Why AI Agents Are a System Design Problem, Not Just LLM Plus Tools

A developer exploring agentic AI systems argues that simply combining a large language model with tools does not constitute a true agent. Unlike basic LLM apps that follow a linear input-output flow, agentic systems operate in a continuous loop of reasoning, acting, and observing until a goal is achieved. This architecture introduces classic software engineering challenges such as state management, error handling, retries, and observability. The author contends that building reliable agents is fundamentally a system design problem, with the LLM serving as just one component inside a broader execution framework. The key open question being explored is how to design execution loops that allow agents to reason and act predictably in production environments.