The article explains how HiClaw’s Worker templates turn ad‑hoc multi‑agent tasks into repeatable, stable processes by encapsulating skills, constraints, and SOPs, enabling easier reuse, versioning, and distribution across teams and private markets.
MoA (Mixture-of-Agents) is a quality-first multi-agent collaboration mode where multiple large models act as Proposers and an Aggregator merges their diverse outputs, delivering more robust and higher-quality results at the cost of increased latency, making it ideal for high-value, open-ended tasks and extensible via multi-layer aggregation.
The article traces the evolution from early function calls to Tool Calling 2.0, explaining how Model Context Protocol standardizes tool interfaces, how Agent‑to‑Agent communication enables multi‑AI collaboration, and how Skills combine tool orchestration into autonomous workflows, while also discussing remaining challenges such as intent misinterpretation and security risks.
Claude Code’s Team mode replaces a lone AI assistant with a manager‑agent and multiple specialist agents, enabling parallel code review, testing, documentation, and debugging through shared task lists and bidirectional messaging, while outlining configurations, use‑case comparisons, advanced patterns, and practical demos.
This article details the end‑to‑end design of an enterprise AI office assistant, covering the three‑layer framework of intent planning, context engineering, and data self‑iteration, the key pain points of intent understanding, knowledge integration, and quality control, and practical architectural and implementation solutions for scalable deployment.
The article demonstrates how to construct an AI research assistant using LangGraph’s multi‑agent framework, detailing system architecture, specialized agents for research, fact‑checking and report writing, workflow orchestration, dynamic routing, parallel processing, debugging, and performance evaluation, showing a 40‑60% efficiency gain over single‑model approaches.
This article explores how first‑principle reasoning underpins the development of AI agents, traces their collaborative technology evolution, details core capabilities such as compute, memory, prediction and action, and forecasts future directions like multimodal models, reduced prompting, and extensive data sharing.
This article explores how first‑principle thinking underpins AI agents, traces their development from single‑craftsman tools to enterprise‑level collaborations, outlines core capabilities such as compute, memory, prediction and action, and forecasts future directions like multimodal models, reduced prompting, and extensive data sharing.
The article explores Google’s A2A protocol and the MCP framework, comparing their roles as complementary gears that enable AI agents to collaborate, extend capabilities, and interact with external resources, illustrated with a smart‑customer‑service scenario and future outlooks.
This article explains the MCP and A2A communication protocols, compares their purposes, and explores how they can be combined to build multi‑agent AI systems with coordinated task management, tool integration, and security safeguards.
The article surveys the rise of multi‑agent LLM systems, detailing how specialized agents collaborate on tasks such as travel planning, outlining their workflow, comparing them with single‑agent models, listing prominent frameworks, and discussing current challenges and research citations.
This article reviews MetaGPT, an open‑source multi‑agent framework that integrates human‑engineered SOPs into LLM‑based agents to improve software generation, data interpretation, and simulation tasks, highlighting its rapid community growth, experimental successes, tool integration strategies, and future research directions.
