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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.

Agentic RLArgo WorkflowsScalable Reinforcement Learning
0 likes · 10 min read
Scaling Agentic Reinforcement Learning with a Decoupled T‑Architecture Using Verl and Argo Workflows
Old Zhang's AI Learning
Old Zhang's AI Learning
Jan 30, 2026 · Artificial Intelligence

Mastering Skills, Tools, MCP, and Subagents in Anthropic’s Agent Course

This article breaks down the core concepts from the free Anthropic short course—Tools, Skills, the Model Context Protocol (MCP), and Subagents—explaining their roles, differences, and how they combine to build reliable, parallelizable AI agents, illustrated with a customer‑insight case study.

AI agentsAgent architectureAnthropic
0 likes · 8 min read
Mastering Skills, Tools, MCP, and Subagents in Anthropic’s Agent Course
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
AI Explorer
AI Explorer
Apr 16, 2026 · Artificial Intelligence

Is a Lightweight Multi‑Agent Workflow Framework the Next Paradigm for AI Application Development?

OpenAI’s newly open‑sourced Agents SDK for Python offers a lightweight, vendor‑neutral framework that lets developers define, orchestrate, and monitor multiple AI agents—each acting as a specialized tool or sandboxed worker—enabling rapid construction of complex, production‑grade AI collaboration workflows.

AI workflowAgents SDKMulti-agent
0 likes · 7 min read
Is a Lightweight Multi‑Agent Workflow Framework the Next Paradigm for AI Application Development?
AI Cyberspace
AI Cyberspace
Sep 15, 2025 · Artificial Intelligence

What Is Agentic AI? From LLM Limits to Autonomous AI Agents

Agentic AI transforms static large language models into autonomous agents by adding perception, goal orientation, planning, action, interaction, and iterative loops, tracing its evolution from early chatbots through Prompt Engineering, ReAct, AutoGPT, OpenAI Function Calling, to modern multi‑agent frameworks, while addressing challenges like memory, hallucinations, and scalability.

Agentic AIMulti-agentRAG
0 likes · 38 min read
What Is Agentic AI? From LLM Limits to Autonomous AI Agents
Programmer DD
Programmer DD
Feb 3, 2026 · Artificial Intelligence

Build Reliable AI Agent Systems: Boost Accuracy 50% While Controlling Cost & Latency

This guide explains how to construct production‑ready AI agent systems by balancing cost, latency, and accuracy, offering a decision framework, concrete techniques such as planner‑executor architecture, chain‑of‑thought prompting, verification agents, parallel agents, and file‑system state management, plus real‑world examples and impact metrics.

AI agentsLatencyProduction Systems
0 likes · 21 min read
Build Reliable AI Agent Systems: Boost Accuracy 50% While Controlling Cost & Latency
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 9, 2025 · Artificial Intelligence

Tackling Real‑World Challenges in Multi‑Agent React: From ToolCalls to Context Compression

This article analyzes production‑grade issues of a multi‑agent React framework—such as long ToolCall latency, context bloat, missing intermediate states, loop control, and supervision gaps—and presents concrete XML‑based tool‑call prompts, context‑compression techniques, summary tools, and a plug‑and‑play MCP supervisor that together improve performance, reliability, and user‑facing output quality.

AI PlanningContext CompressionReAct pattern
0 likes · 16 min read
Tackling Real‑World Challenges in Multi‑Agent React: From ToolCalls to Context Compression
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
Architect
Architect
Apr 20, 2025 · Artificial Intelligence

From Function Calling to A2A: How AI Agents Evolve and Interact

This article analyzes the progressive evolution of AI tool‑integration mechanisms—Function Calling, MCP, and A2A—explaining their core concepts, engineering considerations, use‑case suitability, limitations, and how they complement each other to enable scalable multi‑agent workflows.

A2AAI agentsFunction Calling
0 likes · 9 min read
From Function Calling to A2A: How AI Agents Evolve and Interact
dbaplus Community
dbaplus Community
Jul 6, 2025 · Artificial Intelligence

Why Build AI Agents? Benefits, Challenges, and Real-World Examples

This article explores the definition of AI agents, examines why they are essential despite challenges like latency and hallucinations, highlights their advantages such as lowered development barriers and workflow simplification, and presents real-world cases and future multi‑agent prospects.

AI agentslarge language modelsmulti-agent systems
0 likes · 25 min read
Why Build AI Agents? Benefits, Challenges, and Real-World Examples
IT Services Circle
IT Services Circle
Apr 20, 2026 · Artificial Intelligence

What Is Harness Engineering? The Missing Piece Behind Stable AI Agents

This article explains Harness Engineering—a set of six layers that turn a language model into a reliable, production‑grade AI agent—by detailing its evolution from Prompt and Context engineering, illustrating each layer with a concrete PR‑review agent example, and summarizing the practical principles and pitfalls discovered by leading AI labs such as OpenAI, Anthropic, and DeepMind.

0 likes · 47 min read
What Is Harness Engineering? The Missing Piece Behind Stable AI Agents
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
Baobao Algorithm Notes
Baobao Algorithm Notes
Dec 20, 2025 · Artificial Intelligence

How General‑Purpose Agents Are Converging on Claude Code and Deep Agent Designs

The article analyzes the 2025 shift toward a unified "general‑type" agent architecture exemplified by Claude Code and Deep Agent, detailing industry adoption, core technical features, skill‑based extensions, long‑running capabilities, and practical steps for building domain‑specific agents.

AI ArchitectureAgent SkillsClaude Code
0 likes · 25 min read
How General‑Purpose Agents Are Converging on Claude Code and Deep Agent Designs
PaperAgent
PaperAgent
Apr 12, 2026 · Artificial Intelligence

DeerFlow 2.0: Turning AI Agents into a Super‑Charged, Plug‑and‑Play Harness

ByteDance’s open‑source DeerFlow 2.0, now with over 60 k GitHub stars, provides a fully containerized, skill‑driven framework that lets large‑language‑model agents run parallel sub‑tasks, maintain long‑term memory, and manage context efficiently, reshaping how developers build autonomous AI workflows.

Agent orchestrationDeerFlowDocker sandbox
0 likes · 6 min read
DeerFlow 2.0: Turning AI Agents into a Super‑Charged, Plug‑and‑Play Harness
Architecture and Beyond
Architecture and Beyond
Oct 19, 2025 · Artificial Intelligence

4 Essential AI Agent Design Patterns You Need to Master

This article introduces four common AI Agent design patterns—single‑agent, ReAct, multi‑agent collaboration, and human‑AI cooperation—explaining their definitions, problem scopes, core components, workflows, advantages, limitations, implementation tips, and guidance for selecting the most suitable pattern.

AI agentsReActhuman‑AI collaboration
0 likes · 28 min read
4 Essential AI Agent Design Patterns You Need to Master
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