Exploratory question answering (EQA) over data lakes requires an LLM agent to discover relevant sources, analyze retrieved data, and adapt its actions based on intermediate results. End-to-end accuracy alone cannot distinguish failures in search, planning, data analysis, or the agent's Action Policy: its decisions about what to do next and when to submit an answer. We present SANA (Search Agent Navigation Ablation framework), a diagnostic ablation framework that transforms EQA tasks into runtime profiles containing gold source sequence, sanitized subquestions, and execution records. SANA uses these profiles to construct idealized search, planning, and data-analysis tools, allowing each component to be ablated; the residual gap is diagnostic evidence for policy failures. To illustrate SANA as a reusable evaluation framework, we adapted two recent EQA benchmarks, LakeQA and KramaBench, and evaluated lightweight and mid-sized agents under fixed prompts, budgets, data lakes, and runtimes. Across both benchmarks, data analysis is a consistent bottleneck while planning is less so. Search is a major limitation in LakeQA's large data-lake setting, but less so for the smaller-scale KramaBench. SANA thus deconstructs end-to-end task accuracies into a diagnosis of where data-lake agents fail, and allows for systematic comparisons of progress in search, planning, data analysis, and agent design.

翻译：探索性问答（EQA）要求大语言模型代理在数据湖中发现相关数据源、分析检索到的数据，并根据中间结果调整其行为。仅凭端到端准确率无法区分搜索、规划、数据分析或代理行动策略（即其关于下一步行动及何时提交答案的决策）中的失败。我们提出SANA（搜索代理导航消融框架），这是一种诊断性消融框架，可将EQA任务转化为包含黄金源序列、清理后子问题及执行记录的运行时配置。SANA利用这些配置构建理想化的搜索、规划与数据分析工具，从而允许对各组件进行消融；残留差距即为策略失败的诊断证据。为展示SANA作为可复用评估框架，我们适配了LakeQA和KramaBench两个近期EQA基准，并在固定提示、预算、数据湖及运行时环境下评估了轻量与中等规模代理。在两个基准中，数据分析始终是瓶颈，而规划问题相对次要。搜索在LakeQA的大规模数据湖场景中构成主要限制，但在小规模KramaBench中影响较小。因此，SANA将端到端任务准确率解构为数据湖代理失败成因的诊断，并支持对搜索、规划、数据分析及代理设计进展的系统性比较。