multi-agent systems

AI automation

Efficient Web Automation Assistant: Privacy-Preserving Multi-Agent AI Browser Extension | Open Source Daily No.788

🔍 Overview of Featured Open-Source Projects This collection highlights multi-agent AI systems, automation frameworks, and specialized libraries that are shaping modern development workflows. Projects range from browser automation to database querying, chatbot creation, protein design, and mobile app reverse engineering. --- 1. nanobrowser / nanobrowser GitHub Repository Stars: 10.7k License: Apache-2.

AI in gaming

AI Transformation in Game Development: NetEase’s Multi-Agent System and Knowledge Engineering Practices

# Practical Implementation of Large Language Models in Game Development In recent years, **AI coding tools** have rapidly emerged, yet their adoption in *large-scale game engineering* remains limited. The main constraints are **LLM context length** and the **complex, highly flexible nature of game logic**. This article is based on my talk

LLM agents

IEEE | Where Are the Capability Boundaries of LLM Agents? First “Graph Learning Agent (GLA)” Review Builds a Unified Blueprint for Complex Systems

📰 Research Update — 2025-11-09, Beijing A new research field has been formally defined: Graph-augmented LLM Agents (GLA). --- Authors & Institutions * Yixin Liu, Shiyuan Li, Shirui Pan — Griffith University * Guibin Zhang — National University of Singapore * Kun Wang — Nanyang Technological University --- The Rise & Challenges of LLM Agents LLM Agents have

LLM architecture

Completely Blown Up! A Complete Guide to LLM Application Architecture: From Prompt to Multi-Agent

Table of Contents * What is an Agent? * Why did Agents emerge? * How to implement an Agent * Summary --- Introduction Since the launch of ChatGPT, the industry has actively explored practical applications of LLMs (Large Language Models). This article outlines the evolution of LLM implementation approaches — from Prompt Engineering → Chain Orchestration

AI agents

How to Make Agents Meet Expectations: Top 10 Practical Lessons from Building Cloud Assistant Aivis with Context Engineering and Multi-Agent Systems

Building & Optimizing AI Agents: Lessons from YunXiaoer Aivis This is the 123rd article of 2025 (Estimated reading time: 15 minutes) --- 01. Background This year, our team has been focusing on the YunXiaoer Aivis project — a digital employee in the Alibaba Cloud service domain. It represents our transition from

AI marketing

One Brief Awakens Eight AI Agents: A New Era for Advertisers | 43 Talks

AI Reshaping the Marketing Industry The wave of AI is driving a deep structural transformation in marketing. At the 43 Talks 5th Speculative Salon — AI Marketing Session, co-hosted by 43 College, Yilun AI, and Origin Academy, the first guest speaker — David Chu, CEO of Zhiwei Jianxing and founder of the

AI agents

How to Make Agents More Aligned with Expectations: Top 10 Practical Lessons from Building the Multi-Agent Cloud Assistant Aivis Using Context Engineering

# Building Better Agents: Lessons from Yunxiaoer Aivis ## Introduction This year, our team has invested heavily in **Yunxiaoer Aivis** — a digital employee in the Alibaba Cloud services domain. It represents our evolution from traditional intelligent customer service assistance to a new **end-to-end Multi-Agent** capability. ![image](https://blog.aitoearn.ai/content/images/

AI in gaming

AI Transformation in Game Development: NetEase’s Multi-Agent System and Knowledge Engineering Practices

# Building a “Super-Assistant” for Complex Game Development with LLMs In the last couple of years, AI coding tools have proliferated, but their role in **game project–level engineering** has remained limited. **Two main constraints** hold them back: - **LLM context length limits** - **The high complexity and flexibility of game

AI agents

The Art of Conversing with AI: Beyond Prompting, Mastering the Secrets of Agentic Context Engineering

AI Agents and the Art of Context Engineering Source of Inspiration: Lance (LangChain) & Pete (Manus) Original Video: Watch on YouTube --- We are entering a new era where AI Agents can autonomously execute long, complex tasks. Yet an unexpected paradox arises: > The more we rely on them, the

systems thinking

Mental Models in Architecture and the Social Impact of AI: A Conversation with Nimisha Asthagiri

Podcast Summary: Systems Thinking, Multi-Agent Systems, and AI & Society In this episode, Michael Stiefel speaks with Nimisha Asthagiri about: * The importance of systems thinking in architecture. * The challenges of scaling multi-agent systems. * Risks of applying technology outside its original design scope. * Whether humanity can achieve a healthy relationship with

Responsible AI

Scaling Responsible Multi-Agent Architectures: Applying Systems Thinking

Transcript Rewrite – Responsible AI, Multi‑Agent Systems, and Systems Thinking Introduction: From Systems Thinking to Multi‑Agent Reality The keynote brilliantly set the stage—introducing systems thinking and complexity theory concepts. A year ago, I was deeply optimistic about multi‑agent systems transforming workflows, boosting productivity, and positively impacting businesses.

AI agents

Hands-on Test of MGX: Can a Team of Agents Coding Together Bring Us Closer to AGI than “Model-as-Agent”?

Multi-Agent Can Solve Many Problems — But How to Call Them Is the New Challenge --- What Makes Humans Unique? It’s hard to answer what truly sets humans apart from animals, but three essential traits have shaped our evolution as the “spirit of all living things”: * Tool Usage — From gripping