Candidate Order in LLM-Generated Agent Environments: Prompted Placement, Measured Difficulty and Learned Reliance

Language models increasingly write the environments used to evaluate and train tool-using agents. Such environments are checked for solvability and difficulty, not for where the correct option sits among an agent's candidates. We study this in a record-chain lookup task in which each record lists one labelled active candidate and two decoys. In fresh generated worlds, Sonnet 5 listed the active candidate first in 85.0% of these choices and two GPT-5.6 generators in all of them; the format example in the generation prompt also listed it first. Three separate controlled studies examine this regularity. Moving the active object to second or third place in the format example reduced first-position placement for all three generators (Holm-adjusted p ≤ 3×10⁻⁵ each), almost completely for one and partially for the other two. On identical worlds, the generators' own order raised traversal completion by 6.4 to 15.2 percentage points for Qwen3-1.7B and by 3.4 to 12.4 points for Qwen3-4B, while re-permuting an already shuffled reference produced no detectable change; the 4B solver's final answers showed no corresponding advantage. In matched fine-tuning of Qwen3-1.7B, permuting the candidate lists inside otherwise identical training trajectories raised held-out traversal completion by 58.6 to 86.8 points across three seed pairs and final-answer success by +10.46 points on average; models trained on the generators' order mostly followed a decoy once the order changed. The studies share one task mechanism and one model family and do not test the full prompt-to-learner chain. Generated environments should randomise and report candidate order.