How Agentic AI Systems Execute Multi-Step Workflows (Architecture + Stack)

You've probably used ChatGPT or Claude to answer a question. That's a single-turn interaction, you ask, it answers, done. Agentic AI is different. It doesn't just answer, it plans, acts, observes, and iterates until a goal is achieved. This article breaks down exactly how that works: the architecture, the components, the stack, and the tricky parts nobody talks about. What Makes a System "Agentic"? A system is agentic when the LLM isn't just generating text, it's making decisions that affect what happens next. Three markers of an agentic system: Tool use: the model calls external functions like search, code execution, or APIs Multi-step loops: the model acts, sees a result, then decides the next action based on what it observed Goal-directedness: it's working toward an objective, not just completing a prompt Single LLM call = not agentic. LLM in a loop with tools and memory = agentic. **The Core Architecture At their core, agentic AI systems operate through a recurring execution loop: