Open-o3-Video-v2, an extension of Open-o3-Video, formulates video reasoning as a unified agentic, evidence-grounded process: the model decides what information is missing, actively acquires extra observations, writes the supporting moments and object-level bounding boxes into its trajectory, then continues or answers. To support this behavior the authors built STGR-v2-CoT135K, containing 52K multi-turn agentic grounded reasoning trajectories, plus STGR-v2-RL-13K for reinforcement learning, and propose Clue-Seek GRPO (CS-GRPO), which tracks how tool calls change accumulated evidence, rewards clue coverage and step-wise gains, and penalizes excessive, repetitive or uninformative retrieval. On V-STaR, Open-o3-Video-v2-4B reaches 40.4% mAM and 56.3% mLGM, beating its Qwen3-VL-4B backbone by 8.5% and 12.6% respectively, with consistent gains on general video understanding, long-video reasoning and temporal grounding benchmarks; code and models are released.