Artificial intelligence has long relied on a centralized model—AI systems are trained in large data centers, deployed via cloud platforms, and depend on stable network connections for real-time operations. This approach has worked well for use cases where latency and autonomy are not critical. However, as AI becomes embedded in sectors such as manufacturing, healthcare, and autonomous systems, the need for distributed intelligence is growing.
Two technologies are central to this evolution: and . AI agents are autonomous software programs capable of reasoning, learning, and making decisions independently. When paired with Edge AI—which enables models to run directly on local devices—they create a powerful synergy: enabling faster, cost-effective, and more resilient AI systems that function even in disconnected environments.
According to , worldwide spending on edge computing is expected to exceed $378 billion by 2028, driven by the rising need for , automation, and improved customer experiences.
In this article, we’ll explore how AI agents and edge AI are reshaping distributed intelligence, and what the future may hold as these technologies converge.
