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AgentGuide
AgentGuide
Apr 14, 2026 · Artificial Intelligence

What Is Mixture-of-Agents (MoA) and How Does It Boost Performance?

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.

AIAgent CollaborationMixture of Agents
0 likes · 4 min read
What Is Mixture-of-Agents (MoA) and How Does It Boost Performance?
Data STUDIO
Data STUDIO
Apr 1, 2026 · Artificial Intelligence

Blackboard System: Enabling Dynamic Collaboration Among Expert AI Agents

This article compares a rigid sequential multi‑agent pipeline with a flexible blackboard architecture, showing how shared memory and a dynamic controller let specialist AI agents cooperate opportunistically, obey conditional user instructions, and achieve higher efficiency and instruction‑following scores.

Blackboard SystemDynamic SchedulingLLM
0 likes · 21 min read
Blackboard System: Enabling Dynamic Collaboration Among Expert AI Agents
phodal
phodal
Feb 24, 2026 · Artificial Intelligence

How Routa Turns Multi‑Agent AI Coding into an Engineered Collaboration Framework

Routa is an engineering‑focused multi‑agent framework that separates tasks, state, events, and execution into controllable modules, enabling open‑ecosystem AI coding agents to collaborate through structured specifications, event‑driven coordination, and verifiable tool interfaces rather than fragile prompt stitching.

AI collaborationAgent CoordinationMulti-Agent
0 likes · 12 min read
How Routa Turns Multi‑Agent AI Coding into an Engineered Collaboration Framework
Machine Heart
Machine Heart
May 18, 2026 · Artificial Intelligence

JiuwenSwarm Launches Coordination Engineering for the ‘Beekeeping’ Era of AI Agents

openJiuwen’s open‑source JiuwenSwarm implements Coordination Engineering—a full‑stack system comprising Agent Swarm, Swarm Skills, a Skills Hub and self‑evolution—enabling autonomous multi‑agent collaboration, demonstrated by medical, coding, video and game case studies and achieving a 94.2% PinchBench score with 34.8% token savings.

AI AgentsCoordination EngineeringJiuwenSwarm
0 likes · 13 min read
JiuwenSwarm Launches Coordination Engineering for the ‘Beekeeping’ Era of AI Agents
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 AgentData PipelineLLM
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 AgentsEdictcomparative analysis
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
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 frameworkTopology Design
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 collaborationMetaAgent-XReinforcement Learning
0 likes · 13 min read
MetaAgent-X Enables Agents to Self‑Evolve: A New Paradigm for Native Collaboration
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.

AIMulti-Agentautomation
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.

LangGraphMulti-AgentNetwork Architecture
0 likes · 13 min read
Designing Decentralized Multi‑Agent Networks with LangGraph: The Swarm Architecture
IT Architects Alliance
IT Architects Alliance
Jun 9, 2026 · Artificial Intelligence

From Implementer to Orchestrator: 7 Essential Skills Every 2026 Architect Must Master

The article shares a practitioner’s journey from chasing every new AI framework to focusing on seven durable capabilities—context management, tool design, data‑driven evaluation, robust harness, isolation, traceability, cost control, and disciplined multi‑agent collaboration—that will keep architects productive for years to come.

AI AgentsContext managementEvaluation Framework
0 likes · 11 min read
From Implementer to Orchestrator: 7 Essential Skills Every 2026 Architect Must Master
AI Algorithm Path
AI Algorithm Path
May 8, 2025 · Artificial Intelligence

Five Essential AI Agent Workflow Design Patterns

This article introduces five core workflow design patterns for AI agents—Prompt Chaining, Routing, Parallelization, Orchestrator‑Worker, and Evaluator‑Optimizer—explaining their mechanics, concrete examples, suitable scenarios, and how they help build reliable, maintainable LLM‑driven systems.

AI AgentsEvaluator-OptimizerLLM workflow
0 likes · 10 min read
Five Essential AI Agent Workflow Design Patterns
Smart Workplace Lab
Smart Workplace Lab
Apr 20, 2026 · Artificial Intelligence

Building Enterprise‑Ready Agentic AI: Layered Architecture, Design Patterns, and Production Practices

The article presents a detailed, enterprise‑grade Agentic AI reference architecture—covering dynamic control loops, termination logic, six/seven‑layer stacks, key design patterns like ReAct and Plan‑and‑Execute, memory management, observability, cost optimization, and a step‑by‑step rollout roadmap for 2026 production deployments.

LLMObservabilityagentic AI
0 likes · 9 min read
Building Enterprise‑Ready Agentic AI: Layered Architecture, Design Patterns, and Production Practices
Data Party THU
Data Party THU
May 1, 2026 · Artificial Intelligence

Scaling Large-Scale Agent Networks: A Review of Topology, Memory, and Updates

This review examines why some large‑scale multi‑agent systems remain stable while others falter, introducing a three‑dimensional taxonomy—topology, memory scope, and update behavior—to explain scalability limits and highlighting world‑model inconsistency as a deeper bottleneck than communication protocols.

Scalabilitydynamic updatesmemory
0 likes · 9 min read
Scaling Large-Scale Agent Networks: A Review of Topology, Memory, and Updates
PaperAgent
PaperAgent
Feb 11, 2026 · Artificial Intelligence

Unlocking Agentic Reasoning: A Deep Dive into the New LLM Paradigm

This comprehensive review dissects the emerging Agentic Reasoning paradigm for large language models, outlining its three‑layer architecture, core capabilities, optimization modes, benchmark suites, and real‑world applications across mathematics, science, embodied AI, healthcare, and autonomous web exploration.

AI benchmarksArtificial IntelligenceAutonomous Agents
0 likes · 10 min read
Unlocking Agentic Reasoning: A Deep Dive into the New LLM Paradigm