Claude’s latest update open‑sources the Skills feature as a universal standard, replacing the cumbersome Model Context Protocol (MCP) with installable “skill packages” that run on any platform—Claude web, CLI, Cursor, VS Code, and other AI agents—effectively creating an AI‑focused app store.
This guide walks through installing the OpenSkills CLI, syncing Claude Skills to AI coding agents such as Qoder, Cursor, Trae, and Windsurf, explains the underlying skill system, compares Claude Code and OpenSkills implementations, and details command‑line options and loading priorities for seamless integration.
This article introduces LangGraph 1.0 as a programming‑language‑style framework for AI agents, explains its core abstractions—State, Node, Edge, Reducer, and Human‑in‑the‑Loop—shows how to define state and node functions, builds simple and parallel graphs with static, conditional, and MapReduce edges, and demonstrates conflict‑resolution using built‑in and custom reducers.
This article surveys over 200 recent papers on AI agent memory, introducing a three‑dimensional framework of form, function, and dynamics, classifying memory into token‑level, parametric, and latent types, outlining their roles, lifecycle operations, benchmark datasets, open‑source frameworks, and seven emerging research directions.
The article argues that instead of crafting ever‑more complex AI agents, developers should treat agents as generic containers and equip them with modular “Skills”—simple folder‑based packages of scripts and documentation—that provide domain expertise, reduce context overload, and democratize AI development for non‑technical users.
This guide explains the concept of human‑in‑the‑loop (HITL) interruptions in LangGraph, outlines the core mechanisms such as persistent state and dynamic/static interrupts, and provides detailed Python examples for four classic patterns—approval/rejection, state editing, tool‑call review, and input validation—plus advanced topics like parallel interrupts and MCP‑based tool integration.
This guide explains how to build, containerise, and deploy multi‑agent AI applications using the open‑source AgentScope framework on Alibaba Cloud's ACK Pro and ACS services, covering architecture, key features, step‑by‑step deployment, sandbox usage, and testing procedures.
VS Code 1.107 introduces Agent HQ for managing multiple AI agents, a preview of TypeScript 7 with a Go‑based compiler, the removal of IntelliCode in favor of GitHub Copilot, new AI‑agent capabilities, a controversial YOLO security option, and several other enhancements aimed at boosting developer productivity.
This guide presents a curated collection of over a hundred open‑source large language model applications—including AI agents, RAG pipelines, and domain‑specific tools—explains their categories, showcases example projects, and provides step‑by‑step instructions to clone and run them on your own machine.
This article explains the challenges of traditional point‑to‑point AI agent communication, introduces the A2A protocol and its limitations, and details how combining A2A with MQTT via Tencent Cloud TDMQ creates a dynamic, loosely‑coupled, and scalable solution with practical SDK examples and real‑world case studies.
The article examines the underlying GUI‑Agent technologies behind the 2025 “Doubao” smartphone, comparing Microsoft’s UFO series, Alibaba’s Mobile‑Agent v2/v3, and ByteDance’s UI‑TARS, detailing their model foundations, input modalities, action spaces, planning mechanisms, learning strategies, open‑source status, and multi‑agent frameworks.
The article explains what AI "Skills" are, how modern AI agents like Cursor, Qcoder, and Antigravity combine abilities and tools to perform file operations, terminal commands, browser interactions, and visual tasks, and demonstrates their use through concrete frontend development scenarios.
An in‑depth review of the WEF‑Capgemini 2025 whitepaper reveals how AI agents are transitioning from simple chatbots to autonomous decision‑making partners, outlining a three‑layer architecture, new communication protocols, governance challenges, risk assessment frameworks, and practical steps for enterprises to deploy trustworthy agents.
Anthropic has transferred its Model Context Protocol to the Linux Foundation, creating the Agentic AI Foundation with co‑founders Anthropic, Block and OpenAI and backing from Google, Microsoft, AWS and others, turning MCP into a neutral, open‑source standard that lets any AI model plug into data sources like a USB‑C connector.
The article reviews three recent AI breakthroughs—GLM‑4.6V’s multimodal large‑model with 128K context and native function calling, AutoGLM 2.0’s open‑source mobile‑operating AI agent, and RealGen’s detector‑rewarded image generator that achieves a 50.15% realism win rate—highlighting how they expand toolkits for designers and developers.
The article examines the three main challenges of complex AI projects—knowledge organization, data‑AI interaction, and intent recognition—highlighting why tool (function) calling often breaks, and presents practical engineering strategies such as prompt refinement, clearer tool schemas, intent convergence, and evaluation loops to improve reliability.
The first week of the LangGraphGo project saw five version releases culminating in v0.5.0, the completion of seven full‑scale showcase replications, the launch of a groundbreaking Programmatic Tool Calling (PTC) package with up to ten‑fold performance gains, over 21,000 new lines of code, extensive bilingual documentation, and a fully deployed website and knowledge base, all backed by detailed metrics and community contributions.
This article reviews three cutting‑edge AI search and recommendation techniques—Alibaba Cloud's Agentic RAG architecture, Huawei Noah's LLM‑enhanced recommendation pipeline, and Baidu's GRAB generative ranking model—detailing their design challenges, multi‑modal retrieval strategies, performance gains, and real‑world deployment results.
DeepSeek‑V3.2 introduces a reinforced‑agent framework that combines a synthetic task factory, scaling reinforcement learning, and advanced context management, achieving the highest open‑source agent scores and narrowing the performance gap with leading closed‑source models such as Claude‑4.5‑Sonnet, GPT‑5‑High, and Gemini‑3.0‑Pro.
This article demonstrates how to use ByteDance's Trae Solo to automatically generate a FastAPI service that wraps a LangChain/LangGraph weather‑assistant agent, enabling seamless OpenAI‑style access from the Chatbox client, with step‑by‑step code, testing, and deployment details.
This article explains how the agent-web framework builds a powerful AI agent orchestration system in Go without external libraries, detailing the reasons for choosing Go, the use of go:embed for single‑binary deployment, core data structures, planning and execution logic, and the interaction handling that enables both CLI and web interfaces.
McKinsey’s latest report reveals that Gulf Cooperation Council (GCC) countries are heavily investing in AI infrastructure, achieving high adoption rates, but only a small fraction of firms have scaled AI projects to generate measurable value, highlighting gaps in strategy, talent, and change management.
This article surveys the emerging Deep Research (DR) paradigm that upgrades large language models into research agents capable of autonomous planning, multi‑source evidence gathering, memory management, and verifiable long‑form report generation, outlining its stages, core components, training pipeline, and evaluation benchmarks.
Moon Dev AI Agents is an open‑source Python framework that bundles backtesting, live trading, risk management, arbitrage and market monitoring into over 18 ready‑made AI agents, using a swarm of large language models to automate strategy development and execution for cryptocurrency traders.
This comprehensive guide explores the concept of AI agents, detailing their definitions, classifications, and core interaction loops, then walks you through building a functional LLM‑driven travel assistant with step‑by‑step code, tool integration, and practical insights on agent versus workflow paradigms.
Google’s Agent Development Kit (ADK) has added Go language support, enabling developers to build modular, high‑concurrency AI agents with code‑first workflows, integrated debugging UI, A2A protocol for agent collaboration, and a suite of pre‑built tools for tasks like Gemini search and Cloud API calls.
Anthropic’s recent release introduces three advanced capabilities—Tool Search, Programmatic Tool Calling, and Tool Use Examples—that reduce token consumption, avoid context pollution, and improve tool‑calling accuracy for AI agents, with detailed benchmarks, code samples, and guidance on when each feature is most effective.
This tutorial walks through the fundamentals of large language models, prompt engineering, and word embeddings, then shows how to set up a LangChain‑based LLM stack in Java using LangChain4j, covering core modules, memory, retrieval, chains, agents, and complete code examples.
This article examines the challenges of traditional banking data, explains how large language models can fuse structured and unstructured information, outlines a new data‑centric infrastructure and governance approach, and describes the DiFY platform’s AI‑agent and DataOps capabilities for agile, non‑intrusive integration with core banking systems.
Sierra, founded in 2023 by tech veterans Bret Taylor and Clay Bavor, has surged to a $10 billion valuation thanks to explosive ARR growth, a high‑end enterprise customer base, a hard‑mode GTM strategy, and the strategic shift to Agent OS 2.0 that aims to productize scarce AI Agent Engineer expertise and support outcome‑based pricing.
This guide walks you through creating AI‑powered test agents, defining success metrics, building evaluation datasets, crafting and refining system prompts with techniques like chain‑of‑thought, XML, few‑shot and concise inputs, and scaling the workflow by splitting agents and managing prompt versions.
DeepAgent is a new end‑to‑end reasoning agent that unifies autonomous thinking, dynamic tool search, and execution, handling over 16,000 real APIs, supporting embodied environments and research assistance, and achieving state‑of‑the‑art results across multiple benchmarks through its unified reasoning core, memory‑folding mechanisms, structured memory, and the ToolPO training framework.
This article breaks down the DeepAgent paper, explaining its novel "main model + auxiliary model" architecture, the memory‑folding mechanism that compresses long‑context reasoning, and the ToolPO reinforcement strategy that enables efficient tool discovery and usage.
A McKinsey‑QuantumBlack 2025 report reveals that while 88% of firms now use AI and AI agents are gaining traction, only a small fraction achieve enterprise‑scale deployment or measurable EBIT gains, highlighting a gap between AI hype and real business transformation.
DeepAgents, the new open‑source agent framework from LangChain, extends LangChain and LangGraph with built‑in task planning, virtual file systems, long‑term memory and sub‑agent support, and this article walks through its architecture, core capabilities, detailed code examples, and future roadmap.
This article examines the rise of low‑code platforms for AI agents, outlines their core benefits, and provides a detailed comparison of three popular solutions—Dify, n8n, and Coze—highlighting each platform's features, target users, strengths, and limitations to help readers choose the right tool for their projects.
This article explains how reinforcement learning (RL) underpins intelligent AI agents, covering the Markov Decision Process fundamentals, key RL components, multi‑hop reasoning on knowledge graphs, and a step‑by‑step LangGraph example that integrates an RL‑driven tutoring policy with Python code.
This article details how Shopify engineered the Sidekick AI agent platform, covering its evolving architecture, just‑in‑time instruction system, rigorous LLM evaluation framework, GRPO training method, and strategies to prevent reward‑hacking, offering practical guidance for building production‑ready agentic systems.
This article introduces AIO Sandbox, an all‑in‑one Docker container that unifies browser, shell, VSCode, and Jupyter environments, provides agent‑oriented APIs and multi‑language SDKs, and demonstrates a quick‑start workflow and a Python example converting web pages to Markdown.
A survey of over 30 AI‑agent founders and 40 enterprise users reveals that the biggest obstacles to AI‑agent adoption are workflow integration and human interaction (60%), employee resistance (50%) and data‑privacy concerns (50%), while successful deployments rely on modest, well‑positioned use‑cases, hands‑on support, and clear pricing models.
This report outlines emerging AI trends for the coming year, highlighting Physical AI, AI agents, multimodal models, the MCP standard, and the growing role of AI‑driven DevOps, while also discussing security implications and adoption stages across the technology lifecycle.
The article examines the hype around AI agents, explains why they currently cannot fully take over front‑end development in company projects due to fragmented context, stability demands, and long‑term architectural needs, and offers practical strategies and prompt templates for realistic, productive use.
Tencent Cloud and Tencent Research Institute, together with Gartner, released a comprehensive report that outlines evaluation methods, challenges, and practical solutions for deploying enterprise AI agents, introducing an innovative "AI Agent Scenario Compass" to help companies assess maturity and plan implementation roadmaps.
This guide dives into the four advanced capabilities of LangChain 1.0's create_agent API—MCP tool integration, structured output, memory management, and middleware—showcasing practical examples such as an Amap MCP planner, Pydantic‑based response formatting, InMemorySaver chat history, and custom middleware for dynamic model selection.
The article analyzes why ToB AI agents struggle to succeed, pinpointing two core issues: inadequate model capabilities that force temporary engineering patches, and a disconnect between technical staff who understand AI and business staff who understand domain needs.
Block, a fintech giant, deployed AI agents across all 12,000 staff in eight weeks by adopting the Model Context Protocol, simplifying installation, offering model choice, automating tool management, and building a supportive community, revealing key lessons for enterprise AI adoption.
The 2025 AI Agent Bible report reveals that voice interaction, payment infrastructure, data competition, monitoring tools, and M&A activity are reshaping the AI Agent market, highlighting lucrative coding agents, high‑valuation customer‑service agents, cost pressures, and emerging vertical opportunities for entrepreneurs.
This tutorial walks through setting up a Python environment, explains the three essential components of LangChain 1.0’s create_agent API, details the built‑in message types, and demonstrates four streaming output modes using a weather‑assistant example to help developers quickly adopt the new agent framework.
Alpha Arena’s first season pitted six leading large language models against real crypto markets with $10,000 each, revealing stark differences in trading bias, risk management, and sensitivity to prompts, as Qwen3‑Max and DeepSeek outperformed GPT‑5, while detailed case studies expose model vulnerabilities and future research directions.
Spring AI Agents brings a unified Java SDK that lets developers integrate, orchestrate, and run multiple AI coding agents—such as Claude, Gemini, and OpenAI Codex—directly within GitHub workflows, transforming development from single‑assistant Copilot models to collaborative AI team‑based architectures.
This article explains how Grab, Southeast Asia's leading super‑app, built a large‑model‑driven intelligent analysis platform that injects knowledge and restructures processes to overcome traditional risk‑control challenges, detailing its architecture, core technologies, real‑world use cases, and future outlook for AI‑augmented risk management.
This guide walks through LangChain 1.0’s three major upgrades— the new create_agent API that replaces legacy agent builders, standardized content_blocks for unified model output, and a streamlined package structure—while showing how middleware hooks, built‑in and custom middleware, and improved structured output simplify production‑grade AI agent development.
The article examines why current AI agents fall short of expectations, highlighting weak business understanding, limited execution, controllability issues, high customization costs, and the gap between model capabilities and engineering, while proposing SaaS firms' advantages, vertical scenario focus, security concerns, and future development trends.
This guide walks through Coze’s variable and long‑term memory features, showing how to define and use variables for user data, configure persistent memory, and combine both to build personalized, context‑aware AI agents, with step‑by‑step screenshots, code snippets, and real‑world scenarios.
This article shares the practical lessons learned from building Alibaba Cloud’s digital employee "YunXiaoEr Aivis", explaining why large‑language‑model agents often miss expectations and presenting ten concrete strategies—ranging from clear prompt design to memory management—that dramatically improve multi‑agent reliability.
This guide explains what LangChain toolkits are, why they simplify building domain‑specific AI agents, lists common built‑in toolkits, and walks through the step‑by‑step process of instantiating a toolkit, retrieving its tools, and creating an OpenAI‑powered agent, illustrated with a SQL database example.
Building AI agents may seem straightforward with frameworks like LangChain, but hidden complexities in orchestration, memory management, reproducibility, and scalability turn simple demos into fragile systems, requiring systematic engineering, observability, and robust design to achieve reliable, production‑grade intelligent agents.
The article reviews Alibaba Cloud's container service advancements, highlights AI-driven trends such as intelligent agents reshaping applications, the migration of AI infrastructure to cloud‑native platforms, and showcases four customer case studies demonstrating massive efficiency gains and the emergence of containers as the operating system for the AI era.
This article provides a detailed analysis of LangChain's OpenDeepResearch project, comparing its Graph workflow and Multi‑Agent architectures, explaining core node implementations, execution flows, and offering step‑by‑step instructions for configuring and deploying the system locally.
This guide curates over 500 real‑world AI agent use cases, a comprehensive student resource hub, collections of MCP servers, a Docker‑based Windows runtime, AI‑focused automation platforms, and a YAML‑driven workflow engine, each with direct GitHub links for quick exploration.
In 2025, China’s AI strategy targets over 90% adoption of AI agents, yet enterprises struggle with selection, acceptance, and optimization due to a lack of unified performance metrics, prompting the first national group standard—‘Enterprise‑level AI Agent Application Performance Evaluation Specification’—to provide a comprehensive, multi‑dimensional assessment framework for developers, users, and third‑party evaluators.
This article presents a structured, step‑by‑step learning path that guides beginners from Python fundamentals through AI API mastery, Retrieval‑Augmented Generation, deep MCP protocol knowledge, and advanced multi‑agent development, complete with practical code examples and performance‑monitoring techniques.
Microsoft CEO Satya Nadella explains how AI‑generated code will dominate, why programmers must become AI Agent Managers, and how organizations can harness AI agents to solve technical debt, transform knowledge work, and deliver real economic value across industries.
Anthropic’s recent addition of Skills to Claude introduces a modular, metadata‑driven approach that lets AI agents load specific capabilities on demand, offering a more efficient alternative to MCP’s context‑heavy tool integration and promising scalable, progressive disclosure for specialized workflows.
This article explains the fundamental differences between workflows and agents, introduces seven core design patterns—including three workflow patterns and four agent patterns—provides Python examples using Ollama, and shows how to combine these patterns to create robust, autonomous AI applications.
This article examines Tencent's large language model deployments across diverse business scenarios, detailing how Retrieval‑Augmented Generation, Supervised Fine‑Tuning, and autonomous agents boost model intelligence, reduce hallucinations, and enable sophisticated content creation, understanding, and interactive applications.
This article details the implementation of an LLM‑based code‑review agent in a C3‑level secure repository, describing its RAG‑enhanced knowledge base, CI pipeline integration, real‑world results, prompt engineering, and ongoing optimization to boost review efficiency and defect detection.
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.
This article walks through the design and implementation of a multi‑agent DeepResearch application using the Pipeline‑Agent pattern with LangGraph and LangChain, detailing three agents for task planning, web search via Tavily, and report generation, and provides complete Python code and test results.
This article distills Anthropic’s “Effective Context Engineering for AI Agents” into key insights, explaining why context engineering matters, how it differs from prompt engineering, what constitutes good practice, and practical techniques—system prompts, tool design, few‑shot prompting, compaction, structured note‑taking, and sub‑agent architectures—to mitigate context overload in large language model agents.
Claude’s new Agent Skills let developers package domain expertise into organized SKILL.md files and resource folders, enabling progressive disclosure, code execution, and scalable context loading, with detailed guidance on structure, bundling, evaluation, and security considerations for building robust, reusable AI agents.
The article examines the shift from single-model calls to autonomous AI agents, outlines the seven most influential AI agent frameworks for 2025—including LangChain, LangGraph, CrewAI, AutoGen, and Semantic Kernel—compares their core strengths, learning curves, and ideal use cases, and offers a practical selection guide for developers and enterprises.
This comprehensive AI agent development course covers 18 chapters, ranging from fundamental concepts and architecture to large‑model integration, tool and browser control, memory, RAG self‑learning, sandboxing, database manipulation, multi‑agent architectures, code assistance, and a real‑world frontend automation project, complete with source code and documentation.
This article explains the challenges of moving AI agents from demo to production, compares native framework and non‑intrusive observability solutions, and provides a step‑by‑step guide to integrate Spring AI Alibaba with LoongSuite's Java agent for OpenTelemetry‑based monitoring.
Spring AI Alibaba (SAA) provides a robust framework for building autonomous, scheduled AI agents that can operate independently, respond to events, and involve human oversight, enabling use cases such as automated business reporting, batch data processing, emergency response, and sentiment analysis, with detailed code examples and deployment guidance.
This guide walks you through the fundamentals of Claude Code agents—explaining why multiple agents are useful, how to configure them with YAML templates, manage them via slash commands, install third‑party agents, and orchestrate real‑world multi‑agent workflows—all with practical screenshots and code examples.
OpenAI will unveil Agent Builder at DevDay, a visual drag‑and‑drop canvas that lets users assemble AI agents from templates, logic nodes, connectors and tools, aiming to lower the barrier for creating production‑grade AI solutions and compete with automation platforms like n8n and Zapier.
This article explores the emerging concept of context engineering for AI agents, explains why managing long‑range context is critical, and details five practical strategies—Offload, Reduce, Retrieve, Isolate, and Cache—backed by insights from leading industry teams and the "Bitter Lesson" philosophy.
Shopify’s engineering team explains how they transformed the Sidekick AI assistant from a simple tool‑calling system into a robust, production‑grade AI agent platform, sharing architectural, evaluation and training lessons to help others avoid common pitfalls.
The article explains Google’s Test‑Time Diffusion Deep Research (TTD‑DR) paradigm, which adds iterative draft‑refinement and self‑evolution to AI agents, enabling multi‑step web retrieval, continuous “denoising” of drafts, and superior research reports compared with first‑generation Deep Research systems.
This article explains how function (tool) calling lets large language models like GPT or Gemini invoke external APIs, walks through defining tools with LangChain, and demonstrates a complete Python example that fetches real‑time weather data and returns a natural‑language answer.
This tutorial explains how LangGraph handles short‑term memory with InMemorySaver and thread‑isolated checkpoints, how to persist it using PostgresSaver, and how to build long‑term memory with InMemoryStore and PostgresStore, including semantic search and custom storage extensions.
This article explains how AutoDev adds support for Google’s Agent‑to‑Agent (A2A) protocol, detailing its architecture, integration with the Model Context Protocol (MCP), configuration steps, debugging tools, and the benefits of a modular, open‑source AI programming ecosystem.
The 2025 Enterprise AI Agent Research Report reveals that domestic Chinese agents excel in localized tasks and data precision, while international agents dominate in generalization, speed, and iterative efficiency, highlighting six critical adoption metrics and showcasing diverse industry case studies that illustrate the current AI Agent landscape and future opportunities.
A lightweight, real‑time platform called Awesome AI Agents Live, built by East China Normal University students, aggregates the latest AI Agent research, offering targeted navigation, structured classification, multi‑dimensional sorting, and concise paper summaries for researchers, engineers, and students.
This article explains how AI agents work, examines OpenManus’s comprehensive tool framework, reviews Gemini CLI’s minimalist tool scheduling and error handling, and discusses Shopify Sidekick’s scaling challenges and Just‑in‑Time instruction strategy, offering practical guidance for building robust, production‑ready agentic systems.
This tutorial shows how to use LangGraph’s low‑level API to create a multi‑turn chatbot, define structured state, add model and tool nodes, implement conditional branching for tool calls, and reproduce a ReACT graph agent with a weather‑assistant example.
This article explains how the Model Context Protocol (MCP) bridges large language models and external services by converting OpenAPI specifications into callable tools, covering generation with openapi‑generator, mapping rules, three runtime modes (stdio, streamable, SSE), and implementation details in Java, Python, and Go.
Amazon has launched the Nova Act extension, enabling developers to build and test AI agents directly within popular IDEs such as Visual Studio Code, Amazon’s Kiro, and Cursor, streamlining workflow by eliminating the need to switch between the IDE and a browser.
This article explains how RAGFlow upgrades a traditional RAG system from a passive question‑answer engine to an active problem‑solving agent by integrating the ReAct reasoning‑action‑observation loop, a visual canvas workflow, and a modular component‑tool ecosystem, with concrete Python implementations and code examples.
This article presents a curated collection of five open‑source AI‑related projects—including a comprehensive AI agent directory, a MacBook‑based digital scale, an invisible desktop assistant, a collaborative trading simulator, and Microsoft’s Magentic‑UI web helper—each with brief descriptions and GitHub links.
The article explains how Coze's Compass introduces a flexible evaluation system for AI agents, outlines a four‑dimensional submodule assessment (planning, tool use, self‑reflection, memory), and details specific testing criteria and challenges for web, scientific, dialogue, and programming agents.
This tutorial demonstrates how to use LangGraph's low‑level API to create stateful conditional‑branch, loop, and combined graphs for AI agents, showing step‑by‑step definitions of Pydantic state models, node logic, edge configuration, compilation, and test execution with concrete code examples.
This tutorial explains the design principles of LangGraph's low‑level graph API—nodes, edges, and State—demonstrates a simple addition‑subtraction workflow, shows how to visualize and execute the graph, and introduces Pydantic‑based State validation for more robust agent development.
RepoMaster is an AI‑driven framework that automatically discovers, analyzes, and executes code from massive GitHub repositories, turning them into reusable tools and achieving state‑of‑the‑art performance on challenging benchmarks while drastically reducing token consumption and engineering effort.
This survey formalizes the shift from preference‑based reinforcement fine‑tuning to Agentic Reinforcement Learning, defines Agentic RL via MDP/POMDP abstractions, proposes a dual taxonomy of capabilities and task domains, compiles over 500 recent works, and outlines open challenges for scalable, robust AI agents.
This article examines Tencent's large language model deployments across various business scenarios, detailing the use of Retrieval‑Augmented Generation, GraphRAG for role‑playing, and Agent technologies, while also outlining core application areas and the three main technical approaches—SFT, RAG, and Agents.
Industry leaders discuss how AI is swallowing traditional software, the technical hurdles of high‑quality data, hallucination suppression, and medical ethics, while highlighting AI glasses, voice agents, and user‑retention metrics as key growth drivers for Chinese entrepreneurs in the coming year.
This article examines the context isolation strategies employed by Gemini CLI and Claude Code in AI agents, detailing why isolation is essential, the multi‑layer memory architecture, tool execution pipelines, concurrency controls, session management, and practical recommendations for building robust, cost‑effective agent systems.
At the 2025 Inclusion·外滩大会 forum, leading academics and industry pioneers discussed rapid advances in AI agents, highlighting breakthroughs in multi‑agent systems, reinforcement learning, open‑source frameworks, and the practical challenges of cost, performance, and usability that still separate "usable" from truly "useful" technology.
