When LangChain Agents Collapse: Inference Failure and Bayesian Recovery

Angshul Majumdar

Abstract:

LangChain-style tool-augmented language model agents are typically engineered as deterministic pipelines with validation-and-retry loops. While effective in many settings, these designs often exhibit brittle behavior: once a system commits to an early tool choice or intermediate structured output, subsequent steps can inherit that commitment even when better alternatives exist.

This paper reframes LangChain agent execution as sequential inference over action trajectories under a finite-horizon generative model of actions and tool observations. Within this unified view, deterministic greedy agents, probabilistic forward-sampling agents, and Bayesian particle-based agents correspond to distinct inference regimes over the same underlying interaction contract. Retry-based execution can be interpreted as conditional sampling under binary acceptance, whereas Bayesian Sequential Monte Carlo maintains multiple competing hypotheses and updates their plausibility using likelihood signals derived from tool outputs (e.g., schema validity, reliability, or semantic consistency).

Our goal is conceptual rather than benchmark-driven: we provide a clean inference lens for understanding why collapse occurs in common agent pipelines, and a principled recovery mechanism that preserves alternative trajectories long enough for evidence to accumulate. The accompanying reference implementations instantiate this inference ladder within the LangChain setting while keeping tool interfaces unchanged.