This article explains how the Agent Loop—an engineered while‑loop that repeatedly calls an LLM, decides when to use tools, executes them, and feeds results back—creates persistence, style, memory, judgment, and safety boundaries that together make an AI agent feel like it has its own personality.
This article explores the essential stop‑condition designs for AI agents, detailing hard limits, task‑completion checks, explicit termination tools, loop detection, error accumulation, and user interruption, and then examines concrete implementations in OpenManus and Gemini CLI with code examples and multi‑layer safeguards.
This article explains how the authors built a multi‑agent framework for the energy domain that splits complex tasks into simple subtasks, uses a planner‑scheduler‑executor pipeline, implements structured communication, memory management, and streaming output to overcome large‑model attention diffusion and improve efficiency and reliability.
