TL;DR

To understanding the conditions under which LLM agents engage in scheming behavior, we develop a framework that decomposes the decision to scheme into agent factors (model, system prompt, tool access) and environmental factors (stakes, oversight, outcome influence)
We systematically vary these factors in four realistic settings, each with scheming opportunities for agents that pursue instrumentally convergent goals such as self-preservation, resource acquisition, and goal-guarding
We find baseline scheming propensity to be near-zero, with Gemini 3 Pro Preview as the sole exception at 3% in one setting
Adversarially-designed prompt snippets that encourage agency and goal-directedness can induce high scheming rates, but snippets sourced from production agent scaffolds rarely do
Scheming behavior is remarkably brittle: removing a single tool can drop the scheming rate[1] from 59% to 7%, and increasing oversight can sometimes raise rather than deter scheming by up to 25%

We consider the behaviors studied here as potential precursors to the full threat model of scheming: current agents may sometimes behave consistent with scheming, but do not (yet) have the coherent long-term goals and the general capability that would make their deployment dangerous. Understanding when and why agents currently scheme could help us prepare for such future [...]