A new video question answering benchmark, AgentVidBench, is introduced to evaluate the spatial, temporal, and causal reasoning capabilities of MLLM agents. The benchmark provides step-by-step solution traces to assess whether agents acquire evidence to justify their answers. Experiments with 12 MLLMs show that integrating these models into agentic workflows improves performance on AgentVidBench.