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    • How Data Agent Works

    Data Agent is an intelligent question-answering and analytics platform for enterprise data assets. It turns business language into executable data queries and relies on agent-style reasoning to finish complex tasks. Built on top of ChatBI, it upgrades knowledge modeling, reasoning memory, and system integration so teams can achieve more with less configuration.

    Product Positioning

    • Unified access to multiple data sources: Connect mainstream databases, lakehouses, and APIs, then project scattered data into a single semantic space.
    • Natural-language interaction: Allow business users to ask questions the way they talk to teammates. The system parses the intent, generates SQL automatically, and returns structured charts.
    • Extensible agent framework: Plug-and-play tools (SQL, API, scripts, etc.) enable multi-step reasoning workflows for complicated analysis.

    Core Capabilities

    1. Semantic modeling & context understanding: Build indicators, dimensions, and business-term mappings by subject area, and resolve intent with the help of conversation context.
    2. Intelligent execution chain: Identify intent, choose the right data source, generate optimized SQL/DSL, and stitch multiple steps when calculations, filtering, or aggregation are needed.
    3. Self-improving loop: Use feedback and logs to continuously refine prompts, model parameters, and semantic mappings, steadily improving hit rate and accuracy.
    4. Open integration: Expose APIs, web widgets, IM bots, and more so Data Agent can be embedded into DingTalk, Feishu, WeCom, or in-house portals.

    Typical Scenarios

    • Near-real-time executive dashboards and automated weekly/daily reports.
    • Ad-hoc queries, campaign tracking, and retrospectives for business operations teams.
    • Multidimensional drill-down, comparisons, and KPI monitoring for data analysts.
    • Instant lookup of customers, orders, inventory, etc. for frontline teams like support and sales.

    Architecture Modules

    ModuleDescription
    Access layerHandles authentication, authorization, and multi-end components (Web, IM, API).
    Semantic modeling layerDefines domains, models, metrics, dimensions, and co-occurrence relationships that provide context for natural-language parsing.
    Reasoning & execution layerIncludes LLMs, prompt templates, tool orchestration (SQL, API, scripts, etc.), and execution monitoring.
    Data connectivity layerSupplies adapters for databases/data lakes/third-party APIs and manages connection configs, caching, and rate limiting.
    Operations layerOffers logging, metrics, alerting, releases, and multi-environment configuration.

    After understanding these components, follow the steps in “Quick Start” to complete the first deployment and validation. Then move on to “Guide” to learn how to build robust semantic models and operations practices.