FieldWorkArena: Agentic AI Benchmark for Real Field Work Tasks
Elevating Agentic AI: Real-World Field Work Benchmarking
FieldWorkArena introduces the first standard benchmark for evaluating agentic AI in real-world field operations. Unlike traditional benchmarks focused on simulated or digital environments, this work addresses the fundamental challenge of assessing AI agents in authentic manufacturing, logistics, and retail settings. It leverages on-site captured images, videos, and work manuals to define tasks developed through meticulous interviews with site workers and managers. The benchmark also refines evaluation functions to accurately assess performance in diverse real-world tasks, identifying both the effectiveness and current limitations of agentic AI systems. The complete dataset and evaluation program are publicly accessible, fostering continuous research and development.
Executive Impact at a Glance
FieldWorkArena sets new standards for validating AI agents in mission-critical, real-world scenarios.
886
Real-World Tasks Evaluated
3
Field Scenarios (Factory, Warehouse, Retail)
3
Multimodal Input Types (Image, Video, Doc)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Benchmark Overview & Data
Task Design & Action Space
Evaluation Methodology
MLLM Performance & Limitations
Introducing FieldWorkArena: Bridging AI and Real-World Operations
FieldWorkArena is a groundbreaking benchmark suite designed to promote the application of field-monitoring oriented agents in real-world fieldwork environments. Addressing a critical gap, it focuses on evaluating agentic AI's performance in authentic manufacturing, logistics, and retail settings, moving beyond simulated or digital environments. This benchmark is crucial for tackling labor shortages and improving efficiency, safety, and productivity in these industries.
Comprehensive Data Collection from Diverse Real-World Sites
The dataset for FieldWorkArena comprises over 400 types of data, including images, videos, and work manuals, sourced directly from factories, warehouses, and retail stores. Data acquisition involved meticulous on-site capturing, with consent from all individuals and necessary blurring to ensure privacy. Specific scenarios include assembly processes and verification work in factories, receiving and shipping operations in warehouses, and staff/customer activities in convenience stores, providing a rich and realistic testbed for AI agents.
| Name | Developed by | Domain | Input | Tasks |
|---|---|---|---|---|
| WebArena | CMU | E-commerce, Reddt, GitLab, Content Management | Text | 812 |
| VisualWebArena | CMU | E-commerce, Reddt, Classifieds | Text, Image | 910 |
| WorkArena++ | ServiceNow | Web Operation | Text, Image | 682 |
| FieldWorkArena (Ours) | CMU & Fujitsu | Manufacturing, Logistics, Retail | Text, Image, Video | 866 |
| FieldWorkArena distinguishes itself by focusing on safety-critical perception and decision-making tasks grounded in real-world field data, incorporating video, images, and documents. It moves beyond UI navigation tasks in controlled digital environments to evaluate AI across the full spectrum of real field work challenges. | ||||
886
Total Tasks across 3 Scenarios
Task Generation Reflecting Real-World Needs
Tasks within FieldWorkArena are meticulously designed to align with actual workplace requirements. Through extensive interviews with site supervisors, consultants, and sales representatives, the benchmark incorporates 'safety and manufacturing-related near misses' for industrial settings and 'employee compliance and customer behavior analysis' for retail environments. This ensures the tasks are highly relevant and practical for on-site AI support.
| Task Type | Factory | Warehouse | Retail | Total |
|---|---|---|---|---|
| Perception | 130 | 215 | 366 | 711 |
| Decision making | 16 | 32 | 73 | 121 |
| Combination | 30 | 17 | 7 | 54 |
| Total | 176 | 264 | 446 | 886 |
| Tasks are categorized into 'Perception' (extracting information from multimodal inputs), 'Decision Making' (executing plans based on perceived situations), and 'Combination' (multi-step tasks requiring complex reasoning). Each task includes parameters for setting thresholds for proximity distances or violation counts, derived from on-site documentation or supervisor interviews. | ||||
Defining Coarse Action Space for Simplicity and Reproducibility
The initial implementation of FieldWorkArena defines a coarse action space to prioritize implementation simplicity and reproducibility. Agents interact with the environment through three unified actions: analyze_documents(), analyze_images(), and analyze_videos(). Each action processes input files (documents, images, or videos) based on a query, returning results. Future extensions will introduce finer-grained actions like 'detect_ppe_violations' for more complex planning and tool orchestration evaluation.
FieldWorkArena System Configuration Overview
User
→
Download Dataset
→
Evaluated Agent
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Run Task
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Generate Execution Log
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Evaluation Program
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Refer Ground Truth
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Output Average Score
Comprehensive Evaluation Metrics for Agentic AI
FieldWorkArena employs a refined evaluation methodology tailored for real-world tasks. Unlike traditional benchmarks with binary correct/incorrect scoring, our system uses three judgment types: Correct, Incorrect, and Partially Correct. For 'Partially Correct' responses, a value corresponding to the degree of agreement with the correct answer is assigned. Numerical tasks for distance, time, and number are scored using a piecewise threshold system (e.g., relative error for distance, absolute difference for time), enabling detailed quantitative evaluation.
Addressing LLM Judging Biases with Fuzzy Match
To provide robust evaluation, correctness is judged by an LLM within a modified fuzzy_match() function. Comparisons with human expert ratings revealed that the LLM judge applied systematically stricter criteria, often rejecting partially correct responses that humans would accept. This implies that reported scores are conservative lower-bound estimates, highlighting the need for more granular evaluation logic that accommodates minor errors and paraphrasing.
| Task | GPT-4o | GPT-5.1 | GPT-5.2 | Gemini-2.5 (Flash) | Gemini-2.5 (Pro) |
|---|---|---|---|---|---|
| Perception (711) | 0.30 | 0.44 | 0.49 | 0.40 | 0.45 |
| Decision making (121) | 0.31 | 0.36 | 0.61 | 0.31 | 0.36 |
| Combination (54) | 0.02 | 0.09 | 0.13 | 0.11 | 0.06 |
| Average | 0.28 | 0.40 | 0.47 | 0.38 | 0.42 |
| Overall, newer and higher-end MLLMs (like GPT-5.2 and Gemini-2.5 Pro) demonstrated higher accuracy. Document extraction showed strong performance, while abstract, spatial, and spatiotemporal understanding from images and videos presented significant challenges, though performance in time-based tasks is improving. | |||||
| Perception sub-task | GPT-4o | GPT-5.1 | GPT-5.2 | Gemini 2.5 Flash | Gemini 2.5 Pro |
|---|---|---|---|---|---|
| Extract documents (26) | 0.69 | 0.73 | 0.81 | 0.73 | 0.62 |
| Abstract from images (98) | 0.37 | 0.39 | 0.43 | 0.39 | 0.42 |
| Abstract from videos (252) | 0.30 | 0.40 | 0.44 | 0.36 | 0.41 |
| Spatial from images (94) | 0.12 | 0.34 | 0.52 | 0.40 | 0.48 |
| Spatiotemporal from videos (121) | 0.43 | 0.50 | 0.59 | 0.38 | 0.44 |
| Temporal from videos (120) | 0.18 | 0.53 | 0.61 | 0.31 | 0.56 |
| Average | 0.30 | 0.44 | 0.49 | 0.40 | 0.45 |
| MLLMs excelled in document extraction but struggled with abstract, spatial, and spatiotemporal understanding from visual data. GPT-5.2 showed notable improvements in spatial and temporal reasoning, suggesting progress in handling time-based information, yet highlighting the need for more granular action spaces. | |||||
Challenges in Video Understanding for MLLMs
Current MLLM limitations mean that video information is primarily processed from a set of extracted images (up to 30 frames) rather than full video content. This reduces temporal inference ability for longer videos. Experiments with 'Chunking' (dividing video into 30-second segments) improved accuracy for video perception tasks, especially temporal understanding. Qwen3-VL showed mixed results, improving temporal understanding but degrading spatiotemporal performance, underscoring the task-dependent nature of video modeling and the need for more sophisticated video analysis methods.
Limitations in Decision-Making and Combination Tasks
Decision-making tasks, which require reasoning and information synthesis, showed higher performance from higher-end models like GPT-5.2, indicating that stronger reasoning capabilities can compensate for imperfect perception. Combination tasks, requiring multiple steps and complex decision-making, exhibited low performance. This suggests that current agent designs may be insufficient to guide models through complex subtasks and integrate outcomes effectively, highlighting a need for improved task-planning functions in agentic AI systems.
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Your AI Implementation Roadmap
A phased approach to integrate agentic AI effectively into your field operations.
Phase 1: Discovery & Strategy
Conduct a detailed assessment of your current field operations, identify key pain points, and define specific AI application opportunities. Establish clear objectives and success metrics aligned with FieldWorkArena's real-world benchmarks. Develop a tailored strategy for agentic AI integration.
Phase 2: Pilot & Proof of Concept
Deploy a pilot agentic AI system in a controlled environment, leveraging FieldWorkArena's data and evaluation methods. Test initial multimodal perception and decision-making capabilities. Gather feedback and refine the agent's performance against defined benchmarks.
Phase 3: Scaled Deployment & Integration
Expand the agentic AI solution to broader operational areas. Integrate with existing enterprise systems and workflows. Implement continuous monitoring and retraining cycles to adapt to evolving real-world conditions, ensuring sustained performance and ROI. Leverage ongoing FieldWorkArena updates.
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