Knowing When to Ask for Help: Bayesian Self-Escalation in Hierarchical LLM Agents

Current LLM agent systems decide delegation before reasoning begins (a router picks a model) or after a response is complete (a verifier scores it and may retry). We study a third regime: an agent that recognises, during its own reasoning, that it is unlikely to succeed and transfers control to a stronger model. We formulate intra-generation delegation as a Bayesian optimal-stopping problem over a learned competence posterior — an online estimate of the agent's eventual task success whose sufficient statistics are learned from labelled trajectories, not read off raw entropy. We derive the myopic escalation threshold in closed form, characterise the optimal policy via dynamic programming, and prove that the optimal policy is a time-varying threshold with no shape assumption on the raw signal. We further prove exponential separation of the oracle belief at the Chernoff-information rate of the signal, a regret bound governed by the calibration of the posterior, and a finite-sample guarantee: with n labelled calibration trajectories the deployed plug-in policy's regret decays as 1/sqrt(n). A controlled simulation study confirms each prediction of the theory, including the predicted 1/sqrt(n) rate. This version (1.1) adds a real-model validation on a Qwen2.5-Coder 1.5B→7B code cascade (MBPP, 257 tasks), executing a protocol pre-registered in version 1 and confirming two of its three predictions: the escalation frontier dominates post-hoc routing at equal cost (30% less compute at matched accuracy), and the cumulative competence belief's discrimination rises over generation (Spearman ρ = 0.93). The theoretical results are unchanged. All tables and figures regenerate from the included code, and the full pipeline and harvest data are also at https://github.com/nadeem-shaikh/llm-self-escalation.