Deep reinforcement learning agents have demonstrated impressive capabilities in managing complex systems and networking tasks, such as adaptive video streaming and congestion control. However, to ensure safe deployment, it is essential to analyze the symbolic properties of these agents to understand their behavior across various system states. Researchers have been exploring verification-based methods to reason about agent behavior, but these methods have limitations. A new approach focuses on analyzing symbolic properties to provide insights into agent decision-making processes1. This is particularly crucial in systems and networking, where agents may encounter a wide range of states, and their behavior can have significant consequences. As state-aligned activity involving reinforcement learning becomes more prevalent, the threat model shifts from criminal to geopolitical, requiring a different approach to security. This development matters to practitioners because it highlights the need for a more nuanced understanding of reinforcement learning agents' behavior to mitigate potential risks.