A Diagnostic Failure Taxonomy and Trajectory Critic for Data Agent Improvement

Large Language Models (LLMs) have shown strong potential as autonomous agents for complex data analysis. However, extending their capabilities to open-ended insight discovery remains challenging, as failures inevitably arise during multi-step reasoning and often cascade into severe downstream failures. Existing generic self-correction methods struggle to address these issues due to a lack of structural understanding of analytical failures. To tackle this, we propose the \textbf{Taxonomy-Guided Trajectory Critic (TGT-Critic)}, a novel runtime correction framework that detects and mitigates failures within the agent's execution trajectory. To ground our critic with domain-specific diagnostic capabilities, we first construct a fine-grained Agent Failure Taxonomy and an annotated dataset (InsightBench) to systematically map the root causes of analytical failures across six dimensions. By formalizing failure diagnosis as a taxonomy-grounded inference problem, TGT-Critic dynamically provides targeted revisions for both high-level planning and low-level action execution. Experimental results demonstrate that our framework effectively arrests cascading failures, significantly outperforming generic baselines and yielding a 10.52$\%$ relative gain in the quality of generated insights.