The Neophytic Rooms Green Agent administers that "Rooms" game benchmark, which is an original benchmark created for AgentBeats. The benchmark assess agents' abilities to navigate a system of rooms with limited and obfuscated information, resource management pressure, and high memory and planning requirements. Each configuration of the Rooms agent is a different system of 2-8 rooms. Rooms are connected together and may contain keys or be locked, but this is not visible to the agent until it INSPECTs a room. An agent starts in a room and must find its way to the exit over two phases. In each phase, the agent can choose from a small action space of MOVE, INSPECT, GETKEY, USEKEY, and COMMIT. MOVE moves the agent to an adjacent room to their current room, with some phase specific nuance. INSPECT allows the agent to inspect a room, learning adjacent room connections, and whether the room is locked, is the exit, or has a key. GETKEY allows the agent to pickup a key in a room, and USEKEY allows the agent to unlock a room by using a key. COMMIT changes the phase from Observation to Execution, and cannot be reversed. During the Observation phase, the agent is free to move around the room system, and every room they move into is automatically inspected. However, moving costs more during the Observation phase. Agents can move through locked rooms during Observation, but cannot leave using the exit, or GETKEY or USEKEY. After the Observation phase, the Agent may lose the state of observed rooms, and is reset to their starting room. During the Execution phase, agents have access to more actions and are now actively trying to find the exit and leave using knowledge gained in Observation. However, the number of actions (steps) agents have in Execution is limited. Using this system, the Rooms Green Agent tests agentic ability at logical reasoning with imperfect information, cost-benefit analysis, long-term memory, and failure recognition. There are several configurations of room-systems of various difficulty prebuilt into the Rooms agent, and additional configurations can be generated using an encoding schema, allowing for high scalability.