Agentic AI explained: How agentic AI changes computing from cloud to edge
Agentic AI shifts computing from static models to dynamic, multi-step systems that coordinate memory, tools, and compute across devices and cloud to fulfill user intent autonomously.
Agentic AI systems begin with user intent rather than predefined workflows, using models to reason while orchestrating memory, software tools, and compute across multiple steps to achieve outcomes. This approach moves beyond single-model interactions by integrating context from devices, enterprise systems, and cloud services to prepare and act proactively.
The agentic loop—understand intent, plan, act, and reflect—replaces rigid application flows with human-centric, multimodal interactions that start from simple questions like 'What do you want to do?' though still requiring translation into actionable sequences.
On-device agentic workloads demand efficient memory management, as context retention competes with operating system and application needs, while hardware must evolve to support specialized models within strict power and thermal limits.
Physical AI systems, such as humanoid robots, require agentic runtimes to manage complexity by scheduling tasks, controlling resource access, and defining autonomy boundaries to ensure dependable, scalable behavior in real-world environments.