Large language models (LLMs) are increasingly used as agents capable of learning and adapting within a given context. However, a key challenge lies in controlling their exploratory behavior – how they gather information and make decisions in uncertain environments. Imagine an AI agent tasked with maximizing rewards in a game with two buttons, each offering random points. Current LLMs often get stuck, prematurely committing to one button even when it's not the optimal choice. They become overconfident, failing to explore other options. This research introduces a novel technique called Entropic Activation Steering (EAST) to address this overconfidence. Instead of simply adjusting the randomness of token generation (like changing the "temperature" of a language model), EAST directly influences the agent's decision-making process. It works by analyzing the agent's internal representations of uncertainty and then subtly guiding these representations towards more exploratory actions. The results are striking. AI agents using EAST demonstrate a greater ability to explore different options, avoiding premature commitment. Interestingly, EAST also influences the agent's 'thoughts', making them express more uncertainty and a willingness to experiment. This suggests that LLMs have an internal representation of uncertainty that can be directly manipulated. The implications are significant. EAST offers a powerful tool for controlling LLM agents, enabling them to navigate complex scenarios more effectively. This research opens doors to building more robust and adaptable AI agents capable of handling real-world uncertainties.