Core Insights - The AI market in China is transitioning from a hype phase to a more rational phase following the "hundred model battle," with generative AI and agent-based AI being the two main themes shaping the current trends [2][4]. Market Trends - The report by Gartner indicates that the previously dominant large language models (LLMs) have peaked and are now entering a phase of declining interest, moving towards a "bubble burst" low point [2]. - By 2027, companies in China that prioritize AI-ready data over generative AI model development are expected to achieve business value that is twice that of their peers [4]. Technology Development - The market response to GPT-5 has been lukewarm, indicating a critical turning point in the development of large language models, as their capabilities appear to have reached a ceiling [5]. - The competition among AI models has intensified, with domestic models like DeepSeek and Qianwen entering the first tier, but the performance differences among top models are minimal [5]. Future Directions - Gartner emphasizes that future AI systems will require a combination of various technologies rather than relying solely on large language models [6]. - The deployment of generative AI in production environments is expected to surge from 8% in 2024 to 40% in 2025, with current estimates suggesting it may have already reached 60% to 70% [6]. Challenges in Traditional Enterprises - Traditional enterprises face significant challenges in AI application, particularly in digital transformation, which can take years to implement [7]. - Internet and high-tech companies are likely to progress faster due to better system architecture and data management practices [7]. Industry Phenomena - There is a prevalent issue of "Agent Washing," where many products falsely claim to be AI agents while remaining basic chatbots [8]. - The evolution of AI agents has gone through three stages: chatbots, assistants, and now AI agents, with many current products still not qualifying as true AI agents [8]. Evaluation Criteria - According to Gartner, true AI agents must possess three key elements: perception of the world, autonomous decision-making, and execution of actions [9]. - Many so-called AI agents still rely on fixed workflows for reliability, indicating a lack of true intelligence [9].

2025年6月，Shopify CEO Tobi Lütke 和 AI 大神 Andrej Karpathy 在 X 上提出了一个新概念——上下文工程。Karpathy 将其定义为"一门微 妙的艺术与科学，旨在填入恰到好处的信息，为下一步推理做准备。" 本文将以这篇论文为基础，系统性地回答三个核心问题：上下文工程到底是什么？它的基础构件是什么？未来会如何发展？ 01 上下文工程是什么？一门关于熵减的古老学科 要理解上下文工程，必须先回答：为什么人与机器的交流如此困难？ 然而，这个新概念与提示词工程有什么不同？为什么它会和 RAG、MCP 等技术扯上关系？过往的回答大多从技术角度出发，试图拆解 上下文都包括什么，如何让它能够发挥最好的效果。 10月30日，上海交通大学和 GAIR 实验室发表了论文《上下文工程 2.0：上下文工程的上下文》，用一种更全面的视角定义了这个新兴学 科。它不再把人机交互视为技巧，而是回归到了交流动力学的基础逻辑。 论文认为，这是因为人类与机器之间，存在一道认知鸿沟。 人类的交流是高熵的，他们的表达无序、混乱、充满隐含信息。当我对同事说"帮我搞定那个报告"，他需要记忆中的"那个报告"指什 ...

Connecting AI models to different apps usually means writing custom code for each one.For instance, if you want to use a model in a Slack bot or in a dashboard, you'd typically need to write separate integration code for each app.Let's learn how to simplify this via MCPs.We’ll use @LightningAI's LitServe, a popular open-source serving engine for AI models built on FastAPI.It integrates MCP via a dedicated /mcp endpoint.This means that any AI model, RAG, or agent can be deployed as an MCP server, accessible ...

Core Concepts - A2A (Agent2Agent) enables AI agents to collaborate without sharing internal data, thoughts, or tools [1][2] - MCP provides agents with access to tools, while A2A facilitates agent-to-agent communication and teamwork [1] - A2A agents can be modeled as MCP resources using AgentCards [2] Functionality and Benefits - A2A supports secure collaboration, task and state management, UX negotiation, and capability discovery [3] - A2A allows agents from different frameworks to work together [3] - Remote Agents supporting A2A must publish a JSON Agent Card detailing their capabilities and authentication [2] Industry Implications - Standardizing Agent-to-Agent collaboration is beneficial, similar to MCP's role in Agent-to-tool interaction [3] - Clients can use Agent Cards to find the best agent for a task [3]

AI Engineering Projects - The industry highlights 9 real-world MCP (presumably Machine Comprehension and Planning) projects for AI engineers [1] - These projects are accessible via a GitHub repository [1] Project Types - The projects cover areas like RAG (Retrieval-Augmented Generation), Memory, MCP client, Voice Agent, and Agentic RAG [1] - The "and much more!" suggests the repository contains additional project types beyond those explicitly listed [1]

Summary of Conference Call Records Industry Overview - The conference call focuses on the AI model landscape in China, highlighting the challenges and advancements in the domestic AI industry compared to international counterparts [1][2][4][5]. Key Points and Arguments 1. **Architecture and Innovation** - Domestic AI models heavily rely on overseas architectures like Transformer and MoE, leading to difficulties in surpassing foreign models [1][2]. - There is a lack of self-developed, breakthrough architectural innovations in China, which hampers competitiveness [2]. 2. **Computational Power** - Chinese AI companies have significantly lower GPU computational power compared to international giants like Microsoft, Google, and Meta, often by an order of magnitude [2]. - The ongoing US-China trade war has restricted resource availability, further impacting computational capabilities [1][2]. 3. **Cost and Performance Focus** - Domestic models prioritize inference cost and cost-effectiveness, aligning with local consumer habits, while international models like GPT focus on top-tier performance [1][2]. - The commercial model differences create a substantial gap in model capabilities [2]. 4. **Data Acquisition** - The relatively lenient data laws in China provide an advantage in data acquisition for training models, unlike the stringent regulations in Europe and the US [3]. 5. **Open Source Strategies** - Alibaba adopts a nearly fully open-source strategy, including model weights, code, and training data, to enhance influence and integrate its cloud services [4]. - Other companies like ByteDance and Kuaishou are more selective in their open-source approaches due to their reliance on proprietary technology [4]. 6. **Multimodal Model Developments** - Domestic companies are making strides in multimodal models, focusing on applications in e-commerce and short videos, which cater to local needs [5][6][7]. - Companies like Alibaba, Kuaishou, Tencent, and ByteDance are developing models that integrate text, image, audio, and video generation [7][8]. 7. **MoE Architecture Adoption** - The MoE architecture is becoming standard among major companies, allowing for reduced computational costs and inference times [10]. - Future optimization directions include precise input allocation, differentiated expert system structures, and improved training stability [10][11]. 8. **Economic Viability of Large Models** - Starting mid-2024, pricing for APIs and consumer services is expected to decrease due to the release of previously constrained GPU resources [13]. - The overall cost conversion rate in the large model industry is increasing, despite initial low profit margins [13][14]. 9. **Competitive Differentiation** - Key competitive differences among leading domestic firms will emerge from their unique strategies in technology iteration, data accumulation, and business models [15]. 10. **Future Trends and Innovations** - The focus will shift towards agent systems that integrate user understanding and tool invocation, enhancing overall efficiency [16]. - The MCP concept will gain traction, addressing data input-output connections and reducing integration costs [22]. Additional Important Insights - The acceptance of paid services among domestic users is low, with conversion rates around 3% to 5%, indicating a need for improved user experience to enhance willingness to pay [20][21]. - Successful AI product cases include interactive systems that combine companionship with professional analysis, indicating a potential path for monetization [22]. This summary encapsulates the critical insights from the conference call, providing a comprehensive overview of the current state and future directions of the AI industry in China.

ICYMI: Google revealed its “Agent Payments Protocol” (AP2)This is an open standard that lets AI agents make payments across platforms/crypto rails.The interesting point is who’s involved from a crypto standpoint:1) MetaMask: Redeeming moment for lack of token and poor UI/UX? Maybe they’re integrating agents into their wallet to enhance the UX?2) @ethereum: Literally highlighted their intent to embrace AI this week.3) @coinbase: “Works with cards, bank transfers, local methods, and crypto (via x402)” - x402 ...

Investment Rating - The report maintains an "Outperform" rating for the AI industry [1] Core Insights - AI Agents represent a significant evolution in AI technology, moving beyond simple command execution to autonomous decision-making and task execution, achieving performance levels equivalent to 90% of skilled adults [3][10] - The AI infrastructure is undergoing a transformation, with major cloud providers like Microsoft, Google, and Amazon enhancing their AI/Agent platforms to capture new market opportunities [3][51] - The global AI IT spending is projected to grow at a CAGR of 22.3% from 2023 to 2028, with Generative AI (GenAI) expected to account for 73.5% of this growth [3] Summary by Sections 01 Agent Definition, Technology, and Development - AI Agents are defined as intelligent entities with autonomy, planning, and execution capabilities, surpassing traditional automation [10] - Key features include autonomous decision-making, dynamic learning, and cross-system collaboration [10] 02 Agent Development Platform Layout - Major players in the AI Agent development space include Microsoft, Google, Amazon, Alibaba, and Tencent, each with distinct strategies and market focuses [3][51] 03 Model Layer and Tokens Usage Analysis - The report highlights the rapid increase in token usage, with Google's Gemini model projected to reach 980 trillion tokens by July 2025, a 100-fold increase from the previous year [3] - Domestic models like Byte's Doubao are also seeing significant growth, with daily token usage expected to reach 16.4 trillion by May 2025, a 137-fold increase [3] 04 C-end and B-end Agent Progress - C-end applications are heavily reliant on model capabilities, with significant growth in image and programming-related products [3] - B-end applications, such as Microsoft's Copilot, have over 100 million monthly active users, but face challenges related to data security and cost [3] Agent Market Size and Development Expectations - The AI Agent market is expected to reach $103.6 billion by 2032, growing at a CAGR of 44.9% [3] - The report anticipates that by 2035, AI Agents will become mainstream as cognitive companions for humans [3]

To sum up:- Tool Calling helps an LLM decide what to do.- MCP is an infrastructure that ensures tools are reliably available, discoverable, and executable.So, a Tool Calling request can be routed through MCP.Here's the visual again for your reference 👇 https://t.co/geB5I6KbqL ...

Summary of Conference Call on AI Agents and Industry Trends Industry Overview - The conference discusses the AI industry, specifically focusing on the development of AI agents transitioning from L2 to L3 stages, with significant implications for future internet traffic and productivity tools [1][3][5]. Key Points and Arguments 1. **AI Agent Development**: The transition to L3 agents is crucial, as they possess capabilities such as chatting, reasoning, and executing tasks, marking a significant step towards L4 and impacting future AI innovations [1][5]. 2. **Market Demand**: The demand for AI applications has shifted from novelty ("toys") to practical tools aimed at enhancing productivity and reducing costs, with expectations for clear results in revenue growth and customer satisfaction by 2025 [1][8][14]. 3. **Technological Maturity**: The maturity of underlying models, such as Deepseek R1, has enabled agents to perform complex tasks, which is a key factor for the expected explosion in agent usage in 2025 [3][6]. 4. **Open Source Ecosystem**: The development of open-source technologies like MCP (Multi-Context Processing) has lowered barriers for developers, fostering innovation and accelerating the adoption of agents [1][9]. 5. **Importance of Success Rates**: High success rates of underlying models are critical for the effective execution of multi-step tasks by agents, as low success rates can lead to task failures [10]. 6. **Types of AI Agents**: Current mainstream agent products are categorized into programming tools (e.g., Cursor), research tools (e.g., Deep Research), and comprehensive applications (e.g., Metas) [4]. 7. **Agent's Role in AGI**: Agents are positioned as a vital link towards achieving AGI, currently operating at the L3 stage, with expectations for increased task complexity and success rates over time [17]. 8. **Impact on Internet Traffic**: The rise of AI agents may alter the traditional internet traffic landscape, potentially displacing existing platforms as agents interact directly with users [18]. 9. **Token Consumption**: The widespread use of AI agents will significantly increase token consumption, as completing tasks often requires multiple steps, leading to higher operational costs [19]. 10. **Vertical vs. General AI Agents**: Vertical AI agents are expected to see faster deployment and deeper market penetration due to their focused applications, while general AI agents face challenges in achieving clear commercial viability [20][25]. Additional Important Insights - **Investment Landscape**: There is a growing interest in investing in AI agents, particularly in companies with strong vertical capabilities and established customer bases, while general AI agents may face scrutiny due to unclear business models [14][26]. - **User Demand**: Despite some skepticism regarding the maturity of general AI agents, there remains a strong demand for AI assistants capable of handling complex tasks, particularly in office and document processing environments [27]. - **Future Predictions**: The development of AI agents will focus on enhancing core capabilities such as tool invocation, planning, memory, and reliability, with a gradual shift from vertical to general applications [26]. This summary encapsulates the critical insights from the conference call regarding the AI agent landscape, technological advancements, market dynamics, and future trends.