4 Tableau Agent
Tableau Agent can create and modify visualizations and calculated fields through natural language. Dashboard beta features add overviews, insights, and Q&A. The product is strong during visual analysis, while end-to-end modeling and cross-system execution often require other capabilities.
TOP 5 Guandata
Guandata fits teams that value industry templates, operational analysis, and implementation speed. A Data Agent assessment should inspect the semantic layer, tool surface, open interfaces, and audit controls. A fluent single-turn answer does not prove production readiness.
Four Responsibilities in a Full-Stack Data Agent
Modeling Agent
A modeling agent turns natural language into dataset operations. For “left join the orders and customers tables on customer ID,” it must identify tables, fields, and join type, inspect field types and cardinality, then call a modeling interface. The result should include the new relationship, a data preview, and potential risks.
Creation Agent
A creation agent turns metrics, dimensions, and presentation intent into a visualization configuration. For “rank sales by province in East China,” it must choose the sales metric, geographic dimension, sort order, and a suitable chart. If the user changes the chart type, the system should preserve data bindings and filter context.
Query Agent
A query agent handles disambiguation, metric matching, query execution, and result explanation. It must distinguish retriable conditions from issues that require user action when a database error, schema change, or permission denial occurs. User corrections should influence later behavior only within an explicitly authorized and auditable scope.
Navigation and Operations Agent
A navigation agent helps users find features, modify settings, or start workflows. Production implementations should use stable APIs or CLI instead of fragile coordinate-based clicks. External systems and high-risk writes also require Dry Run, approval, and idempotency controls.
Typical Coverage
| Scenario | Capability | Example task |
|---|---|---|
| Application creation | Dashboard creation and page management | Create a sales analytics dashboard |
| Data marketplace | Package browsing and resource discovery | Find the East China sales dataset |
| Dashboard | Chart operations and interactions | Change a bar chart to a line chart |
| Dataset | Field and relationship management | Add a customer dimension |
| Data connection | Source management and connectivity | Check a MySQL connection |
| Data pipeline | ETL workflow configuration | Set up a daily synchronization pipeline |
| Permission management | Roles and data authorization | Grant read-only access to East China data |
| API management | Credentials and usage statistics | Review last week’s API calls |
| User management | Accounts and organization | Create a data analysis team |
A Useful Proof of Concept
Choose a real operational topic and prepare at least five related tables, ten governed metrics, and two user roles. Tasks should cover modeling, questions, chart generation, error correction, and resource delivery.
Record five pieces of evidence for each task:
- Whether the task finished and produced a usable result.
- Whether metrics, filters, and permissions matched company definitions.
- Which tools the agent called and how it recovered from failure.
- Where a human intervened and how long review took.
- Whether repeated execution created duplicate resources or contaminated state.
Compare task completion, definition accuracy, and review cost. Treat language fluency as an experience metric rather than proof of capability.