If multimodal AI could run locally on devices, it would mark a significant breakthrough in how we interact with technology. It would become the natural home for AI reasoning agents — smart systems capable of understanding context by processing raw data from device sensors, cameras, and wearables. These agents would use this data to make complex, human-like decisions autonomously and almost in real-time.
What sets these AI agents apart is their ability to engage in deep reflection, cycling through up to 10 iterations to refine their outputs. Reflection allows the agents to critique their own results, identify areas for improvement, and generate better outcomes with each pass. This approach not only enables reasoning agents to outperform even the most advanced language models but also does so with a lower total cost of ownership (TCO), thanks to efficient use of edge hardware.
By working locally, on-device AI ensures low-latency data processing, providing reasoning agents with the real-time inputs needed to make fast and accurate decisions. This combination of speed, precision, and reflection unlocks the potential for entirely new consumer use cases, paving the way for real-world adoption of AI in everyday life.
The potential for AI agents at the edge is significant. IDC predicts that by 2025, 75% of enterprise-generated data will be created and processed at the edge. Gartner anticipates that by 2025, 75% of enterprise-generated data will be created and processed outside centralized data centers or the cloud.
Applications in Different Sectors
- Transportation: Autonomous vehicle navigation and traffic optimization
- Manufacturing: Quality control inspections and predictive maintenance
- Retail: Personalized product recommendations and inventory management
- Healthcare: Real-time patient monitoring and diagnosis support
- Sport & Wellness: Real-time sport & fitness coaching
AI agents at the edge will enable businesses to react to data in real-time, significantly impacting how companies analyze and respond to information. This is particularly crucial in situations where immediate action is necessary, such as in industrial settings or customer service interactions