Agentic AI Solutions Such as FoxBrain Have Great Potential, but to Maximize Their Impact, Fundamental Considerations Still Apply

Foxconn announced in March 2025 the introduction of an Agentic Artificial Intelligence (AI) solution, FoxBrain, to optimize its factories and supply chains. The objective is for AI agents to improve operations by reconfiguring workflows in real time due to changes in demand forecasts, supply chain, and current capacity levels.

Contract manufacturing and the assembly of electronics is characterized by variations to customer orders on very short notice. FoxBrain shows how manufacturers can add flexibility into their operations via AI agents.

FoxBrain supports conventional use cases such as predictive maintenance programs and visual inspection for quality control, while  reducing the time needed to configure a machine for new production runs and assisting engineers in troubleshooting issues. In addition, FoxBrain analyzes supply chain data and can adjust procurement orders, inventory levels, and instruct logistics functions in response to prevailing conditions. Having implemented FoxBrain at several facilities, Foxconn can identify and scale best practices more quickly. The creation and training of FoxBrain was led by the Director of Foxconn’s Artificial Intelligence Research Center, Yung-Hui Li.

Foxconn’s manufacturing hubs in Taiwan were the first facilities to utilize FoxBrain followed by the electronics assembly plants in China. The solution is part of quality control programs, efforts to improve the coordination of robots, and to optimize the logistics inside the facilities. FoxBrain is also being implemented at facilities in the United States and Foxconn uses the Fii Omniverse Digital Twin (FODT) platform to simulate, introduce, and manage FoxBrain deployments across its factories.

There is already evidence that FoxBrain is providing tangible benefits with Foxconn reporting the time required for equipment setup having dropped by 80% and dropping for human inspection by 70%. Equipment reliability has improved by 75%.

Local facilities can adjust FoxBrain to their specific requirements (including product specifications and local regulations); however, managing the compute and data resources and training the model’s performance are the responsibility of the centralized team.

The team has encountered several challenges when introducing FoxBrain at its facilities. The company needs to upgrade infrastructure at its facilities to accommodate AI-based workflows. FoxBrain was created and optimized for interactions in Chinese, so adjustments were required for non-Chinese speakers. The workforce on the factory floor needs to be trained on how the AI agents can improve processes and decision-making. Furthermore, Foxconn needed to devise and implement change management programs to explain the objectives behind FoxBrain and its benefits, such as reducing the time needed to perform repetitive tasks.

More fundamentally managing expectations is the key challenge for Foxconn. Engineers, technologists, and business managers have different perceptions and expectations regarding the impact that Agentic AI can have. In an attempt to ensure alignment, Foxconn creates multi-functional teams combining individuals with technology and operational expertise.

The motivation for developing FoxBrain was to support operations at Foxconn’s factories. Despite only being launched in March 2025, the company already plans to open-source FoxBrain with the AI community. This constitutes a very different approach to innovations developed by technology vendors for sure.