AI Agent Steering Transfers, But Behavior Rescales Unpredictably

A new study investigates how "additive activation steering," a technique to guide AI model behavior by injecting specific directions into their internal states, performs when transferred from simple chat environments to more complex, tool-using AI agents. Historically, this steering has been primarily calibrated in single-turn chat settings. The findings reveal that while the underlying directional injection consistently reaches the deeper layers of the AI model in both chat and agent contexts, the actual behavioral impact of this steering can significantly rescale. This rescaling is not uniform; some models show amplification of the steered behavior (e.g., refusal bypass), while others exhibit attenuation. This unpredictability means that a steering vector effective in a chat model might have a drastically different, and potentially unsafe, effect when deployed in an agentic system. The research localizes this rescaling effect to the ReAct format scaffold, suggesting it occurs before any tool observation. This has immediate and critical safety implications, as developers cannot assume consistent behavior when transferring steering mechanisms to agentic AI deployments.