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James' Growth Diary
James' Growth Diary
Apr 28, 2026 · Artificial Intelligence

Mastering LangGraph Multi‑Agent Collaboration: The Supervisor Pattern Explained from Theory to Practice

The article examines why single‑agent setups fail, introduces the Supervisor pattern for clear responsibility separation, compares Tool‑Calling and Handoff approaches, provides a complete TypeScript implementation, explores hierarchical supervisors, and outlines five common pitfalls with concrete fixes.

HandoffLangGraphMulti-Agent
0 likes · 15 min read
Mastering LangGraph Multi‑Agent Collaboration: The Supervisor Pattern Explained from Theory to Practice
AI Explorer
AI Explorer
Mar 7, 2026 · Artificial Intelligence

Can Tang Dynasty Bureaucracy Manage AI Agents? Inside the edict Open‑Source Multi‑Agent Framework

The edict project adapts the Tang dynasty’s three‑province, six‑department bureaucracy to a multi‑agent AI framework, introducing a hierarchical “Prince”, “Three Ministries”, and “Six Departments” structure with a veto‑power “Chancellor” layer, real‑time dashboards, task intervention, health monitoring, and zero‑dependency deployment.

AI AgentsEdictPython
0 likes · 9 min read
Can Tang Dynasty Bureaucracy Manage AI Agents? Inside the edict Open‑Source Multi‑Agent Framework
Fighter's World
Fighter's World
Jun 21, 2025 · Artificial Intelligence

Speculating Devin’s Context Engineering Architecture: How Long‑Horizon Agents Preserve Complete Context

The article analyzes why context engineering is crucial for multi‑agent AI systems, illustrates the fragility caused by fragmented context with a Flappy Bird analogy, and proposes three detailed speculative components—a compression‑to‑structure pipeline, a hybrid layered memory architecture, and a context‑aware coordination mechanism—culminating in a unified reference design for long‑horizon agents.

Agent CoordinationCompression PipelineContext Engineering
0 likes · 22 min read
Speculating Devin’s Context Engineering Architecture: How Long‑Horizon Agents Preserve Complete Context
Alibaba Cloud Infrastructure
Alibaba Cloud Infrastructure
Mar 16, 2026 · Artificial Intelligence

Scaling Agentic Reinforcement Learning with a Decoupled T‑Architecture Using Verl and Argo Workflows

Agentic reinforcement learning is evolving from simple text generation to complex, scalable agents, but large‑scale deployment faces challenges like massive parallel rollout scheduling and reproducible environments; this article presents a decoupled T‑architecture that separates high‑level RL logic (Verl) from execution orchestration (Argo Workflows) to address these issues.

Argo WorkflowsScalable Reinforcement Learningagentic RL
0 likes · 10 min read
Scaling Agentic Reinforcement Learning with a Decoupled T‑Architecture Using Verl and Argo Workflows
Design Hub
Design Hub
Mar 28, 2026 · Artificial Intelligence

Why Harness Engineering Is Emerging as a New Kind of Company

The AI community is shifting its focus from model performance to building runnable, observable, and scalable agent systems, a trend illustrated by the rise of Harness Engineering, Open Agents Company, and Agent Matrix across X discussions, GitHub projects, and developer meetups.

AI AgentsAI InfrastructureAgent Matrix
0 likes · 14 min read
Why Harness Engineering Is Emerging as a New Kind of Company
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
May 15, 2026 · Artificial Intelligence

ClawMark: A Living‑World Benchmark for Multi‑Turn, Multi‑Day, Multimodal Coworker Agents

The ClawMark benchmark introduces 100 multi‑turn, multi‑day tasks across 13 professional scenarios and five stateful sandbox services, evaluating seven cutting‑edge agent systems with a top weighted score of 75.8 but only a 20% strict success rate, highlighting the difficulty of end‑to‑end collaborative agent performance.

LLMagent performancebenchmark
0 likes · 4 min read
ClawMark: A Living‑World Benchmark for Multi‑Turn, Multi‑Day, Multimodal Coworker Agents
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Apr 25, 2026 · Artificial Intelligence

Coordination Engineering’s Key Leap: Jiuwen Claw Introduces the New Team Skills Paradigm

Jiuwen Claw advances AI coordination engineering by unveiling Coordination Engineering and the first standardized multi‑agent capability package, Team Skills, which codifies collaboration workflows, offers a creator tool and hub for reusable, cross‑framework team skills such as a medical expert consultation team.

AI collaborationCoordination EngineeringJiuwenClaw
0 likes · 10 min read
Coordination Engineering’s Key Leap: Jiuwen Claw Introduces the New Team Skills Paradigm
James' Growth Diary
James' Growth Diary
May 7, 2026 · Artificial Intelligence

Mastering the Coordinator Pattern: Control‑Plane/Data‑Plane Separation for Scalable Multi‑Agent Orchestration

The article dissects Claude Code’s Coordinator pattern, explaining how separating the control plane from the data plane eliminates serial bottlenecks, context overflow, and fault‑propagation in single‑Agent setups, and details the dual back‑end design, message protocol, engineering insights, technical debt, and practical adoption guidelines.

Backend AbstractionControl PlaneData Plane
0 likes · 16 min read
Mastering the Coordinator Pattern: Control‑Plane/Data‑Plane Separation for Scalable Multi‑Agent Orchestration
PaperAgent
PaperAgent
May 13, 2026 · Artificial Intelligence

One-for-All Multi-Agent Collaboration: Adaptive Cross-Task Topology Design

The paper introduces OFA-MAS, a one‑for‑all multi‑agent system that learns a universal topology designer using task‑aware graph encoding and a Mixture‑of‑Experts generator, achieving superior performance, OOD generalization, robustness, and efficiency across six major benchmarks.

LLMMixture of ExpertsTask-Aware Graph Encoder
0 likes · 14 min read
One-for-All Multi-Agent Collaboration: Adaptive Cross-Task Topology Design
Machine Heart
Machine Heart
May 30, 2026 · Artificial Intelligence

Beyond Single-Agent: Survey of Collaboration, Attribution, and Self‑Evolution in LLM Multi‑Agents

This survey introduces the LIFE framework for LLM‑based multi‑agent systems, outlining four stages—from individual agent capabilities through collaborative structures, failure attribution, to systemic self‑evolution—while analyzing how role design, communication, and scheduling affect performance, error propagation, and adaptive improvement.

AI SurveyCollaborationFailure Attribution
0 likes · 10 min read
Beyond Single-Agent: Survey of Collaboration, Attribution, and Self‑Evolution in LLM Multi‑Agents
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jun 3, 2026 · Artificial Intelligence

How 8 Agents Can Converge Stably: Trust‑Region Constraints Reshape Multi‑Agent LLM Workflows

The paper introduces TeamTR, a trust‑region fine‑tuning framework that mitigates compounding occupancy shift in multi‑agent LLM workflows by fresh rollout sampling and token‑level KL constraints, achieving stable performance gains of up to 7.1% overall and dramatic improvements on large‑scale tasks such as AIME24.

AI coordinationTeamTRfine-tuning
0 likes · 9 min read
How 8 Agents Can Converge Stably: Trust‑Region Constraints Reshape Multi‑Agent LLM Workflows
PaperAgent
PaperAgent
Dec 22, 2025 · Artificial Intelligence

Can Budget‑Aware Tool Use Unlock Scalable AI Agents? A Deep Dive

This article analyzes recent Google research on test‑time scaling and agentization, introducing budget‑aware tool use and the BATS framework, presenting experimental results across 180 configurations, uncovering scaling laws, and offering a predictive model for optimal multi‑agent architectures.

AI AgentsBATS frameworkLLM Tool Use
0 likes · 7 min read
Can Budget‑Aware Tool Use Unlock Scalable AI Agents? A Deep Dive
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
May 31, 2026 · Artificial Intelligence

MetaAgent-X Enables Self‑Evolving Agents for Native Collaboration

MetaAgent-X tackles the limitation of fixed‑executor multi‑agent systems by jointly training a Designer that creates lightweight Python‑based collaboration scripts and an Executor that runs them, using hierarchical rollouts and stagewise co‑evolution to improve both design and execution across math and code benchmarks.

LLMMetaAgent-XReinforcement Learning
0 likes · 13 min read
MetaAgent-X Enables Self‑Evolving Agents for Native Collaboration
Wuming AI
Wuming AI
Dec 10, 2025 · Artificial Intelligence

Workflow vs Agent: Choosing Fixed Pipelines or Dynamic LLM Orchestration

This article explains the fundamental differences between workflow‑style fixed pipelines and agent‑style dynamic LLM orchestration, compares their characteristics, reviews classic workflow patterns, and walks through a concrete implementation using the Kuzi platform with step‑by‑step screenshots.

AIAgentKuzi
0 likes · 9 min read
Workflow vs Agent: Choosing Fixed Pipelines or Dynamic LLM Orchestration