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multi-agent

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Qunar Tech Salon
Qunar Tech Salon
Jun 5, 2025 · Artificial Intelligence

Unlocking OpenAI Agents SDK: Core Features, Code Samples, and Framework Comparisons

This article introduces the OpenAI Agents SDK, explains its key capabilities such as Agent Loop, Handoffs, Guardrails, and Tracing, provides practical Python code examples, compares it with other multi‑agent frameworks, and discusses best practices for building reliable AI applications.

AI agentsAgents SDKGuardrails
0 likes · 17 min read
Unlocking OpenAI Agents SDK: Core Features, Code Samples, and Framework Comparisons
Tencent Technical Engineering
Tencent Technical Engineering
May 23, 2025 · Artificial Intelligence

The Evolution, Challenges, and Future Directions of AI Agents

An in‑depth overview traces the development of AI agents from early LLM milestones to modern “class‑Agent” models, examines core components such as memory, tool use, planning and reflection, analyzes current limitations, and outlines emerging solutions like workflows, multi‑agent systems, and model‑as‑product paradigms.

AI AgentAgentic WorkflowFunction Call
0 likes · 40 min read
The Evolution, Challenges, and Future Directions of AI Agents
Architect
Architect
Apr 22, 2025 · Artificial Intelligence

A2A and MCP Protocols: Complementary Architectures for AI Agent Collaboration

This article explains the design principles, core components, and workflows of Google’s A2A (Agent‑to‑Agent) protocol and Anthropic’s MCP (Model Context Protocol), shows how they complement each other in multi‑agent AI systems, and discusses future directions for these standards.

A2AAI agentsMCP
0 likes · 11 min read
A2A and MCP Protocols: Complementary Architectures for AI Agent Collaboration
JD Tech Talk
JD Tech Talk
Mar 24, 2025 · Artificial Intelligence

MaRCA: Multi‑Agent Reinforcement Learning Computation Allocation for Full‑Chain Ad Serving

This article presents MaRCA, a multi‑agent reinforcement learning framework that allocates computation resources across the full ad‑serving chain by modeling user value, compute consumption, and action rewards, enabling fine‑grained power‑tilting toward high‑quality traffic and achieving significant business gains under strict latency constraints.

AI optimizationLoad Balancingad serving
0 likes · 16 min read
MaRCA: Multi‑Agent Reinforcement Learning Computation Allocation for Full‑Chain Ad Serving
JD Retail Technology
JD Retail Technology
Mar 18, 2025 · Artificial Intelligence

Multi‑Agent Reinforcement Learning Based Full‑Chain Computation Allocation (MaRCA) for Advertising Systems

MaRCA, a multi‑agent reinforcement‑learning framework, allocates compute across JD’s advertising playback chain by jointly estimating user value, resource consumption, and action outcomes while dynamically adjusting to real‑time load, achieving roughly 15 % higher ad revenue without extra compute resources.

advertisingcompute schedulingdeep learning
0 likes · 18 min read
Multi‑Agent Reinforcement Learning Based Full‑Chain Computation Allocation (MaRCA) for Advertising Systems
JD Tech Talk
JD Tech Talk
Feb 20, 2025 · Artificial Intelligence

Multi‑Agent Architecture for an E‑Commerce Business Assistant: Design, Planning, Evaluation, and Sample Generation

The document describes the evolution, design principles, key technologies, online inference workflow, evaluation methods, and sample‑generation techniques of a large‑language‑model‑based multi‑agent system that powers a 24/7 e‑commerce merchant assistant, highlighting its benefits, challenges, and future work.

AI planningLLMOnline Inference
0 likes · 21 min read
Multi‑Agent Architecture for an E‑Commerce Business Assistant: Design, Planning, Evaluation, and Sample Generation
JD Tech
JD Tech
Feb 14, 2025 · Artificial Intelligence

JD Merchant Intelligent Assistant – Multi‑Agent System Architecture, Planning, and Evaluation

JD’s Merchant Intelligent Assistant leverages a large‑language‑model‑based multi‑agent architecture to provide 24/7 e‑commerce support, detailing its evolution, planning techniques, online inference, evaluation methods, sample generation, and practical insights for scalable AI‑driven operations.

AutomationE-commerce AILLM
0 likes · 22 min read
JD Merchant Intelligent Assistant – Multi‑Agent System Architecture, Planning, and Evaluation
JD Retail Technology
JD Retail Technology
Feb 10, 2025 · Artificial Intelligence

JD Merchant Intelligent Assistant: Multi‑Agent Architecture and Technical Exploration

The JD Merchant Intelligent Assistant employs a large‑language‑model‑driven multi‑agent architecture with dynamic ReAct planning, enabling merchants to query and execute store operations in under a second with over 90 % decision accuracy, while reducing inference cost, hallucinations, and engineering effort across diverse e‑commerce tasks.

AILLMe-commerce
0 likes · 25 min read
JD Merchant Intelligent Assistant: Multi‑Agent Architecture and Technical Exploration
Alimama Tech
Alimama Tech
Dec 11, 2024 · Artificial Intelligence

Engineering Architecture of Alibaba's AI Digital Employee "AI XiaoWan"

Alibaba’s AI digital employee “AI XiaoWan” uses a native multi‑agent architecture where a Controller Agent interprets intent, plans tasks, and orchestrates execution while an Executable Agent performs domain‑specific operations, communicating via a standardized Agent Communication Protocol, leveraging a centralized Tool Center, a retrieval‑augmented knowledge base, and a data‑flywheel feedback loop to continuously improve and evolve toward memory‑based reasoning and self‑learning.

AIKnowledge BaseRAG
0 likes · 14 min read
Engineering Architecture of Alibaba's AI Digital Employee "AI XiaoWan"
DataFunSummit
DataFunSummit
Nov 14, 2024 · Artificial Intelligence

Building Multi‑Scenario Personal and Office AI Assistants with Large Models at Huolala

Huolala leverages large language models to create a suite of AI assistants—ranging from professional troubleshooting bots to multimodal insurance quoting tools—across more than 14 logistics scenarios, detailing platform architecture, prompt engineering, multi‑agent coordination, and future AI‑driven business empowerment.

AI assistantsLLM platformbusiness automation
0 likes · 13 min read
Building Multi‑Scenario Personal and Office AI Assistants with Large Models at Huolala
DataFunSummit
DataFunSummit
Sep 19, 2024 · Artificial Intelligence

AI-Powered Anomaly Diagnosis and Root Cause Analysis for Gaming Business Intelligence

This article presents 37 Mobile Games' exploration of AI-driven intelligent analysis, covering abnormal diagnosis, root‑cause analysis, QBI fluctuation insights, AI data analysis reports, and a multi‑agent workflow for generating analytical reports within a gaming BI platform.

AIAnomaly DetectionBusiness Intelligence
0 likes · 12 min read
AI-Powered Anomaly Diagnosis and Root Cause Analysis for Gaming Business Intelligence
DataFunSummit
DataFunSummit
Aug 25, 2024 · Artificial Intelligence

Applying Large AI Models to Financial Data Governance and Innovative Use Cases

This article presents a comprehensive technical overview of how large AI models are reshaping financial data production, governance, multimodal document understanding, lakehouse storage, private‑domain model deployment, data‑centric engineering methods, and multi‑agent intelligent advisory within the finance sector.

AILarge ModelsRAG
0 likes · 21 min read
Applying Large AI Models to Financial Data Governance and Innovative Use Cases
DataFunSummit
DataFunSummit
Jul 30, 2024 · Artificial Intelligence

Multimodal Mobile AI Agent (Mobile‑Agent): From V1 to V2 and Open‑Source Practice

This article introduces Alibaba Tongyi Lab's multimodal mobile AI agent, Mobile‑Agent, covering the background of large‑model agents, the design and capabilities of V1 and V2, the multi‑agent framework, evaluation results, open‑source resources, and future development directions.

AI planninglarge language modelmobile agent
0 likes · 13 min read
Multimodal Mobile AI Agent (Mobile‑Agent): From V1 to V2 and Open‑Source Practice
DataFunSummit
DataFunSummit
Jul 27, 2024 · Artificial Intelligence

Introducing ModelScope-Agent: An Open‑Source Multi‑Modal Multi‑Agent System

This article presents ModelScope‑Agent, an open‑source multi‑modal multi‑agent framework built on the ModelScope community, explains its underlying agent concepts, outlines its architecture and key features, showcases several real‑world applications such as ModelScope GPT, Story‑Agent and Facechain‑Agent, and includes a detailed Q&A on future directions and challenges.

Artificial IntelligenceModelScope-AgentTool Calling
0 likes · 15 min read
Introducing ModelScope-Agent: An Open‑Source Multi‑Modal Multi‑Agent System
DataFunSummit
DataFunSummit
Jul 17, 2024 · Artificial Intelligence

Overview of LLM‑Based Agents: Architecture, Key Challenges, and Future Directions

This article reviews the emerging field of large‑language‑model (LLM) based AI agents, outlining their overall architecture, core modules such as profiling, memory, planning and action, discussing current challenges, presenting concrete use‑cases, and highlighting promising research directions.

AI AgentLLMMemory Mechanism
0 likes · 11 min read
Overview of LLM‑Based Agents: Architecture, Key Challenges, and Future Directions
DataFunSummit
DataFunSummit
Jun 27, 2024 · Artificial Intelligence

Advances in Code Large Models: AIGC Impact on Software Development and Future Multi‑Agent Tools

This article explores how code large models and AIGC are transforming software development, covering their impact on developer skills, collaboration, cost control, the evolution of Copilot and Agent modes, multi‑agent architectures, retrieval‑augmented generation, and future directions for intelligent development tools.

AIGCCopilotcode generation
0 likes · 23 min read
Advances in Code Large Models: AIGC Impact on Software Development and Future Multi‑Agent Tools
DataFunSummit
DataFunSummit
Jun 16, 2024 · Artificial Intelligence

Reinforcement Learning in Recommendation Systems: Practice, Challenges, and Industry Advances

This article presents a comprehensive overview of applying reinforcement learning to recommendation systems, covering background challenges, practical exploration, frontier research directions, multi‑agent and inverse RL approaches, evaluation methods, and future outlooks, based on a KDD‑published study and industry experience.

Inverse RLOffline RLRecommendation systems
0 likes · 24 min read
Reinforcement Learning in Recommendation Systems: Practice, Challenges, and Industry Advances
JD Tech
JD Tech
May 22, 2024 · Artificial Intelligence

AI Multi‑Agent System for E‑commerce Merchant Assistance: Design, ReAct Architecture, and Implementation

The article describes JD Retail's AI‑driven multi‑agent platform that models real‑world merchant decision‑making with ReAct‑based LLM agents, detailing the system architecture, agent roles, reasoning loops, workflow examples, training pipelines, monitoring, and future directions for e‑commerce support.

AIDecision SupportLLM
0 likes · 21 min read
AI Multi‑Agent System for E‑commerce Merchant Assistance: Design, ReAct Architecture, and Implementation
JD Retail Technology
JD Retail Technology
May 22, 2024 · Artificial Intelligence

AI Multi‑Agent System for E‑commerce: Design, Implementation, and Operational Insights

This article presents a comprehensive overview of JD Retail's AI‑driven multi‑agent architecture for e‑commerce assistance, detailing how real‑world merchant decision processes are modeled with ReAct‑based LLM agents, the hierarchical workflow, training pipelines, monitoring mechanisms, and future directions for scalable intelligent commerce support.

AILLMagent architecture
0 likes · 20 min read
AI Multi‑Agent System for E‑commerce: Design, Implementation, and Operational Insights
Efficient Ops
Efficient Ops
May 14, 2024 · Artificial Intelligence

How Large‑Model Agents Are Revolutionizing AIOps and Modern Operations

This article explores why large‑model Agent technology is essential for AIOps, explains single‑ and multi‑Agent architectures, memory and tool integration, and demonstrates practical applications such as anomaly detection, fault diagnosis, automated remediation, ChatOps, and future directions for intelligent, autonomous operations.

AI agentsAIOpsLLM
0 likes · 14 min read
How Large‑Model Agents Are Revolutionizing AIOps and Modern Operations