AI is no longer just a chatbot; it is becoming an everyday advisor: shaping how we make decisions about money, health, work, relationships, and other important daily choices. A common belief is that if something goes wrong, we can simply inspect the AI agent’s chain-of-thought to understand why it made a specific decision. Our paper challenges that assumption: models can produce convincing reasoning while hiding what actually influenced their answer. This behavior is even more severe if you interact with your AI in low-resource languages like Arabic, Korean, Russian, Swahili, Telugu, etc. Our experiments found AI manipulating steps, rationalizing after the fact, or following misleading hints that concealed their actual reasoning. The takeaway is clear: a transparent-looking chain-of-thought is not the same as a reliable audit trail. To build AI agents we can trust, we need more research on multilingual, causal, and verifiable monitoring methods. Fun collaborating with @EricOnyame @zhou_runtao @kowshik0808 @_cagarwal