The Digital Press

AI system development has evolved from simple Large Language Models (LLMs) to more complex AI agents that can autonomously manage tasks, but not every application needs this level of complexity.

Why it matters: Choosing the right AI architecture balances cost, complexity, and functionality to better serve specific real-world use cases.

The big picture: AI development progresses through stages—pure LLMs, Retrieval Augmented Generation (RAG), AI workflows, and fully autonomous AI agents.

The stakes: Overusing AI agents can add unnecessary complexity; simpler workflows often provide more reliable, maintainable solutions.

Commenters say: Readers express confusion over how AI agents differ from workflows, questioning if agents rely on LLMs to dynamically decide actions without explicit branching logic.