Designing effective AI agents requires a modular and role-based architecture, deep observability from the start, and robust feedback loops to ensure continuous improvement. Successful implementation of these principles transforms LLMs from static tools into dynamic, autonomous systems capable of adapting to real-world complexities. Understanding the foundational concepts of agent design can bridge the gap between basic AI applications and more sophisticated, self-improving AI agents.