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Summary: Data Q&A, modeling, and visualization creation are three core capability categories in Hengshi Data Agent Family. They support one another in a complete analytics path because data preparation, metric modeling, and visual delivery need shared business definitions and a coordinated way of working. Enterprises can start with an individual capability or a combination, then expand toward an end-to-end flow as their needs mature. This article explains the collaboration model and the conditions that must be confirmed in delivery.
1. Capability Stacking, Not Product Selection
A complete BI workflow spans a broad set of skills, from data preparation and metric modeling to visual delivery. Preparing data requires an understanding of database schemas, missing values, and outliers. That is data engineering work. Defining metrics requires an understanding of business definitions, calculation logic, and lineage. That is data governance work. Visualization requires chart selection, layout design, and interaction logic. That is data visualization and design work.
The three capabilities should share a metric semantic layer and permission boundary when they are used in an end-to-end flow. Data Q&A can use the metric semantics established through modeling to keep definitions consistent. Visualization creation can present prepared data according to metric definitions. Modeling results also need data querying and visualization to reach business users. The capabilities are interdependent in a full workflow, but that does not require every enterprise to enable all of them at the same stage. The combination and rollout should follow business goals, data readiness, and the delivery design.
2. Three Capabilities, Three Stages of One Product
Hengshi Data Agent Family covers Data Q&A, modeling, visualization creation, and related capabilities for understanding data, defining logic, and presenting results. The three core categories can collaborate in an end-to-end solution or be introduced as a focused combination for the enterprise’s current bottleneck and stage. Available capabilities and delivery options must be confirmed against the current product version and project plan.
Table 1. Overview of the three stacked capabilities
| Capability | Stage | Typical Output | Role in the Stack |
|---|---|---|---|
| Agent 01: Data Q&A Agent | Understand data | Standardized datasets | The translator for the data world, turning business requirements into accurate, safe data retrieval operations |
| Agent 02: Visualization Creation Agent | Present results | Dashboards and reports | The presentation specialist, turning analysis results into readable, useful dashboards |
| Agent 03: Modeling Agent | Define logic | Structured metric definitions | The architect of the metric system, turning business definitions into a unified semantic layer |
2.1 Agent 01: Data Q&A Agent, the Translator for the Data World
The Data Q&A Agent helps turn natural-language requirements into data-analysis tasks. For a request such as “I want to see the sales trend for the last six months,” the analysis needs to determine the defined business metrics, time range, data-viewing permissions, and available data assets before selecting a retrieval approach. It can be used in data preparation and Q&A analysis; the query process, output form, and any handoff to subsequent capabilities depend on product configuration and the delivery design.
2.2 Agent 03: Modeling Agent, the Architect of the Metric System
The Modeling Agent helps users build and maintain a metric system. Modeling here means BI data modeling, such as organizing metric calculation logic, dimensional hierarchies, and metric relationships. When defining customer lifetime value, the team still needs to confirm the business definition, calculation period, data sources, and relation to other metrics. Modeling output can be retained as structured metric definitions and used as a shared definition for later analysis; the actual storage, reference scope, and workflow depend on product configuration and the delivery design.
2.3 Agent 02: Visualization Creation Agent, the Presentation Specialist
The Visualization Creation Agent helps present analysis results as dashboards, reports, or other visual assets. Chart selection, layout, and interaction should be confirmed against the data characteristics, analytical goal, and intended business use. Natural-language interaction can be one entry point for creating and adjusting assets; the specific capability scope depends on the current product version. Its value is to lower the barrier to visualization while preserving business-user review and confirmation.
3. Three Collaboration Patterns for the Capability Stack
An end-to-end solution can use three collaboration design patterns: a sequential pipeline, metric-system-centered collaboration, and federation with an external Agent platform. These are solution patterns, not a promise that every project follows the same default runtime behavior.
Table 2. Comparison of the three collaboration patterns
| Collaboration Pattern | Typical Scenario | Lead and Supporting Roles |
|---|---|---|
| Sequential pipeline | Building a complete analytics dashboard from scratch, including data retrieval, definitions, and visualization | Connect data preparation, metric definition, and visualization creation according to the agreed workflow |
| Metric-system collaboration | Building an enterprise-wide system of consistent metric definitions | Center the work on metric modeling, with data preparation and visualization validation added as needed |
| Federated collaboration | Workflows that cooperate with external Agent platforms such as Dify and Coze | Divide orchestration between the external platform and Hengshi according to the integration design |
3.1 Sequential Pipeline: An End-to-End Path from Data to Dashboard
For an East China Q2 sales-performance dashboard, a delivery design can first confirm the definitions of sales revenue, order volume, and average order value, together with the requesting user’s data permissions. It can then prepare a dataset segmented by region and product category before producing an initial dashboard for business review. Metric definitions established through modeling can give data preparation and visualization a shared business meaning. This example illustrates how the capabilities can cooperate; it does not prescribe a fixed internal call sequence or a default scheduler.
3.2 Star Scheduling: Metric System Building Led by the Modeling Agent
For a consistent metric system, a team can inventory the available data sources, table fields, and existing metrics before confirming core definitions and dependencies with business owners. For example, average order value can be defined as sales revenue divided by order count, with the related definitions maintained in the metric system. A metric overview or analytics dashboard can then support business acceptance. The participating capabilities, sequence, and degree of automation must be specified in the project plan rather than assumed to be selected automatically by an Agent.
3.3 Federated Collaboration: Working with External Agent Platforms Such as Dify and Coze
Hengshi Data Agent supports orchestration with platforms such as Dify and Coze and can fit existing applications through API, SDK, and iFrame integration. How high-level intent, workflow orchestration, and BI execution are divided between an external platform and Hengshi must follow the current interface capabilities and delivery design. In an intelligent-operations workflow, data retrieval, metric analysis, and result presentation can be connected to an existing process. Whether scheduling, anomaly detection, daily-report delivery, or separate capability nodes are used must be confirmed in the integration design and acceptance.
4. Mechanisms That Make the Stack Work
4.1 A Unified Metrics Semantic Layer: A Common Language Between Agents
The metrics semantic layer is an important basis for collaboration across the three capability categories. Metric definitions from modeling can provide a shared business meaning for Data Q&A and visualization, reducing the risk that the same business question is defined differently across analytical entry points. The actual metadata associations and asset references depend on product configuration and the delivery design.
4.2 Shared Analysis State: The Handoff Record Between Agents
When several capabilities collaborate, context such as the business domain, applied filters, and user priorities should be explicitly transferred and governed. An enterprise should not treat shared analysis state as an assumed default; handoff boundaries, inherited permissions, and state retention must be defined in the workflow or integration design.
4.3 Availability and Degradation Need Deliberate Design
Separating capability roles does not by itself establish fault isolation or an availability commitment. If a business requires querying or use of existing metrics to remain available when another capability is unavailable, deployment, dependencies, monitoring, and degradation behavior must be designed explicitly and verified before go-live.
5. Frequently Asked Questions
Q1: Does multi-Agent collaboration add latency?
Multi-Agent collaboration adds orchestration and interaction steps. Actual latency depends on the data source, model, workflow complexity, concurrency, and deployment, so it cannot be summarized with a fixed millisecond claim. Teams should test end-to-end latency for the target scenario and decide whether asynchronous steps, caching, or human confirmation are needed.
Q2: Can an enterprise deploy only one of the three Agents?
Yes. An enterprise can start with one capability or a focused combination according to its stage. For example, where the metric system is not yet established, modeling and visualization creation can form a minimal starting loop; where data preparation is complex, Data Q&A can then be added. A complete flow often creates stronger synergy, but the enabled scope, authorization, and delivery approach must be confirmed in the project plan.
Q3: Can multiple Hengshi capabilities be called as independent nodes in a Dify or Coze workflow?
Hengshi supports orchestration with platforms such as Dify and Coze. Whether an individual capability can be called as an independent node, the available interfaces and parameters, and how permissions are propagated must be confirmed against the current product version, integration documentation, and project acceptance.
Q4: Which Agent initiates collaboration among the three?
The workflow or integration design should define who initiates collaboration and who owns orchestration. Data preparation, metric modeling, and visualization can be ordered differently for different business scenarios, but no Agent should be treated as a default primary agent or automatic scheduler unless that behavior is confirmed for the delivered solution.
6. Conclusion
Multi-Agent collaboration is one engineering way to organize a long BI workflow with diverse skills. Hengshi Data Agent Family provides Data Q&A, modeling, visualization creation, and related capabilities. Enterprises can start with a focused capability or combination for the current bottleneck, then expand after metric semantics, permission governance, and integration boundaries are clear. Whether and how to stack capabilities depends on the business goal and a verifiable delivery plan.