Clear the Bottleneck: Learning When Language Agents Are Ready to Act

Long-horizon language agents often fail by acting before task-enabling prerequisites have been resolved: answering before evidence is sufficient, executing before the relevant state is known, or committing before required verification is complete. We study these failures through readiness bottlenecks, route-dependent prerequisites whose completion can substantially change the value of the remaining trajectory. The most faithful signal requires actually executing each candidate completion and rolling out the resulting context, which is too expensive for dense training. We therefore introduce a readiness certificate: a compact, grounded semantic representation of a prerequisite's completed state. The certificate acts as a surrogate for the full post-completion context, and Readiness-to-Action (RTA) measures the teacher's induced high-level action response to this surrogate. Its validity is tested against real execution rather than assumed: certificate-induced action shifts align with real-completion shifts (cosine 0.78), and RTA sensitivity correlates with downstream Prerequisite Completion Gain (Spearman $\rho =0.66$ across 2,643 audited state–condition pairs). Across the six prerequisite families, real completion yields a +14.8-point macro-average gain in normalized continuation return. We aggregate calibrated RTA signals into a readiness-aware action advantage and distill the resulting KL-regularized policy update alongside outcome RL. The resulting Bottleneck Distillation improves average task success from 42.6% to 46.1% over the strongest non-ours baseline while reducing premature commitment from 9.8% to 5.9% across procedural, stateful, evidence-intensive, compliance, and scientific agent tasks.