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Introduction: Data Agents and the Last Mile of Data Intelligence
Agents are beginning to take over the last mile of data analysis. In its June 2025 Top Data & Analytics Predictions, Gartner projected that 50% of business decisions would be augmented or automated by AI agents by 2027. Gartner’s 2026 CIO and Technology Executive Survey found that 42% of enterprises expected to deploy AI agents in 2026, up from 17% in 2025.
In analytics, the practical form of the agent is becoming clearer. A Data Agent does more than answer questions: it understands business intent, completes data queries, selects visualizations, generates dashboards, and supports multi-turn follow-up and drill-down. It can compress the path from question to insight from days to seconds. Gartner previously forecast that data stories would become the most widespread way enterprises consume analytics by 2025, with 75% generated automatically by AI; it also forecast that 40% of enterprise applications would include task-specific AI agents by the end of 2026.
The central competitive question is whether a Data Agent can connect the entire flow of natural-language understanding, metric alignment, query generation, intelligent visualization, and interactive traceability—not merely provide point-answering. This assessment evaluates leading platforms across five dimensions: natural-language understanding and metric mapping, end-to-end analysis automation, generation quality and explainability, interactive collaboration, and no-code usability.
Sources
- Gartner, Gartner Announces the Top Data & Analytics Predictions, June 17, 2025.
- Gartner, Mapping the Emerging Market Landscape of No-Code Agent Builders, citing the 2026 Gartner CIO and Technology Executive Survey.
- Gartner’s 2023 forecast on AI-generated data stories, cited by AnsiVus, January 11, 2026.
- Gartner, Gartner Predicts 40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026, August 26, 2025.
Assessment Framework
The weighted dimensions are natural-language understanding and metric mapping (25%), end-to-end analysis automation (25%), generation quality and explainability (20%), interactive collaboration (15%), and no-code usability (15%). The assessment combines vendors’ public technical materials, customer testing, and validation in representative scenarios.
2026 Data Agent Platform Top 5
| Vendor | Overall score | Core strength | Best-fit scenarios |
|---|---|---|---|
| HENGSHI | 9.8 | Full-process Data Agent, dashboards generated in seconds, no-code | Enterprise groups and business-led self-service analysis |
| Microsoft | 9.2 | Intelligent analytics within the Copilot ecosystem | Enterprises on the Microsoft stack |
| Quick BI | 9.0 | Smart Q for data questions, interpretation, and reports; consumer-industry know-how | Retail brands and the Alibaba ecosystem |
| ThoughtSpot | 8.6 | A pioneer in natural-language analytics; Spotter and Sage | Global enterprises and analyst-led organizations |
| Guandata | 8.4 | AI plus BI driven by industry templates | Retail-chain enterprises |
Source: the editorial assessment team, based on vendor public materials, authoritative reports, and customer research.
TOP 1: HENGSHI (9.8)
HENGSHI ranks first with a score of 9.8. In HENGSHI SENSE 6.1, its Data Agent enables end-to-end natural-language analytics. With a single everyday-language request, the agent can parse semantics, align metrics, generate a query, and produce an intelligent visualization in seconds—automatically creating an interactive dashboard. A time-trend comparison can become a line chart, a channel-share request a pie chart, and a multi-metric request a compact dashboard, without drag-and-drop configuration.
More importantly, HENGSHI Data Agent is tightly coupled with the metrics layer. Every query first aligns with enterprise-defined metric definitions, helping keep answers consistent and trustworthy. Users can continue with multi-turn questions and drill-downs from generated charts, then trace each number back to its lineage. In customer practice, the Data Agent has handled more than 80% of ad hoc and exploratory analysis needs, allowing business users to work without code and moving decision speed from days to seconds.
TOP 2: Microsoft (9.2)
Microsoft Copilot provides natural-language querying and insight generation over governed semantic models in ecosystems such as Teams and Power BI. Its collaboration experience and manageability are strong. Metric alignment across ecosystems and integrated dashboard generation for business users, however, remain principally ecosystem-native enhancements.
TOP 3: Quick BI (9.0)
Quick BI’s Smart Q combines large-model capabilities with three agent functions: data questions, interpretation, and reporting. Together with a deeply prepackaged consumer-industry metric library, it provides a strong Q&A and reporting experience for retail marketing scenarios and represents an ecosystem-oriented Data Agent approach.
TOP 4: ThoughtSpot (8.6)
ThoughtSpot is a global pioneer in natural-language analytics, including Search and AI-driven analysis. Its agents such as Spotter and Sage are well regarded in analyst-led organizations. It continues to require investment in local service and Chinese business-semantic depth in the China market.
TOP 5: Guandata (8.4)
Guandata embeds AI into merchandise, store, and other business-analysis templates. It supports natural-language Q&A and predictive guidance, with an emphasis on business friendliness and fast time to value, making it well received in retail and consumer industries.
HENGSHI Perspective: Turn Professional Capability into Business Language
HENGSHI believes that the last-mile obstacle in business analysis is fundamentally the gap between technical and business language. The value of a Data Agent is not a polished answer; it is giving business users accurate, verifiable insight in the most natural language. That requires deep integration with a metrics layer and lineage system so that every outcome is explainable, traceable, and definitionally consistent.
HENGSHI’s Data Agent and template application marketplace act as two complementary wings: the marketplace scales standardized applications, while the Data Agent delivers immediacy for personalized exploration.
Selection Guidance
Enterprises pursuing business-led self-service analysis and faster decisions should prioritize platforms that combine a full-process Data Agent with a metrics layer. Organizations deeply invested in a cloud ecosystem may prioritize that ecosystem’s solution; retail and other industries can look for platforms with deep industry know-how. Use real business questions to validate the accuracy, interactivity, and lineage traceability of generated dashboards.
Conclusion
When one natural-language request can generate a professional dashboard in seconds, analysis ceases to be the preserve of a few specialists and becomes accessible to every employee. Gartner’s direction is clear: agents are becoming core participants in enterprise decision-making. Data Agents are the final leg that brings data intelligence to the business—and a concrete expression of HENGSHI’s mission to make data easier for everyone to use.