# Сопутствующие статьи по теме Agent

Новостной центр HTX предлагает последние статьи и углубленный анализ по "Agent", охватывающие рыночные тренды, новости проектов, развитие технологий и политику регулирования в криптоиндустрии.

OpenAI Launches Workflow Agent, GPTs Begin Countdown

OpenAI has launched Workspace Agents, an evolution of GPTs designed for teams. This new feature allows users to create reusable, shareable agents that automate repetitive workflows. Driven by Codex, these agents operate within their own workspace, can access files, call tools, and run continuously in the background. Users describe a workflow—such as collecting information, making judgments, generating results, and sending outputs—to ChatGPT, which then builds the agent. The entire team can use and refine the agent via ChatGPT or Slack. Once set up, the agent runs autonomously. Workspace Agents are team-shared, code-free, and manageable with predefined permissions and controls. They are suitable for structured, repeatable tasks involving multiple tools and continuous operation, such as software review, feedback整理, report generation, sales follow-up, and risk assessment. The system operates within clear rules, with sensitive actions requiring human confirmation. It aims to transform workflows from being person-dependent to being documented, executable, and reusable—akin to scientific management principles. Competing solutions from Microsoft and Google are also emerging in this space.

marsbit04/24 00:50

OpenAI Launches Workflow Agent, GPTs Begin Countdown

marsbit04/24 00:50

K2.6 is Yang Zhilin's First Roadshow

K2.6 is Yang Zhilin's first roadshow for Moonshot AI's upcoming IPO. The release of K2.6 marks a strategic shift from a developer-focused approach to one aimed at investors and enterprise clients. Key changes include a 58% price increase for API inputs, structured to favor locked-in enterprise users over casual customers. The model is positioned against previous-generation international models like GPT-5.4 rather than the latest competitors, framing it as "first-tier." It also introduces large-scale Agent clusters (300+ agents) and open-source offerings, targeting enterprise automation scenarios. These moves are seen as preparations for K3, a anticipated 3-4 trillion parameter model. With Moonshot's valuation rising to $18 billion, K2.6 is a crucial step to demonstrate commercial viability and market positioning ahead of a potential late-2026 IPO.

marsbit04/22 10:45

China's Chips, the Hidden Intersection of DeepSeek and Kimi

China's AI startup Kimi has released its latest open-source model, K2.6, featuring significant improvements in coding and agent capabilities. This version enhances long-context processing, supports up to 4,000 lines of code generation, and improves API accuracy and multi-agent collaboration, allowing up to 300 sub-agents to work in parallel. Kimi also introduced architectural innovations like MuonClip, Kimi Linear, and Attention Residuals to optimize scaling and efficiency. A key development is the "Prefill-as-a-Service" (PrfaaS) approach, which decouples prefill and decode tasks across data centers using heterogeneous hardware, significantly reducing latency and cost per token. This strategy not only improves performance but also creates opportunities for integration with domestic AI chips, as reliance on Chinese hardware grows due to export restrictions. Both Kimi and DeepSeek are increasingly aligning with local semiconductor advancements, shaping a new ecosystem for AI development in China.

marsbit04/22 06:58

China's Chips, the Hidden Intersection of DeepSeek and Kimi

marsbit04/22 06:58

Google Is Really Anxious, Launches Deep Research Agent Late at Night with MCP Support and Native Charts

Google has launched two new AI research agents, Deep Research and Deep Research Max, built on the Gemini 3.1 Pro model. These agents are designed for enterprise and developer use, accessible via API, and support integration of open web data with private enterprise information through a single API call. They also feature native chart and infographic generation within reports and support the Model Context Protocol (MCP) to connect with third-party data sources securely. Deep Research is optimized for speed and lower latency, while Deep Research Max prioritizes depth and extended reasoning, making it suitable for asynchronous workflows like overnight analysis tasks. The agents are not available to general Gemini app users, including Pro subscribers, and are currently offered as a paid preview through the Interactions API. The update aims to position Google competitively against rivals like OpenAI and Anthropic in the AI research and analysis tool space, particularly targeting high-value sectors such as finance and consulting. Enhanced capabilities include multi-modal input support, collaborative planning features, and improved performance on benchmarks, though comparative data with competitors should be interpreted cautiously due to potential methodological differences.

marsbit04/22 02:37

Google Is Really Anxious, Launches Deep Research Agent Late at Night with MCP Support and Native Charts

marsbit04/22 02:37

Ant Digital Tech Proposes New Architecture for Agent Economy, Covering Four Layers: Identity, Payment, Risk Control, and Compliance

Ant Digital Technologies (Ant Digital) has introduced a new architectural framework for the agentic economy, named the "4R Full-Stack Architecture," at the Hong Kong Web3 Festival. The framework is designed to address four core challenges in AI agent operations: identity, payment, risk control, and compliance. The four layers include: - **Agentic Runtime**, featuring DTClaw with the CARLI security model to enforce behavioral constraints and ensure controllability and auditability; - **Payment Rails**, which provide on-chain payment channels supporting smart decision-making, verifiable credentials, instant settlement, and cross-chain asset transfers; - **Agent Registry**, leveraging DIDs and the ERC-8004 standard to assign verifiable on-chain identities to agents; - **Root Infrastructure**, built on Jovay Layer2 and ZKVM technology to enable high-speed micro-payments and trusted off-chain computation with on-chain verification. According to CTO Yan Ying, the architecture aims to resolve fundamental gaps in the current agent economy—such as execution vulnerabilities, identity issues, payment barriers, and trust deficits—by redesigning underlying infrastructure rather than applying superficial fixes. The initiative builds on Ant Digital’s extensive experience in financial-grade security, privacy computing, and blockchain.

marsbit04/20 09:24

Ant Digital Tech Proposes New Architecture for Agent Economy, Covering Four Layers: Identity, Payment, Risk Control, and Compliance

marsbit04/20 09:24

Harness Arbitrage Era: Rescuing DeFi from the SaaS Edge

The article "Harness Arbitrage Era: Rescuing DeFi from the SaaS Edge" explores the convergence of AI and decentralized finance (DeFi), arguing that AI-driven organizational models and token economies are surpassing DeFi and SaaS paradigms in efficiency. It traces how AI, particularly through agents and harness engineering, is quantizing human labor and organizational structures into scalable, automated systems ("Skill" extraction). Token consumption has exploded, driving data and content production costs toward zero, while SaaS models crumble as AI sells "work capability" rather than mere information. DeFi, though pioneering, has stagnated into a SaaS-like state—charging for transactions but failing to innovate fundamentally. The piece proposes that AI can reboot DeFi by enhancing security (e.g., via Mythos), optimizing capital efficiency, and redesigning token economics around verifiable, real-time returns rather than speculative value. Ultimately, AI agents learning from human behavior could autonomously manage DeFi protocols, making crypto tokens certificates of capital回报率 (return on capital). While AI reduces the value of data and repetitive labor, it opens new economic opportunities for individuals, reshaping finance through scalable, agent-driven automation.

marsbit04/19 01:16

0G Labs Launches Consumer-Grade AI Development Platform Based on Decentralized Infrastructure

0G Labs has launched 0G App, a consumer AI development platform that enables users to build applications using natural language instructions without coding. It combines decentralized infrastructure, privacy-preserving Trusted Execution Environment (TEE) verification, and integrated token and AI agent deployment. The platform aims to bridge the gap between Web2 usability and Web3 infrastructure, allowing users to create, preview, and deploy applications entirely within a unified ecosystem. Key features include support for real-time iteration, hardware-secured AI inference (using Intel TDX and NVIDIA H100/H200), and the use of the GLM-5 74.4B parameter model. Additionally, 0G App includes a "Launcher Hub" for app, agent, and token deployment, with compute resources tied to the $0G token. Applications can be deployed to platforms like Vercel while storing data on 0G’s decentralized storage layer.

marsbit04/15 13:26

0G Labs Launches Consumer-Grade AI Development Platform Based on Decentralized Infrastructure

marsbit04/15 13:26

Agents Have Entered the Harness-Driven Era

The article discusses the significance of the leaked Claude Code from Anthropic, highlighting its revelation of advanced Agent engineering practices centered on "Harness" design. Rather than relying solely on model capabilities, modern AI systems now depend on a structured engineering framework—the Harness—to maximize performance. This framework includes six core components: multi-layered System Prompts, Tool Schema, Tool Call Loop (with Plan and Execute modes), Context Manager, Sub-Agent coordination, and Verification Hooks. The Harness enables tighter integration between training and inference, supports long-chain tool execution, and improves reliability through objective verification. It also drives six key training directions: behavior alignment via System Prompt, end-to-end tool-use training, integrated plan-execute training, memory compression, sub-agent orchestration, and multi-objective reinforcement learning. The shift to Harness-driven development reduces the emphasis on pure prompt engineering, favoring instead multidisciplinary talent with skills in AI, backend engineering, and infrastructure. The market is evolving toward more secure, private, and vertically integrated Agent deployments, with "model shell" companies needing either strong infrastructure or deep domain expertise to compete. Claude Code’s leak underscores that future AI advancements will be shaped by engineering architecture as much as by algorithmic innovation.

marsbit04/15 10:11

Agents Have Entered the Harness-Driven Era

marsbit04/15 10:11

10 Charts to Understand the State of AI in 2026: US-China Gap Only 2.7%, Sharp Decline in Programmer Positions for Under-25s

The 2026 AI Index Report from Stanford HAI reveals that AI adoption is accelerating faster than PCs and the internet, with a 53% global adoption rate. However, societal systems, job markets, and measurement tools lag behind. Key findings include: - Benchmark reliability is questionable, with 42% of GSM8K math problems deemed invalid. - The U.S. and China show near-parity in model performance (2.7% gap), with the U.S. leading in compute/capital and China in research/manufacturing. - Top models (Anthropic, xAI, Google, OpenAI) show converging capabilities, shifting competition to cost and reliability. - Employment for young developers (22–25) fell nearly 20%, with McKinsey noting AI-driven reductions in services, supply chain, and engineering. - The U.S. ranks 24th in adoption (28.3%) despite leading investment ($285.9B private AI funding in 2025). - AI agent task success improved but has ~33% failure rates; physical robots struggle outside labs (12.4% home success vs. 89.4% in sim). - A stark expert-public divide exists: 73% of experts vs. 23% of the public view AI’s job impact positively. - GPT-4o’s annual water use exceeds 12M people’s needs; AI data centers consume power equivalent to New York State. The report underscores rapid AI integration amid unresolved ethical, environmental, and economic challenges.

marsbit04/15 00:18

10 Charts to Understand the State of AI in 2026: US-China Gap Only 2.7%, Sharp Decline in Programmer Positions for Under-25s

marsbit04/15 00:18

Someone Turned Buffett and Munger into Agents, Then Open-Sourced It...

The open-source project "AI Hedge Fund" has gained significant traction on GitHub, creating AI agents modeled after 12 legendary investors, including Warren Buffett and Charlie Munger, to analyze stocks and develop trading strategies. It features a team of 6 analyst agents that synthesize insights and make final decisions. The system includes a backtesting module to evaluate strategies with historical data before real investment. Built by developer Virat Singh, the project supports 13 major AI models and can run locally. It uses a React front-end with a visual workflow editor and a Python/FastAPI backend orchestrated with LangGraph. The agent team covers diverse philosophies, from value investing (Graham) to growth (Cathie Wood) and risk management (Taleb). While not proven in live markets, it offers a platform for learning agent frameworks and diverse investment perspectives through simulated expert debate. The project highlights a growing trend of "distilling" financial wisdom into AI, but users are cautioned about its unverified returns and investment risks.

marsbit04/14 11:16

Someone Turned Buffett and Munger into Agents, Then Open-Sourced It...