ARCHITECT: Uncertainty-Aware Dynamic Tool Learning via Causal Intervention for Open-World Agents

Dynamic tool generation empowers Large Language Model (LLM) agents to synthesize tools on demand, yet a critical challenge remains: 32.4% of generated tools fail on first invocation. We present Causal Tool Diagnosis (CTD), a principled framework that moves beyond blackbox reliability prediction to interpretable failure attribution. CTD constructs a Structural Causal Model (SCM) capturing how specification quality, code characteristics, and execution environment jointly determine tool outcomes. Uniquely leveraging code's intervenability, we conduct controlled sandbox experiments to estimate causal effects-an advantage unavailable in pure text generation. CTD jointly predicts confidence (Spearman rank correlation coefficient ρ=0.90) and root cause attribution (78% accuracy), with attributions directly guiding targeted repairs (+9.6% success rate over error-type classification). Our ARCHI-TECT framework, integrating CTD throughout the tool lifecycle, achieves state-of-the-art on four benchmarks including StableToolBench (+3.8%), MINT (+4.6%), T-Eval (+3.7%), and SWE-bench Lite (+2.4%), with consistent improvements across all settings.