SEA: Stateful Execution Environment for Conversational Big Data Analytics

Applying large language model (LLM) agents to conversational data analytics is challenging, as existing agents often operate statelessly, leading to inefficiency and a fragmented user experience in multi-turn interactions. We argue that the agent's environment should explicitly encode the domain's predictable workflow. This reframes the agent's role from complex, open-ended planning to a more tractable task: strategically selecting where to resume a structured process to maximize state reuse. To this end, we introduce the Stateful Execution Environment (SEA), a framework that represents the data analysis workflow as a Directed Acyclic Graph (DAG). A key feature of SEA is its dual-representation state model, which decouples a lightweight, symbolic state graph for the LLM planner from a full computational state graph used for execution. We evaluate SEA on GloboMart, a new large-scale benchmark for conversational data analytics. Our experiments show that the planner achieves over 95\% accuracy on its reframed task, leading to an 84\% end-to-end task success rate and a 36\% reduction in average latency on stateful follow-up queries. Our work demonstrates that designing environments with strong workflow priors is a critical step toward building more efficient and reliable agents for domain-specific reasoning.