In long-horizon tool use, early-step defects passing per-call checks often propagate silently across dependency chains to cause irreversible late-stage failures—a propagation class accounting for 73.0% of diagnosed Vanilla FC failures. Because these defects are locally valid (stepwise legality) yet manifest differently across tool boundaries (semantic heterogeneity), they easily evade standard per-call guardrails and post-hoc reflection, necessitating proactive pre-execution risk anticipation. To this end, we formalize the pre-execution risk propagation problem and propose RiskGuard, a training-free, dev-calibrated runtime controller. To capture the multi-source, conjunctive dependencies inherent in tool composition, RiskGuard models execution traces via a typed dependency hypergraph. Meanwhile, a Referential/Constraint/Operational (R/C/O) kernel tracks risk transitions across semantic boundaries. Replacing rigid binary blocking, a five-mode intervention rule selects the lowest-commitment feasible response to mitigate risks smoothly. Evaluated on ToolSandbox-hard, pooled τ²-Bench, and ToolHop, RiskGuard achieves absolute gains of +0.133 Sim (0.281 → 0.414), +9.2 pp pass@1, and +4.85 pp accuracy over the baseline, with depth diagnostics showing stronger gains on harder dependency slices.

Existing large language model (LLM) agents for automating data science show promise, but they remain constrained by narrow task scopes, limited generalization across tasks and models, and over-reliance on state-of-the-art (SOTA) LLMs. We introduce DatawiseAgent, a notebook-centric LLM agent framework for adaptive and robust data science automation. Inspired by how human data scientists work in computational notebooks, DatawiseAgent introduces a unified interaction representation and a multi-stage architecture based on finite-state transducers (FSTs). This design enables flexible long-horizon planning, progressive solution development, and robust recovery from execution failures. Extensive experiments across diverse data science scenarios and models show that DatawiseAgent consistently achieves SOTA performance by surpassing strong baselines such as AutoGen and TaskWeaver, demonstrating superior effectiveness and adaptability. Further evaluations reveal graceful performance degradation under weaker or smaller models, underscoring the robustness and scalability.