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当智能体进入生产，华为云把“开放”做成了护城河

近日，在华为全联接大会2026期间，华为公司董事、华为云CEO周跃峰发表了“共建Agentic Cloud，共赢行业智能体时代”主题演讲。题眼落在共建和行业两个词上，这恰恰点出了华为云对当前AI产业阶段的核心判断：智能体正在从技术概念走向行业落地，而落地的前提，是需要一个足够开放、足够扎实、足够懂行业的云底座。

从“算力焦虑”到“算力理性”：Akamai解答AI推理时代的ROI之问

过去两年，全球AI产业的关键词是“抢GPU”。从Meta、微软到xAI，动辄十万卡级别的集群建设不断刷新着人们对算力规模的想象。在这一背景下，笔者有幸采访到了Akamai云计算首席技术官Jay Jenkins，围绕AI算力的分配逻辑、推理场景的隐性成本、分布式架构的合规价值以及多智能体时代的基础设施挑战，进行了深入探讨。

How V7 gives AI agents institutional memory

Using GPT-5.6, V7 turns scattered company files into context agents can use to complete complex, source-linked work.

Jev-as-a-Judge for Agent Evals

v2.1.277

What's changed Added AGENTS.md support: in a project with no CLAUDE.md, Claude Code reads AGENTS.md instead; change it under "Project instructions" in /config (not yet on Bedrock, Vertex or Foundry) Added CLAUDE_GATEWAY_PROXY_IS_EGRESS_BOUNDARY=1 for Claude apps gateways whose only egress is a forward proxy: every outbound request hands the proxy the hostname instead of resolving it locally Added an optional headers: map on Claude apps gateway upstreams, to send static headers to a proxy you run in front of a provider Added a line saying a background task's update is waiting when it finishes while a panel such as /tasks is open Fixed claude -

How DHI Group accelerates generative AI workloads from idea to production using hackathons

Learn how DHI Group partnered with AWS to move generative AI workloads from idea to production using a structured hackathon. This post covers the Hackathon Acceleration Package, the winning ClearanceJobs and AgileATS agentic architecture on Amazon Bedrock AgentCore, and the principles that make hackathons a repeatable path to production.

Migrating the GitHub Copilot runtime to Rust, using Copilot

A rewrite this size wasn't affordable before agents. Here's what porting the Copilot agent runtime to 800,000 lines of production Rust actually took. The post Migrating the GitHub Copilot runtime to Rust, using Copilot appeared first on The GitHub Blog .

Building an Agent Harness for Life Sciences: Introducing Deep Life Sci

How Included Health Built Federated Agents for Healthcare Navigation with Deep Agents and LangGraph

How to Use AI Agents to Prepare 3D Scenes for Simulation

Agentic AI workflows can be used to prepare and validate digital twins for physical AI systems. Agents can inspect 3D scenes, author simulation-relevant data in...

TensorRT Edge-LLM Completes the MLPerf Edge Agentic Benchmark 6.4x Faster on Jetson AGX Thor

AI agents are moving from cloud data centers to vehicles, robots, and other edge devices. Unlike a chatbot that answers a single prompt, an agent works through...

Translating CUDA Tile Operations from Python to Rust Using Agentic AI

cuTile Rust (cutile-rs) is a tile-based system for safe, idiomatic GPU kernel authoring in the Rust programming language. Extending the Rust ownership model to...

Reimagining advertising with AI

Explore new AI-powered advertising experiences from OpenAI, including Sponsored Agents, tools for marketers, and integrations with HubSpot and Shopify.

Hex turns complex analysis into visual reports with GPT‑6 Astra

GPT-6 Astra helps Hex’s data agents turn answers into interactive visualizations that employees are proud to share.

v2.1.273

What's changed Added x-claude-code-request-class , x-claude-code-agent-type , x-claude-code-prev-tool-durations , x-claude-code-compaction and x-claude-code-context-compacted request headers for LLM gateways; opt in with CLAUDE_CODE_GATEWAY_HINT_HEADERS=1 Added a notification when an MCP server disconnects mid-session and automatic reconnection gives up, pointing at /mcp Added forking a session started with claude --remote-control or /remote-control from the Claude app; the fork runs as a background session on your computer Fixed Bash commands the permission checker cannot fully analyze skipping the prompt under permissions.blockReadsOutsideW

Your Agent Aced the Task. Will It Do It Again?

Give every teammate and agent the right level of access to your Workers

You can now scope access to individual Workers and assign narrower Developer Platform roles, so teammates, CI tokens, and agents get only the access they need to debug, deploy, or monitor safely.

Scaling Agents in Healthcare & Life Sciences: Lessons from Madrigal Pharmaceuticals, Abridge, and Vizient

How we built LangChain's Paid Media Agent

From zero-shot forecast to purchase order with Amazon Bedrock AgentCore

Combine zero-shot forecasting with Amazon Chronos2 and multi-agent orchestration on Amazon Bedrock AgentCore to turn demand forecasts into validated purchase orders. No per-product model training, with business rules, auditability, and cost that scales to zero.

Arm的智能体时代答卷：从云到边缘，让计算平台无处不在

近日，以“赋能AI计算的未来”为主题的Arm Everywhere China年度大会盛大召开。在AI从生成式走向智能体的关键节点上，产业界对计算底座的关注度正在急剧升温。行业需要一个既能提供开发者所需一致性，又能赋能差异化创新的平台。Arm正在试图证明，它就是这样的平台。

GitHub Copilot app for Beginners: Using the diff, terminal, and browser

Checking agent-generated code usually means hopping between tabs. Learn how to view diffs, run terminal commands, and preview web apps side by side in the GitHub Copilot app. The post GitHub Copilot app for Beginners: Using the diff, terminal, and browser appeared first on The GitHub Blog .

The Economics of Agent Optimization: How AI agent governance controls cost and proves ROI

This blog post is the fourth and final installment of The Economics of Agent Optimization, which shares the strategies, capabilities, and proof points that can help you optimize agent costs and run AI as a managed investment system on Microsoft Foundry. The post The Economics of Agent Optimization: How AI agent governance controls cost and proves ROI appeared first on Microsoft Azure Blog .

Now everyone can put data to work

Meet the Data agent in ChatGPT Work. Connect company data, uncover insights, and build interactive dashboards with AI using natural language.

智能无处不在，Arm赋能AI计算的未来

日前，Arm 控股有限公司举办 Arm Everywhere China 年度大会，以“赋能 AI 计算的未来”为主题，吸引近 2000 名来自产业链上下游的行业领袖、开发者及生态伙伴报名参会，共同探讨智能体时代的计算创新与产业发展机遇。

Connections: Managed credentials and per-caller identity for Managed Deep Agents

Beyond the benchmark: How an adaptive approach drives scientific discovery

For research and development (R&D) organizations, the promise of agentic AI is not a better one-time answer. It is a new way to explore complex scientific and engineering problems: pursuing multiple hypotheses, validating them against evidence, learning from what does not work, and adapting their approach as new information becomes available. The post Beyond the benchmark: How an adaptive approach drives scientific discovery appeared first on Microsoft Azure Blog .

Organizing Context in a Multi-Agent Harness

vLLM x AgentX: Optimizing for Real-World Agentic Serving

How vLLM optimizes KV cache management, parallelism, scheduling, and P/D disaggregation for agentic workloads, validated on SemiAnalysis AgentX with up to 130K

Building a Memory-Driven Agent with NVIDIA NemoClaw

Enterprise work spans messages, decisions, projects, and obligations that change over time. An AI agent that starts without this context must reconstruct it...