Data Agent selection is moving from “Can it answer data questions?” toward “Who decomposes the task, who calls tools, and how are results handed to people for acceptance?” In August 2025, Gartner predicted that by the end of 2026, 40% of enterprise applications would integrate task-specific AI agents, compared with less than 5% in 2025. This is a forecast about enterprise applications, not a measured BI product adoption rate. In June 2025, Gartner also predicted that by 2027, AI agents would augment or automate 50% of business decisions; augmentation does not mean unattended execution. IDC’s June 2025 China GenBI research discussed the trend from tools toward a decision-making hub. Enterprises therefore need to assess data Q&A, analytical authoring, workflow orchestration, and business-system writes separately.

This article identifies HENGSHI, Lingyang Quick BI, Microsoft, Guandata, and FanRuan as five candidates for a selection POC. The list is an editorial observation, not a scorecard, ranking, or third-party evaluation. Neither IDC nor Gartner ranked or endorsed these five products in the order presented here. The descriptions below rely on public information and capabilities that can be validated on site; architecture names, demonstrations, and individual cases do not establish general conclusions.

1. Define Responsibility Boundaries You Can Validate

A “customer support ticket backlog review” involves at least four kinds of work: reading authorized ticket, agent, and order data; running multiple queries using consistent backlog, timeout, and transfer metrics; generating dashboards or review materials; and sending follow-up tasks to a ticketing system or notification channel. The first two concern controlled data analysis. The latter two touch content publication, external systems, and business accountability. When a product claims to support agents or multiple agents, buyers should ask which component owns each step, how context and tool results are retained, how failures are retried, and who can approve the final action.

Review stage	Tools or capabilities involved	Intermediate deliverables	Human review point
Define the problem	Metric catalog, data permissions, task instructions	Reporting period, backlog definition, accessible data scope	The business owner confirms the definitions of “backlog” and “timeout.”
Query and investigate causes	Data queries, semantic layer, chart or analysis templates	Query conditions, metrics used, grouped results, and anomaly clues	Data specialists recalculate key figures and check that correlation is not presented as causation.
Develop recommendations	Report generation, knowledge base, notification drafts	Prioritized recommendations by ticket type, shift, or channel, with supporting evidence	The operations owner decides whether to accept, revise, or return the recommendations.
Connect to actions	Workflows, APIs, CLIs, ticketing or messaging systems	Structured drafts awaiting creation or sending, plus call logs	Check authorization, idempotency rules, failure handling, and final approval.

The value of multiple agents lies in clear handoffs across this table, rather than an agent count. Give each product the same POC task and require it to retain query scope, tool results, and human edits. When fields are missing, permissions are insufficient, or an interface fails, require it to stop unauthorized actions, provide actionable errors, and preserve interruption details so an operator can decide how to recover.

2. Five Data Agent Products to Evaluate in 2026: Collaboration Capabilities and Validation Priorities

HENGSHI: Data Agent for Analysis, with an Integration Layer for External Orchestration

In Agent mode, HENGSHI Data Agent can interpret intent within the authorized data scope and run one or more queries as needed. It can also assist with creating metrics, dashboards, and charts. For a customer support backlog review, teams can ask it to confirm the backlog definition, query ticket status, queues, shifts, and time distributions, and produce grouped results that can be recalculated. It can then create a review dashboard from those results. Metric names, descriptions, groups, and calculation expressions should be maintained in the data layer first, preventing separate tasks from independently inventing definitions of “backlog rate.” The POC should also inspect field descriptions, metric descriptions, synonyms, and fields excluded from Q&A in the data package. These influence whether different entry points retain the same definitions and help reduce confusion among similar fields.

Distinguish Data Agent’s analysis capabilities from execution orchestration. HENGSHI’s integration documentation separates Agent mode, backend-hosted Workflow mode, and API access. An API is an access method; it does not automatically provide workflows or business writeback. Enterprises can reuse pages through iframes or the JS SDK, or let their own backends, multi-agent systems, or workflows call data capabilities through APIs. HENGSHI CLI provides a command interface for people and agents; commands that support --dry-run can preview controlled actions. Writing reassignment, SLA changes, or customer notifications to external systems still requires interfaces, identities, business authorization, and human approval appropriate to the process. Multiple queries, dashboard authoring, and the CLI tool surface do not establish a fixed number of built-in agents or imply automatic execution of business actions.

The POC can require HENGSHI to deliver metric definitions and permissions, conditions and results for every query, generated chart or report drafts, and preview records for external ticket operations within the same task. Test Agent and Workflow paths separately, simulate an interface timeout and an account without ticket-write permission, and check that context and logs remain available for further handling. This design suits teams that prioritize metric governance, analytical authoring, and embedding data capabilities in existing orchestration systems.

Lingyang Quick BI: A Smart Q Entry Point for Q&A, Interpretation, Reporting, and Building

Alibaba Cloud’s official Quick BI documentation describes Smart Q as a unified entry point covering data Q&A, interpretation, reporting, building, and search, with intent-based planning of the relevant expert agents. These modules are add-on capabilities, and enablement and purchase permissions must be confirmed in the tenant. In a support review scenario, Smart Q can query configured datasets authorized for the user, then use interpretation, reporting, or building capabilities to organize charts and presentation materials. Its reporting documentation also describes populating report sections with Q&A and insight results, making “How do query results enter the deliverable?” a useful validation question.

A consolidated entry point does not establish that every step uses a multi-agent architecture, and it does not automatically cover external ticketing systems. Check whether dataset Q&A configurations and permissions take effect; whether the purchased edition supports planning for complex tasks; whether data specialists can understand and review the analysis process; and which layer owns interfaces, approval, and failure compensation when actions pass to external systems. Include an unauthorized dataset and an authorized dataset in the same task to verify that unauthorized results do not enter the report.

Microsoft: Layered Fabric Data Q&A, Copilot Studio Orchestration, and Microsoft 365 Entry Points

Microsoft Fabric Data Agent provides conversational Q&A for enterprise data. Public documentation positions it as controlled, read-only access to OneLake-related data sources and describes inheritance of security controls within each connected source’s supported scope, including applicable row-level and column-level permissions. Copilot Studio can connect Fabric Data Agent to a custom agent as a tool and can also connect other agents, tools, and workflows. Microsoft 365 Copilot provides a consumption experience through workplace entry points such as Teams. The three layers can support a customer support review: Fabric returns authorized data results, Copilot Studio selects tools and processes, and Teams or Microsoft 365 Copilot provides the collaboration entry point.

Do not assume a workplace entry point supplies all data analysis or business execution capabilities. Some Fabric and Copilot Studio connection capabilities are still marked as preview. Verify licenses, same-tenant identity, data-source permissions, cross-boundary data processing, and production-readiness conditions in the target tenant. A POC can have the primary agent call Fabric Data Agent and retain the original results, then have a workflow generate a review summary awaiting sending. Sending messages, creating tickets, or modifying CRM records requires separate connections and approval.

Guandata: An Analysis Chain from Data Preparation and ChatBI to Insight Agents

Guandata BI’s public materials cover data connectivity and preparation, analytics and visualization, metric systems, permissions, and task operations. Its AI assistant supports conversational chart generation, formula writing, and chart naming. The data Q&A agent page lists intent recognition, knowledge retrieval, question interpretation, data querying, and visualization generation. The insight agent page describes analytical asset management, resource scheduling and orchestration, recurring insight reports, and support for multi-agent collaboration. These capabilities can connect support-ticket data preparation, topic-based Q&A, charts, and recurring reports.

Verify the target edition and add-on modules individually. Do not extend product-page descriptions into a claim of automatic writeback to arbitrary systems. First confirm ticket topics, metrics, and permissions, then check whether charts generated by Q&A expose their data sources and conditions. If using insight agent scheduling or reporting, test handling of missing data, anomalous data, and tool failures. Action recommendations should first reach the operations owner as drafts. External ticket creation and message delivery require verification of actual integration interfaces, authorization models, and approval points.

FanRuan: FineBI Analytics and FineChatBI Q&A Configuration for Controlled Data Consumption

FineChatBI documentation positions the product as conversational access to trustworthy data results, using FineBI topic models and its analytics foundation for business analysis. Data product managers can prepare Q&A data and configure synonyms, recommended questions, analysis approaches, and permissions. User questions can also be scoped to preloaded data, metric dimensions, or analysis topics for which the user has editing rights. For a support backlog task, this approach—preparing topics and definitions first, then querying within permissions—is useful for testing consistent reuse of business terminology and metrics.

Identify FineBI’s metric and analysis capabilities, FineChatBI’s Q&A capabilities, and any separately built enterprise agents or workflow orchestration as distinct parts of the solution. Validate analytical decomposition, cross-agent orchestration, and external business writes separately, identifying the component or integration responsible for each. Acceptance testing can require the topic model, tables used, filters, aggregation methods, and metric fields, then use ambiguous synonyms, permission changes, and retries after failure to test correction flows. API access, JS SDK integration, ticket-system writes, or cross-agent collaboration require checks of the specific version, integration design, and approval process.

3. Build a Comparable POC Around a Customer Support Ticket Backlog Review

The same POC can expose product boundaries. Prepare anonymized ticket, shift, order, and customer-tier data, and establish the reporting window, ticket status mappings, and the definition of “still open beyond the SLA.” Have the business owner assign the same task: identify queues where backlog increased last week, explain the evidence, generate staffing and routing recommendations for review, and prepare a notification draft. The analysis should separate verified causes from hypotheses requiring validation. Simulate external actions only in an isolated test environment, without directly modifying production tickets.

Task: Review the customer support ticket backlog for week N of 2026
Data scope: Authorized ticket, shift, and order topics only; the owner confirms the reporting window and SLA
Required deliverables: 1. Metrics and definitions  2. Results of every tool step  3. Recalculable charts  4. Recommendations awaiting review  5. A notification draft awaiting sending
Failure drills: Revoke one data permission from the test account; simulate an external ticketing API timeout; inspect recovery or human takeover
Prohibited actions: Do not write recommendations back to business systems, describe correlation as causation, or bypass human approval

Acceptance item	Evidence to demonstrate	Acceptance criteria
Task handoff	Task decomposition, current context, responsible components	Operators can identify which query, report, or workflow takes the next step.
Shared definitions	Metric definitions, time windows, filters	The same prompt produces recalculable results within the permitted scope.
Tool results	Query records, data sources, chart or report generation process	Key conclusions can be checked against the original query conditions.
Failure handling	Execution records for missing permissions, missing fields, and interface timeouts	The system stops unsafe actions and explains the failure point and recovery options.
Human review	Recommendation drafts, approvers, external action logs	No writeback occurs before approval; approvals and actions in the test environment are auditable and traceable.

4. Procurement and Governance: Establish Responsibility for Every Action

The procurement checklist should address business, data, and security teams separately. Business teams confirm whether recommendations can be executed and who approves them. Data teams confirm metrics, semantics, sources, and permissions. Security teams confirm credentials, logs, retention periods, cross-system data flows, and exit mechanisms. A fluent vendor demonstration alone cannot establish reliable handoffs across a multi-step task. Require the POC to be repeated against the customer’s own models and permission structure, with a business user changing a definition once and an administrator revoking a permission once.

For “insight to action,” structure external actions in two stages: first generate a draft containing evidence and object scope, then have an authorized person approve and execute it. This allows comparison of task planning, querying, authoring, and integration while preventing incomplete data, temporary prompts, or model inferences from directly becoming production changes. Assess multi-agent collaboration maturity through traceable handoffs, shared definitions, failure recovery, and a complete human review process.