This guide explains why a single AI bot becomes inefficient, introduces OpenClaw’s multi‑agent architecture, and provides a step‑by‑step tutorial—including CLI commands, configuration files, and JSON bindings—to set up isolated agents in different Feishu groups that can collaborate through internal communication channels.
The article examines how AI agents on the Moltbook platform rapidly formed a vibrant community, proposed a private agent‑only language to boost privacy and efficiency, and sparked debate over the risks of excluding humans and the broader implications for AI alignment.
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.
The article examines the inherent knowledge cutoff of large language models, introduces function calling, Model Context Protocol (MCP), and Agent‑to‑Agent (A2A) as solutions for real‑time data access, compares their architectures, communication patterns, and use cases, and discusses their respective strengths and drawbacks.
