Data-analytic agents must iteratively generate and execute code while interpreting real-time feedback to refine their analytical strategies. Existing training paradigms decouple the agent’s policy from the underlying environment dynamics, producing agents that learn to recover from errors rather than to avoid them: failure rates rise alongside recovery rates, and interaction turns inflate as agents resort to trial-and-error exploration. We introduce DataSight, a framework that internalizes environment dynamics by making the model’s anticipation of execution outcomes an explicit, structured output accompanying every generated action. DataSight first enriches expert trajectories with foresight prediction fields grounded in real execution feedback, then applies a dual-stage RL curriculum that calibrates prediction fidelity before optimizing long-horizon task performance, and at inference time intercepts anticipated failures before physical execution. Trained on only 7,273 trajectories, DataSight-4B surpasses both DataMind-7B and DeepAnalyse-8B in average accuracy, while DataSight-8B achieves 64.13% Avg@3 across four benchmarks with Pass@3 of 74.81%, approaching DeepSeek-V3.2 at 75.56%. Analysis confirms that foresight training generalizes across non-reasoning backbones and SQL-based tasks, improving both single-step code quality and multi-turn interaction efficiency.

Large language models (LLMs) have demonstrated remarkable capabilities in tool learning. In real-world scenarios, user queries are often ambiguous and incomplete, requiring effective clarification. However, existing interactive clarification approaches face two critical limitations: reliance on manually constructed datasets, which inherently constrains training data scale and diversity, and lack of error correction mechanisms during multi-turn clarification, leading to error accumulation that compromises both accuracy and efficiency. We present AskToAct, which addresses these challenges by exploiting the structural mapping between queries and their tool invocation solutions. Our key insight is that tool parameters naturally represent explicit user intents. By systematically removing key parameters from queries while retaining them as ground truth, we enable automated construction of high-quality training data. We further enhance model robustness through error-correction pairs and selective masking, enabling dynamic error detection during clarification interactions. Comprehensive experiments demonstrate that AskToAct significantly outperforms existing approaches, achieving above 57% accuracy in recovering critical unspecified intents and enhancing clarification efficiency by an average of 10.46% while maintaining high accuracy in tool invocation. Our framework exhibits robust performance across different model architectures and successfully generalizes to entirely unseen APIs without additional training, achieving performance comparable to GPT-4o with substantially fewer computational resources.