Agentic AI Workflows Explained: Patterns, Infrastructure, and GPU Requirements

Agentic AI workflows have evolved from simple model calls to complex systems where the model autonomously plans its steps, utilizes tools, checks its output, and iterates until completion, distinguishing them from traditional fixed-sequence workflows. These workflows are characterized by their bursty and unpredictable compute demands, requiring infrastructure capable of handling stateless, horizontally scalable workers, fast cold starts, and real parallelism billed by use. Five patterns define agentic systems: sequential, parallel, hierarchical, event-driven, and recursive, each offering different operational complexities and benefits. The infrastructure needs to accommodate these patterns by efficiently managing workloads and scaling dynamically, a task well-suited to platforms like Runpod Serverless, which can quickly scale resources in response to demand spikes and maintain simplicity in deployment and execution.

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