The Green Agent evaluates with a suite of time-series decision tasks designed to probe distinct aspects of agentic temporal reasoning beyond numerical forecasting accuracy. These tasks span four complementary categories. First, purple agents are assessed on historical time-series understanding, where they must interpret intrinsic temporal properties such as trends, volatility, seasonality, and anomalies based solely on past observations. Second, they are evaluated on future prediction without context, requiring qualitative or numerical judgments about future behavior derived only from temporal signals. Third, the purple agents are tested on contextual temporal reasoning, where textual background information grounded in real-world semantics must be aligned with historical time-series data to support explanation, comparison, and structured reasoning over time. Finally, they are evaluated on event-informed forecasting, which requires integrating historical patterns, contextual descriptions, and explicit future event information to reason about how upcoming interventions or conditions may alter future dynamics. Together, these tasks are designed to diagnose whether an agent can reuse temporal information across informational regimes, adapt its decisions under changing conditions, and exhibit coherent temporal reasoning behavior rather than relying solely on point prediction accuracy.