How Strongly Should Task State Influence an LLM Agent?

This paper isolates how task state is coupled to an LLM agent while holding models, rules, and paired episodes fixed. It compares a raw transcript, an exact displayed checklist, state-derived per-turn directives, and an enforcement gate that rejects state-violating actions. Synthetic assigned-project episodes, a real-tool harness, and two external benchmarks test models across reasoning regimes and task sizes. Accurate displayed state remains unreliable; directives improve results according to model obedience; enforcement is robust when errors are state-decidable but inherits compiler and matcher mistakes. Reasoning narrows rung differences, and PM-Bench shows that enforcement can hurt when action depends on uncertain free-text cue recognition.