This article explains why large‑language‑model agents become hard to manage, outlines the benefits of modular multi‑agent designs, compares several connection architectures, and provides concrete LangGraph code for supervisor‑based, tool‑calling, and custom workflow patterns.
After a client demo revealed that using Slack chats to coordinate three AI agents cannot scale to dozens, the author argues that instant‑messaging is only a gateway, proposes a four‑layer ICSE architecture (Intent‑Control‑Service‑Event), outlines governance policies, and maps new product opportunities for PMs in the multi‑agent era.
This article introduces the concept of multi‑agent systems, compares five popular orchestration frameworks, and provides a step‑by‑step tutorial for building and testing a simple supervision‑based workflow using OpenAI's experimental Swam library, complete with code snippets and performance observations.
This article explains why single‑agent LLM pipelines fail when many tools are attached, introduces the Supervisor pattern that separates routing and execution across specialized agents, compares Tool‑Calling and Handoff approaches, provides a complete TypeScript implementation—including hierarchical supervisors—and lists five common pitfalls with concrete fixes.
This article clarifies the concepts of AI Agent and Agentic AI, compares their core definitions, architectures, autonomy, and application scenarios, and uses analogies to illustrate how they complement each other in the evolution from single-task automation to collaborative multi‑agent intelligence.
The article analyzes how AI agent architecture choices—single‑agent versus multi‑agent, ReAct, plan‑and‑execute, orchestrator‑worker, hierarchical teams, reflection, and HITL—affect cost, reliability, and scalability, providing quantitative trade‑offs and industry examples to guide workload‑specific selection.
This article explains how the authors built a multi‑agent framework for the energy domain that splits complex tasks into simple subtasks, uses a planner‑scheduler‑executor pipeline, implements structured communication, memory management, and streaming output to overcome large‑model attention diffusion and improve efficiency and reliability.
This article analyses why a single‑agent LLM approach quickly hits scalability, context, and governance limits, and presents a production‑ready Spring AI Multi‑Agent design—including layered architecture, agent metadata, skill engineering, routing strategies, orchestration, resilience, A2A service discovery, Kubernetes deployment, observability, security, and cost‑control—backed by concrete Java code examples.
This article breaks down Anthropic's four‑layer AI Agent architecture, explains when multi‑Agent setups are worthwhile, details how to design reusable Skills and a standardized MCP connection protocol, and provides a practical checklist and a ready‑to‑use Skill template for immediate implementation.
The article explores Ant Group’s agentUniverse team’s experience applying multi‑agent technology in finance, covering background on large language models, the agentUniverse framework, real‑world implementations, and the advantages of coordinated multi‑agent collaboration for complex analytical and decision‑making tasks.
The article analyses why using instant‑messaging groups to orchestrate multiple AI agents cannot scale to dozens or hundreds of agents, proposes a four‑layer ICSE architecture, compares three agent‑to‑agent communication models, and outlines the new governance, design, and roadmap responsibilities that product managers will need to master.
The article outlines a five‑layer AI multi‑agent architecture covering hierarchical command chains, human‑in‑the‑loop security barriers, decentralized peer‑to‑peer networks, industrial‑grade pipeline processing, and data‑transformation alchemy, each illustrated with concrete enterprise and autonomous‑driving examples.
The article explains that multi-agent systems typically operate in three patterns—sequential execution, parallel execution, and an evaluator-optimizer loop—covers when each pattern is appropriate, and offers interview tips on how to discuss these designs effectively.
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.
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.
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.
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.
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.
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.
The article analyzes three core workflow patterns for AI agents—sequential, parallel, and evaluation‑optimized—explaining their mechanics, trade‑offs in latency, token cost, and reliability, and offering concrete guidance on when to adopt each pattern or combine them.
