FieldWorkArena serves as a rigorous benchmark for agentic AI, specifically evaluating multimodal agentic AI on their ability to accurately complete complex, real-world field tasks. The benchmark's tasks are meticulously designed to simulate practical challenges in environments such as factories, warehouses and retails. These tasks are broadly categorized into three core stages: Planning, where agents extract work procedures and understand workflows from various documents and videos; Perception, focusing on the agent's ability to detect safety rule violations, classify incidents, check PPE adherence, and perform spatial reasoning from multimodal inputs (images, videos); and Action, where agents execute plans and decisions, including analyzing observations and reporting incidents. Additionally, Combination Tasks integrate these stages, requiring the agent to perform multi-step operations like detecting incidents from videos/documents and reporting them. Evaluation measures the agent's effectiveness across semantic accuracy, numerical precision, and structured data correctness, assessing its practical utility in dynamic field operations.