Building Reliable AI Analysts: Observability Framework for Text-to-SQL Systems (bagofwords.com)

A practical observability framework for text-to-SQL systems — developed while building the open-source AI analyst Bow (Bag of Words) — was announced to help teams move from demo to dependable production. The post catalogs where accuracy actually fails even with agentic infrastructure (ReAct loops, retrieval, uncertainty handling): ambiguous business metrics, schema traps that lead to wrong join/grain, runtime/code errors (syntax, permissions, timeouts), and degraded user satisfaction. The core diagnosis is prompt–context misalignment: when the model lacks the precise instructions, it guesses plausibly and silently produces wrong answers. The framework focuses on four compact, actionable signals instrumented at the agent-run level: Answer Quality (LLM judges + rotating human review), Context Effectiveness (semantic-match and clarification patterns), Code Errors (execution failures tied to tables/columns), and User Feedback (flags, rejections, iteration). Teams store full traces (retrieved context, agent actions, SQL, results, user reaction) and convert recurring failures into scoped runtime instructions attached to domains/tables/metrics. The recommended workflow — diagnose root cause, draft/test an instruction, review/approve, rollout and track — lets fixes act like evolving unit tests. An optional self-learning mode auto-drafts and validates instructions for human approval, shortening feedback loops without changing schemas or retraining models, and complementing traditional data modeling.