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Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
May 19, 2026 · Artificial Intelligence

Agent‑Driven R&D Efficiency: Exploration and Practice at QECon Shenzhen 2026

At QECon Shenzhen 2026, Xiaohongshu's tech team will present five technical talks that showcase how AI agents are applied to architecture risk analysis, change automation, large‑model load‑testing data construction, end‑to‑end testing, and client‑side performance, illustrating concrete engineering solutions and measurable productivity gains.

AI AgentAutomationData Pipeline
0 likes · 13 min read
Agent‑Driven R&D Efficiency: Exploration and Practice at QECon Shenzhen 2026
Geek Labs
Geek Labs
Mar 26, 2026 · Artificial Intelligence

Designing AI Agent Collaboration with a 1300‑Year‑Old Imperial System (12.7k Stars)

Edict (三省六部) is an open‑source AI multi‑agent framework that embeds a 1300‑year‑old Chinese imperial bureaucracy into its workflow, offering built‑in approval, real‑time dashboards, task intervention, and full audit trails, and it has already attracted 12.7k GitHub stars.

AI AgentsEdictMulti-Agent Systems
0 likes · 7 min read
Designing AI Agent Collaboration with a 1300‑Year‑Old Imperial System (12.7k Stars)
SuanNi
SuanNi
Jun 2, 2026 · Artificial Intelligence

Harvard’s AutoScientists Lets AI Agents Self‑Organize Research Teams and Outperform Traditional AI Agents

AutoScientists, a Harvard‑built system where nine AI agents self‑organize via a shared state without a central commander, achieves a 74.4% average rank on BioML‑Bench, runs GPT training experiments 1.9× faster, and improves ProteinGym fitness prediction by 12.5%, while ablation studies reveal the critical role of each of its four core mechanisms.

AI AgentsAI researchAutoScientists
0 likes · 12 min read
Harvard’s AutoScientists Lets AI Agents Self‑Organize Research Teams and Outperform Traditional AI Agents
TechVision Expert Circle
TechVision Expert Circle
Jul 16, 2026 · Artificial Intelligence

Enterprise AI Trends for H2 2026: Key Priorities for Tech Leaders

In the second half of 2026, enterprise AI shifts from adoption to reliable, cost‑effective deployment, with six key trends—including multi‑agent orchestration, GraphRAG retrieval, MoE model clusters, AI observability, built‑in data governance, and reorganized AI engineering roles—guiding tech leaders toward trustworthy AI systems.

AI AgentAI ObservabilityAI Team Structure
0 likes · 13 min read
Enterprise AI Trends for H2 2026: Key Priorities for Tech Leaders
High Availability Architecture
High Availability Architecture
Jun 3, 2026 · Artificial Intelligence

From Harness to Dynamic Workflows: Claude Code’s New Multi‑Agent Task Orchestration Paradigm

Claude Code’s Dynamic Workflows let the model generate custom multi‑agent execution frameworks that classify, fan‑out, perform adversarial verification, and run tournaments, addressing agent laziness, self‑preference bias, and goal drift across coding and non‑technical tasks.

AI automationClaude CodeMulti-Agent Orchestration
0 likes · 17 min read
From Harness to Dynamic Workflows: Claude Code’s New Multi‑Agent Task Orchestration Paradigm
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jul 26, 2026 · Artificial Intelligence

EvoX Matches Codex Scores at Just $1.95 per Task – How a Chinese Team Achieved It

The article analyzes why most multi‑agent AI projects fail, introduces EvoX’s swarm‑self‑evolution approach that splits tasks into atomic units, shows benchmark results where EvoX rivals Codex while cutting per‑task cost to $1.95, and explores how information design drives agent self‑organization.

AI AgentsEvoXMulti-Agent Systems
0 likes · 12 min read
EvoX Matches Codex Scores at Just $1.95 per Task – How a Chinese Team Achieved It
Data Party THU
Data Party THU
Jun 3, 2026 · Artificial Intelligence

A Six‑Day, Million‑Token AI‑Driven Review Unpacks the L1‑L5 Agent Hierarchy

The article details how an AI‑augmented workflow completed a 46‑page research paper in six days using 108 agent calls and 648 k tokens, introduces an L1‑L5 autonomy taxonomy, compares four architectural patterns across 17 systems, and highlights six open challenges and key bottlenecks such as continual knowledge accumulation and reliable self‑assessment.

AI AgentsL1-L5 taxonomyagent architecture
0 likes · 8 min read
A Six‑Day, Million‑Token AI‑Driven Review Unpacks the L1‑L5 Agent Hierarchy
Big Data and Microservices
Big Data and Microservices
Apr 24, 2026 · Artificial Intelligence

How to Keep System Complexity in Check for Multi‑Agent Collaboration

The article outlines practical principles and concrete measures—starting with a simple coordinator‑sub‑agent pattern, evolving only when bottlenecks appear, and controlling dimensions such as agent splitting, count, roles, communication, and orchestration—to prevent complexity overload in multi‑agent AI systems, and adds runtime safeguards and a step‑by‑step deployment roadmap.

AI AgentsMulti-agent collaborationarchitectural design
0 likes · 7 min read
How to Keep System Complexity in Check for Multi‑Agent Collaboration
Architect
Architect
Jun 7, 2025 · Artificial Intelligence

Mass Framework: Boosting Multi‑Agent Design with Smarter Prompts & Topologies

The Mass framework, developed by Google and Cambridge University, automates multi‑agent system design by jointly optimizing prompts and topologies through three staged processes, demonstrating significant performance gains over existing methods across various tasks while highlighting the importance of coordinated prompt‑topology optimization.

AI researchMass frameworkMulti-Agent Systems
0 likes · 6 min read
Mass Framework: Boosting Multi‑Agent Design with Smarter Prompts & Topologies
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jun 1, 2026 · Artificial Intelligence

MetaAgent-X Enables Agents to Self‑Evolve: A New Paradigm for Native Collaboration

MetaAgent‑X integrates system design and execution within a single base model, using hierarchical rollout and stagewise co‑evolution to jointly train Designer and Executor roles, and achieves significant gains over single‑agent and prior multi‑agent baselines on math and code benchmarks.

AI collaborationLarge Language ModelsMetaAgent-X
0 likes · 13 min read
MetaAgent-X Enables Agents to Self‑Evolve: A New Paradigm for Native Collaboration
Data Party THU
Data Party THU
Jul 21, 2026 · Artificial Intelligence

Task Decomposition with Multi‑Agent Systems: Boosting Complex AI Workflows

This article reviews a Berkeley PhD thesis that argues powerful foundation models still need task decomposition, detailing six contributions—including LLM‑grounded diffusion, video diffusion, self‑correcting loops, detailed local description, adaptive parallel reasoning, and ThreadWeaver—to organize computation across multiple agents for more controllable, reliable AI systems.

AI systemsLLMMulti-Agent Systems
0 likes · 16 min read
Task Decomposition with Multi‑Agent Systems: Boosting Complex AI Workflows
AI Product Manager Community
AI Product Manager Community
Mar 8, 2025 · Artificial Intelligence

How OWL AI Agent Outperforms OpenManus: Technical Deep Dive

The article introduces the OWL (Optimized Workforce Learning) general‑purpose AI agent, explains its six‑step architecture, benchmark performance surpassing OpenManus, and argues that its innovations represent genuine application‑level advancement rather than mere “shell‑wrapping,” while highlighting its multi‑agent collaboration framework.

AIAutomationinnovation
0 likes · 5 min read
How OWL AI Agent Outperforms OpenManus: Technical Deep Dive
Fun with Large Models
Fun with Large Models
Jan 10, 2026 · Artificial Intelligence

Designing Decentralized Multi‑Agent Networks with LangGraph: The Swarm Architecture

This article explains LangGraph's network (decentralized) architecture for multi‑agent systems, compares it with supervisor and hierarchical designs, and provides a step‑by‑step Python example using the langgraph‑swarm library to build agents that can dynamically hand off control and preserve conversation continuity.

LangGraphNetwork ArchitecturePython
0 likes · 13 min read
Designing Decentralized Multi‑Agent Networks with LangGraph: The Swarm Architecture