This article explains the concept of AI agents, contrasting them with chatbots, detailing their ability and structural layers, summarizing academic surveys and whitepapers, and illustrating how agents plan, perceive, and act to autonomously accomplish user‑defined goals.
JoyCode Agent, an AI‑driven multi‑agent system, secured a 74.6% pass@1 rate on the SWE‑bench Verified benchmark, placing it in the global Top‑3 while cutting computational resource usage by 30‑50% through a novel patch‑test co‑generation and iterative verification pipeline.
This article explores the rise of multimodal agents that integrate language, vision, and action, detailing their core architecture, model fusion strategies, decision chain, and a practical Python implementation using GPT‑4o‑mini and BLIP, while also discussing future extensions such as reinforcement learning and robotic control.
This article surveys the evolution of GUI automation from rule‑based scripts to multimodal large‑model‑driven agents, detailing core architectures, key components, application scenarios, current challenges, and future research directions for intelligent GUI agents.
This article provides a comprehensive guide to AI Agent planning modules, covering their core responsibilities, architectural designs, major planning paradigms such as ReAct, Plan‑and‑Execute, Hierarchical Planning and Reflexion, detailed prompt engineering, execution frameworks, and practical case studies in data analysis and intelligent customer service.
The talk outlines how Alibaba Cloud’s Aivis autonomous service agent tackles the “impossible triangle” of ultra‑high experience, low cost, and complex services by evolving from tool‑based chatbots to teammate‑level agents, detailing a four‑layer architecture, domain‑model training, and actionable steps for enterprise AI service transformation.
In this talk, Volcano Engine’s technical expert Chen Shuo explains the evolution of the Data Agent platform, the four‑quadrant framework for AI‑driven analytics, real‑world deployment challenges, architectural upgrades from pipeline to intelligent scheduling, and key lessons for building reliable, enterprise‑grade AI agents.
This article systematically presents Tencent PGC's Text‑to‑SQL research, detailing the DEA‑SQL framework, its agent‑based architecture, extensive experiments on benchmark datasets, and real‑world deployment in the OlaChat intelligent BI product, highlighting performance gains and practical capabilities.
This article explains how Xiaomi designed and deployed a vertical‑domain question‑answering assistant for product and car queries, covering business background, a four‑module RAG‑plus‑LLM architecture, knowledge‑base construction, custom chunking strategies, dynamic signal handling, and the challenges overcome to achieve reliable real‑time voice interactions.
This article details the redesign of a code coverage platform, introducing a Kafka‑centric real‑time data stream, language‑agnostic contracts, non‑intrusive agents for Java and PHP, fine‑grained multi‑dimensional analysis, and interactive visualizations that dramatically improve testing efficiency and coverage accuracy.
This article provides a comprehensive overview of AI Agents, covering their fundamental concepts, core features, technical evolution, work cycle, architectural modules, key technologies such as prompt engineering and RAG, practical development steps, a data‑analysis agent case study, and typical industry applications.
The article analyzes the emerging AI browser war, explaining why Perplexity sees building its own native browser Comet as essential for survival, detailing its strategic rationale, workflow‑centric business model, technical architecture, and the risks of relying on rival AI models.
An in‑depth guide outlines the nine core technologies—ranging from autonomous agent fundamentals and multi‑agent collaboration to workflow orchestration, retrieval‑augmented generation, fine‑tuning, function calling, model context protocols, agent‑to‑agent communication, and AI‑driven UI—required to design, deploy, and scale enterprise‑grade AI agents.
The article reverse‑engineers Cursor’s Agent mode, explains its system prompt, enumerates the 16 built‑in tools, draws insights about the importance of model choice and tool usage, and outlines the components needed to build a similar open‑source coding assistant.
The article explains the Generator‑Verifier Gap (GVG)—the asymmetry where verifying a solution is far cheaper than generating it—covers its origin, its impact on test‑time scaling for large language models, reinforcement‑learning approaches, and how the concept can shape agent architectures and AI product strategy.
This article explores how the rapid rise of large language models and techniques like Retrieval‑Augmented Generation, Chain‑of‑Thought prompting, and multi‑agent architectures can dramatically improve the speed, accuracy, and automation of fault localization in modern operations environments.
This article examines how the rise of large language models reshapes core business assets, defines agents and tools, explores multi‑agent collaboration patterns, task allocation and conflict resolution mechanisms, and evaluates the MCP protocol and engineering requirements for building scalable, flexible agent platforms.
This article examines five major LLM‑driven AI agent planning and execution patterns—ReAct, Plan‑and‑Execute, Static Workflow, Static Workflow with local intelligence, and modular hierarchical planning—detailing their mechanisms, code examples, strengths, weaknesses, suitable scenarios, and optimization techniques.
This article outlines Anthropic's yearly insights on constructing large‑language‑model agents, explaining their definitions, when to employ them, recommended frameworks, modular building blocks, common workflow patterns, and real‑world application scenarios for developers.
Amid the AI wave, large language models like DeepSeek R1 are poised to explode by 2025, driven by open-source, low-cost access and superior reasoning, with successful deployment requiring four key factors—domain expertise, knowledge bases, robust search, and engineered agent architectures—to unlock value beyond simple chat.
This tutorial explains AI agent architectures, compares reactive, deliberative, hybrid, neural‑symbolic and cognitive designs, shows their trade‑offs, provides Python code examples for each, and links these patterns to LangGraph design templates for building scalable intelligent systems.
The article discusses how defining clear problem boundaries is essential for AI agents, explains the challenges of vague tasks for large language models, and proposes multi‑stage decomposition, self‑reflection, and human‑in‑the‑loop strategies to improve AI performance on complex, dynamic tasks.
The article compiles insights from the AIR 2025 conference and related talks, covering the evolution of agents from reinforcement‑learning to LLM‑driven systems, novel agent architectures like AIDE, GUI agents, natural‑language reinforcement learning, and scaling advances in large language models such as Qwen, while highlighting key algorithms, benchmarks, and open research questions.
This article traces the evolution of large‑model agents from a simple chain‑of‑thought design through tool and agent instantiation, structured PEER patterns, and self‑evolving architectures, highlighting practical challenges, middleware solutions, and open‑source resources for building robust AI agents.
OpenManus is an open‑source, plug‑in‑friendly multi‑agent framework that combines planning, tool‑driven ReAct agents, dynamic task allocation, and memory management, detailing its design principles, code structure, workflow, technical components, and future research directions within the AI agent ecosystem.
This article provides a comprehensive analysis of AI agents, covering their historical origins, three‑layer technology stack, market size forecasts, evolution from training to inference, interaction modes, core modules, and the full industry chain from infrastructure providers to downstream applications.
The Model Context Protocol (MCP) introduced by Anthropic provides a standardized, TCP/IP‑like communication layer that unifies resources, tools, and prompts, enabling seamless integration of large language model agents with external systems, reducing fragmentation, and accelerating AI agent development.
This article explains the evolution of multi‑agent systems for e‑commerce assistants, detailing the ReAct‑based planning framework, hierarchical master‑sub agent collaboration, evaluation methods, and sample‑generation techniques that together improve accuracy, efficiency, and scalability of AI‑driven merchant services.
This article details Akulaku’s deployment of large language models across multimodal financial risk‑control scenarios—covering business background, a three‑module intelligent‑agent architecture, concrete tool‑ and planning‑enhancement case studies, and future outlook—demonstrating how LLMs boost efficiency, reduce labeling effort, and enable copilot‑style assistance.
This article explores how large‑model technologies can address data analysis challenges by introducing an Agent‑based architecture integrated with a semantic layer, detailing design principles, optimization paths, technical implementation, real‑world retail case studies, product design considerations, and future directions for intelligent analytics.
This presentation outlines the development of ChatGPT, the underlying principles of large language models, and how they empower new industrialization in automotive manufacturing, detailing practical implementations, agent architectures, data and model closed loops, and case studies such as intelligent inspection and G8D agents.
This article surveys the design of autonomous large‑language‑model agents, detailing their modular architecture—including profiling, memory, planning, and execution—while also reviewing common profiling methods, memory structures, planning techniques, action strategies, and various learning approaches such as exemplar, human‑in‑the‑loop, and environment‑feedback training.
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.
This article demystifies large language models by comparing them to classic compression algorithms, explains how they compress massive data into compact parameters, explores their ability to learn abstract patterns, and provides practical insights into prompt engineering, sampling strategies, and multi‑step agent architectures for real‑world applications.
This guide walks through the complete workflow of creating an AI‑native 24‑point game using Baidu Cloud's AppBuilder, covering the three‑step methodology, Agent architecture, component design, custom workflow implementation, and practical tips for optimal model selection.
This article walks through the complete workflow for creating an AI‑native conversational 24‑point game using Baidu Cloud Qianfan AppBuilder, covering methodology, component design, agent architecture, and implementation details with code examples and visual diagrams.
This article explores JD Retail's AI‑driven multi‑agent system that mimics real‑world merchant decision processes, detailing the ReAct paradigm, agent roles, workflow, training methods, monitoring, and future directions for building intelligent e‑commerce assistants.
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.
This article surveys the emerging field of large‑language‑model (LLM) based agents, detailing their modular architecture—including profiling, memory, planning, and action components—while discussing critical challenges such as role‑playing, memory design, reasoning, multi‑agent collaboration, and outlining promising research directions and practical case studies.
The article surveys modern AI agents—software entities powered by large language models that perceive multimodal inputs, reason via brain modules, act through tools or embodied actions, employ retrieval‑augmented generation and chain‑of‑thought planning, and can operate singly (e.g., AutoGPT) or collaboratively via frameworks like Microsoft’s AutoGen—while highlighting current challenges such as controllability, memory limits, parallelism, and reliability.
