The article analyzes how DataAgent can become practical by tackling correct data retrieval and business understanding, detailing a four‑layer architecture, mixed semantic pathways, metadata enrichment, and iterative agent collaboration to support enterprise credit scenarios.
It discusses Xiaomi DataAgent's third‑place ranking on the global BIRD Text‑to‑SQL benchmark, analyzes challenges such as model hallucination, lack of business knowledge, and complex multi‑table joins, and explains how a semantic harness addresses these problems to enable reliable enterprise data querying.
DataAgent, built on Spring AI Alibaba, tackles the "last mile" of AI data analysis by combining deterministic workflow orchestration with large‑model reasoning, offering human‑in‑the‑loop feedback, dynamic prompt configuration, hybrid retrieval, containerized Python execution, streaming SSE, multi‑model scheduling, multi‑source connectivity, and secure API‑key management to deliver instant, insight‑rich reports for business users.
