1. Introduction: From “Individual AI” to “AI Expert Teams” In the early stages of artificial intelligence development, we tended to view AI as an “all-rounder.” It could answer questions, write articles, create images, and generate code, seemingly capable of anything. However, when applying AI to complex business scenarios—such as supply chain management, investment research analysis, … Read more
Have you ever thought that one day your AI assistant could not only answer questions but also actively help you write code, edit images, send emails, and even browse the internet for information and make decisions? Not just a mechanical conversation of “you say one thing, it replies another,” but like a real “digital colleague”—with … Read more
Background This article is the fifth in the series on telecom AIOps intelligent agents. Building on the previous articles focusing on monitoring, diagnosis, and optimization, it centers on the closed-loop management of operational events and knowledge accumulation—automated post-incident review reports. We have constructed an AI reporting agent based on Dify’s workflow orchestration capabilities and the … Read more
The Google research team has proposed a framework for the Personal Health Agent (PHA) framework: a multi-agent system framework consisting of three sub-agents responsible for data analysis, medical knowledge reasoning, and health coaching, managed and integrated by a central coordinator that unifies the outputs of each agent, providing personalized and comprehensive health guidance by integrating … Read more
Theme of This Issue With the rapid development of LLMs and their demonstrated application potential in various fields, research on LLM-based agents has garnered widespread attention from scholars. However, single-agent systems often exhibit drawbacks such as lengthy context and poor interpretability in reasoning processes in complex task scenarios. Meanwhile, research on multi-agent systems has recently … Read more
To address key issues in the design of autonomous combat systems for drone swarms, a design method based on Multi-Agent systems is proposed. An Agent model for each node in the drone swarm is established along with its inference rules; in response to the need for modularization and generalization of the simulation system, an interoperable … Read more
https://arxiv.org/pdf/2502.02533 “Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies” by Google Abstract This article introduces the Multi-Agent System Search (MASS) framework proposed by Google, which optimizes Multi-Agent Systems (MAS) by alternating between prompt and topology optimization, significantly enhancing performance in various complex tasks. The article presents the Multi-Agent System Search, an optimization framework for … Read more
01Introduction In previous articles, we learned how to use AgentScope to build a conversation with @ functionality, as well as how to set up a simple Go game. A commonality in these applications is that large models generate responses directly based on prompts; for simple tasks, perhaps the large model can handle them. However, when … Read more
🔖 Features 🧠 Intelligent Intent Classification — Dynamically routes queries to the most suitable Agent based on context and content.🔤 Bilingual Support — Fully supports both Python and TypeScript.🌊 Flexible Agent Responses — Supports both streaming and non-streaming responses from different Agents.📚 Context Management — Maintains and utilizes session context across multiple Agents for coherent … Read more
I recently spent a lot of time trying to elevate Cursor, a common Agentic AI tool, to a new level. Initially, Cursor could only write and execute code, which made us feel that while it was like a clever little brother, there was still room for improvement. Therefore, we rewrote Cursor Rules to enable it … Read more
