We introduce TS-Bench Agent, a unified benchmarking framework for evaluating the capabilities of agentic systems in solving financial time-series modelling problems. The benchmark assesses whether a time-series agent can autonomously interpret task specifications, retrieve and process data, construct appropriate machine-learning models, and produce valid outputs with the objective of achieving strong performance. TS-Bench Agent focuses on two core classes of tasks: time-series forecasting and time-series generation. Forecasting tasks require agents to predict the future dynamics of financial time series, while generation tasks require agents to synthesize realistic time series that faithfully reproduce the statistical and temporal properties of historical data. To ensure comprehensive coverage, each task class comprises multiple tasks organised into three difficulty levels, ranging from short-horizon stock return and volatility prediction at the easiest level to more complex crypto-market dynamics and regime-switching financial processes at higher difficulty levels. TS-Bench Agent further incorporates a comprehensive and robust evaluation protocol. Forecasting performance is assessed using RMSE, MAE, and MAPE, while generation quality is evaluated using Histogram Loss, Autocorrelation Loss, and Cross-Correlation Loss. Metric values are normalised and aggregated to produce task-level scores, which are then combined across tasks using difficulty-based weighting to yield an overall score for each task class. By considering diverse tasks and difficulty levels, TS-Bench Agent delivers a more robust, comprehensive, and reliable assessment of agent capabilities than evaluations based on individual tasks. Beyond quantitative metrics, TS-Bench Agent provides structured task summaries, including detailed task descriptions, data access links, evaluation code, and explicit output format requirements. This design ensures that agents are evaluated under clearly specified and reproducible conditions. The overarching goal of TS-Bench Agent is to enable fair, transparent, and reproducible ranking of agentic solutions for financial time-series modelling. By standardising task definitions, evaluation metrics, and aggregation rules, TS-Bench Agent offers a consistent and reliable foundation for comparing agent workflow for financial time-series analysis.