标签：#Microsoft

Microsoft is placing greater emphasis on controlling disruption in Azure Kubernetes Service (AKS) Node Auto-Provisioning (NAP), publishing new guidance to help platform teams balance the efficiency benefits of automated node consolidation with application availability. By Craig Risi

Some of the world's largest tech companies and AI startups have come together to decry the current state of cybersecurity and to advertise a new solution that they say can ward off a new generation of cyber threats.

"Most" Xbox One and Series X games are compatible; discs will still work after being claimed.

Microsoft is officially announcing a new Xbox feature that will allow Xbox owners to digitize their existing physical game collections. The disc-to-digital feature, which The Verge exclusively revealed last month, is part of work that Microsoft is doing for game preservation and in preparation for the next generation of Xbox consoles. "Most Xbox One and […]

The special-edition translucent green Xbox finally has an official price. Preorders for the console start August 27th at 10AM ET / 7AM PT, although Microsoft says "some of XBOX's most dedicated fans" will receive early access preorder emails starting today. The console will launch on November 13th, alongside a matching controller with the same "OG […]

Bill Gates has been reflecting a lot on AI lately, and the process has triggered a stark awakening. Once a staunch AI optimist, the Microsoft cofounder is now deeply pessimistic about what AI means for our collective future. Having been conspicuously quiet on AI issues recently, Gates is back with a nearly 6,000-word essay seeking […]

The new Designed for Xbox 25th Anniversary collection features a handful of limited edition gadgets available for pre-order today. Paying homage to the Halo special edition of the original Xbox, almost everything in the collection is translucent "OG Green," matching the Xbox Series X25 limited edition console it announced in June at E3. That includes […]

从 Demo 到生产：那些真正让 AI Agent 敢上线的护栏 开场钩子： 你在网上看到的多数「AI Agent」都是 demo。它们之所以上不了生产，原因往往 只有一个 —— 而下面这个开源的小脚手架，专门解决它。 我们已经过了「能调通大模型」就算赢的阶段。现在真正难的是那没人讲的 10%： 是什么阻止 Agent 做出伤害性的事？ 我在微软跑过一套约 25 个 Agent 的生产平台，现在也帮团队把 Agent 从笔记本推进到真实用户面前。两边的体会是一致的。 一个不太舒服的真相：能调 5 个工具的聊天机器人， 不是产品 。周末项目和你敢放到客户面前的 系统之间，差的只有三件事 —— 而且全都是不酷、不性感的工程： 你怎么给输出质量打分 （质量门）。 你怎么决定什么时候必须人签字 （审批门）。 你如何让整套东西模型无关 ，不被某个厂商锁死。 所以我写了一个很小的 harness，把这三件事摆在最显眼的位置。它故意做得很小 —— 一小时能 读完 —— 因为价值不在「框架」，在 模式 本身。 仓库： github.com/zhasun0818/ai-agent-scaffold 1. 质量门：别发布你无法打分的东西 Agent 的输出是「预测」不是「承诺」。上线前它必须过一道 检查 ：是否达到你的标准。脚手架里 这是一个可插拔的 QualityGate ，你可以换成 LLM 裁判或测试套件： # agent_harness/eval.py @dataclass class EvalReport : passed : bool score : float checks : List [ str ] class QualityGate : def grade ( self , proposal : str , context : str = "" ) -> EvalReport : return self . grader ( proposal , context ) 循环在门没过之前拒绝执行： result . report = self . quality . grade ( proposal , f " state= { state } " ) if not result . report . passed : self . approval . log ( " quality-gate " , " blocked " , result . report . __str__ ()) return result 注意它 把拦截记录下来了 。生产里你会想把这些被拦的尝试都进可观测性系统。「这周我们拦下 了 12% 的 Agent 提议」是个真实 KPI —— 它说明门在工作。 2. 审批门：所有人都忘掉的那一步 这才是让企业真正点头说「可以」的东西。当 Agent 想加急订单、取消订阅、或动钱的时候，它应该 停下来问人 。沉默不等于同意。 # agent_harness/approval.py class ApprovalGate : def request ( self , action : str , detail : str ) -> bool : # 生产里：推一条通知到 Teams / Slack / 邮件，然后等待。 decision = input ( f " Approve { action } ? [y/N] " ). strip (). lower () self . audit . append ( AuditEntry ( time . time (), action , " human-reviewer " , decision , detail )) return decision . startswith ( " y " ) 在脚手架里，标记 needs_approval=True 就够了： @tool ( " expedite_order " , " Mark an order as expedited. " , needs_approval = True ) def expedite_order ( order_id : str ) -> str : return f " PO { order_id } : marked expedited " 而且因为有 审计链 ，你永远能回答「谁改的、为什么」—— 这通常是合规团队问的第一个问题。 3. 模型无关的 provider：别跟一个厂商结婚 模型每几周就变，价格也是。你的 Agent 循环不该知道自己在对谁说话： # agent_harness/providers.py class ModelProvider ( Protocol ): def

From Demo to Production: The Guardrails That Make an AI Agent Safe to Ship Hook: Most "AI agents" you see on the internet are demos. Here's the single most common reason they never reach production — and a small, open-source harness that gets past it. We are past the phase where the hard part of building an AI agent was calling the model. The hard part now is the 10% nobody talks about: what stops the agent from doing something harmful? I've seen this from both sides — I built and ran a ~25-agent platform in production at Microsoft, and now I help teams take agent ideas from a notebook to real users. The uncomfortable truth: a chatbox that can call 5 tools is not a product. The difference between a weekend project and a system you can put in front of customers is three things — and they're all boring, non-glamorous engineering: How you grade output quality (the quality gate). How you decide when a human must sign off (the approval gate). How you make the whole thing model-agnostic so you're not locked into one vendor. So I wrote a tiny harness that keeps these front and center. It's intentionally small — small enough to read in an hour — because the value isn't in a framework, it's in the pattern . Repo: github.com/zhasun0818/ai-agent-scaffold 1. The quality gate: don't ship what you can't grade An agent's output is a prediction, not a promise. Before it ships, you need a check that it passes your bar. In the harness this is a pluggable QualityGate — a rule of thumb you swap with an LLM judge or a test suite: # agent_harness/eval.py @dataclass class EvalReport : passed : bool score : float checks : List [ str ] class QualityGate : def grade ( self , proposal : str , context : str = "" ) -> EvalReport : return self . grader ( proposal , context ) The loop refuses to execute if the gate fails: result . report = self . quality . grade ( proposal , f " state= { state } " ) if not result . report . passed : self . approval . log ( " quality-gate " , " blocked " , result

Laptop prices are out of whack. $500 used to get you a tolerable laptop, and $900 got you a really good one. They often had similar CPU, RAM, and storage options because that stuff was comparatively cheap; the difference was often in build quality and screen rather than power. But RAMageddon has thrown everything off. […]

Data and finance professionals are competing in Excel obstacle courses—amassing huge followings and keeping the Microsoft program relevant.

Microsoft did the hard work so you don't have to. The Microsoft.FeatureManagement library integrates directly with .NET's configuration and dependency injection systems, which means you can get feature flags working with minimal code and a solid foundation. For the full documentation, check out the Microsoft Feature Management documentation . Let's get this thing running. Installation Add the NuGet package to your project: dotnet add package Microsoft.FeatureManagement.AspNetCore That's it for dependencies. No magic rituals required. Configuration Register the feature management services in Program.cs : builder . Services . AddFeatureManagement (); By default, feature flags are read from the FeatureManagement section of your appsettings.json : { "FeatureManagement" : { "NewDashboard" : true , "ExperimentalSearch" : false } } Flag names are strings. Values are booleans. Simple. Checking a Flag in Code Inject IFeatureManager wherever you need to check a flag: public class DashboardController : Controller { private readonly IFeatureManager _featureManager ; public DashboardController ( IFeatureManager featureManager ) { _featureManager = featureManager ; } public async Task < IActionResult > Index () { if ( await _featureManager . IsEnabledAsync ( "NewDashboard" )) { return View ( "NewDashboard" ); } return View ( "OldDashboard" ); } } That's the whole pattern. Inject. Check. Branch. Repeat. Using Feature Filters Boolean flags are useful, but sometimes you need something a little more sophisticated. The library supports feature filters for things like: Percentage rollouts Time windows User targeting For example, you can enable a feature for a percentage of requests: { "FeatureManagement" : { "BetaFeature" : { "EnabledFor" : [ { "Name" : "Percentage" , "Parameters" : { "Value" : 20 } } ] } } } This enables BetaFeature for 20% of requests. The library handles the sampling. You handle the business logic. Everybody wins. Razor Tag Helpers Building a Razor-based UI? The lib

前言 2026年4月2日，微软正式开源发布了 Agent Governance Toolkit （AGT），这是一套专为自主AI智能体打造的开源运行时安全治理框架。MIT许可证，支持Python/TypeScript/Rust/Go/.NET多语言，覆盖全部10项OWASP Agentic Top 10风险，策略执行延迟低于0.1毫秒。 本文将系统性地解答：这个工具包是什么、为什么需要它、怎么使用、以及它能帮助我们实现什么目标。 一、它是什么 1.1 基本定义 Agent Governance Toolkit 是微软开源的AI Agent运行时安全治理框架。它的核心理念是： 将操作系统内核设计的几十年经验，应用于AI智能体的安全治理 。 用微软官方博客的话说： "当你观察AI智能体在生产环境中的实际行为时，你会发现一个熟悉的模式：多个不可信程序共享资源、做决策、与外部世界交互，而它们的行为几乎没有得到任何中介管控。操作系统早在几十年前就解决了这个问题——通过内核、权限等级和进程隔离。服务网格用mTLS和身份认证解决了微服务的同类问题。SRE用SLO和熔断器解决了分布式系统的可靠性问题。我们的问题是：把这些经过实战检验的成熟模式，应用到AI智能体上会怎样？" 1.2 架构全景 AGT经历了v4.0.0版本重构，将早期45个独立包整合为5个顶层分发包： 分发包 包含内容 agent-governance-toolkit-core 策略引擎（Agent OS Kernel）+ 身份管理（AgentMesh Platform） agent-governance-toolkit-runtime 执行环（Execution Rings）+ 沙箱 + 熔断器 + 急停开关 agent-governance-toolkit-sre 健康监控 + SLO执行 + 事件响应 + 混沌工程 agent-governance-toolkit-cli 命令行工具集（agt doctor / agt verify 等） agent-governance-toolkit[full] 完整全家桶安装 早期包名（agent-os-kernel、agentmesh-platform、agentmesh-runtime、agent-sre等）仍可作为存根包安装，会自动重定向到新分发包。 1.3 五大核心组件 Agent OS（策略引擎） AGT的策略引擎是整个系统的核心，被称为AI智能体的"内核"。它以无状态方式运行，使水平扩展和容器化部署自然可行。策略引擎以应用中间件层形式工作（而非OS内核层），策略引擎与智能体共享同一进程边界。生产推荐：在独立容器中运行每个智能体以实现OS级隔离。 支持的策略语言：YAML规则、OPA Rego、Cedar Policy Language。 Agent Mesh（身份与信任层） 密码学身份 ：使用Ed25519生成去中心化标识符（DIDs），为每个智能体建立不可伪造的加密身份 智能体间信任协议（IATP） ：安全的智能体对智能体通信协议 动态信任评分 ：0-1000分五层行为等级。信任是动态的——上周被信任但此后沉默的智能体，会逐渐失去信任，这与"二进制信任/不信任"的传统模型截然不同 Ed25519签名验证 ：对智能体间通信进行密码学验证 Agent Runtime（执行运行时） 执行环（Execution Rings） ：借鉴CPU权限等级设计，将智能体分为4个Ring Ring 信任等级 能力 Ring 0（内核） 评分 ≥ 900 完全系统访问，可修改策略 Ring 1（Supervisor） 评分 ≥ 700 跨智能体协调，提升的工具访问 Ring 2（User） 评分 ≥ 400 标准工具访问，限定的作用域 Ring 3（Untrusted） 评分 < 400 只读，无副作用 Saga编排 ：多步骤事务的原子性保证 急停开关（Kill Switch） ：紧急终止失控智能体，支持多种终止原因（RATE_LIMIT、RING_BREACH、BEHAVIORAL_DRIFT、MANUAL） Agent SRE（可靠性工程） 将SRE的黄金实践应用于智能体系统：SLO与错误预算、熔断器（防止级联故障）、混沌工程测试、渐进式发布。 Agent Compliance（合规自动化） 防篡改Merkle审计日志（每次决策均记录：策略版本、动作、身份、裁决结果） 合规分级与监管框架映射 覆盖标准：OWASP Agentic Top 10、NIST AI RMF 1.0、EU AI Act、SOC 2 Type II、CSA ATF、新加坡MGF agt verify CLI生成机器可读证据文件，可直接接入CI/CD流水线 1.4 M

title: Why I left Warehouse out of our Fabric deployment scope published: true tags: microsoftfabric, datawarehouse, cicd, devops Our Fabric deployment pipeline handles sixteen item types. Warehouse is not one of them, and that was deliberate. DEFAULT_ITEM_TYPES = [ " DataPipeline " , " Lakehouse " , " Notebook " , " SemanticModel " , # "Warehouse" is intentionally excluded. Warehouse schema deployment must # be handled separately to avoid schema reset risk during publish. " Environment " , " Eventhouse " , ... ] The reason Publishing a warehouse through this path can reset its schema. Not "might behave unexpectedly". The failure mode is that a deployment intended to be additive removes structure, and the thing that removes it is the same routine that successfully deploys the other sixteen types. The choice that follows Two options once you know that. Include it and hope nobody deploys a warehouse without reading the docs. The pipeline supports everything, and one day someone promotes a change on a Friday and finds out. Or exclude it, document why, and handle warehouse deployment as its own problem with its own tooling. I took the second. An automation that covers most cases and silently corrupts the rest is worse than one that covers most cases and refuses the rest. The refusal is visible. The corruption is not. Making the exclusion loud An exclusion is only useful if someone notices it. Three things help: The comment sits inside the list , not in a doc nobody opens. Anyone reading the item types sees the gap and the reason in the same glance. It is in the README under known limitations, next to the other things the framework does not do. There is a test. It asserts Warehouse is absent from the default scope: def test_warehouse_stays_excluded ( self ): """ Warehouse publish can reset schema, so it is handled separately. """ self . assertNotIn ( " Warehouse " , deploy . DEFAULT_ITEM_TYPES ) That test looks silly. It is asserting that a string is missing from a list.

Threat Overview 🚨 Microsoft SharePoint now has a critical flaw, CVE-2026-55040, that has already started being exploited in the wild. ⚠️ The vulnerability scores a 9.1 on CVSS and lets unauthenticated attackers bypass authentication to perform arbitrary operations on any affected site. Vulnerability Technical Background 🔍 The root cause is a flaw in SharePoint’s JWT token validation chain used for service‑to‑service (S2S) communication. ❌ Two internal classes, SPJsonWebSecurityTokenHandlerV2 and SPJsonWebSecurityBaseTokenHandlerV2, incorrectly parse the outer header of a JWT, allowing attackers to skip signature verification under certain conditions. Exploit Chain Step‑by‑Step 1️⃣ The attacker crafts a JWT with alg=none in its outer header, effectively removing the requirement for an outer token signature. 2️⃣ An inner actor token is embedded that includes SharePoint’s own STS certificate thumbprint, tricking SharePoint into resolving a signing key without proper verification. 3️⃣ The resolved certificate is not listed in TrustedSecurityTokenServices, letting the issuer claim be accepted unquestioned. 4️⃣ The actor token’s signature can simply be a non‑empty placeholder like AAAA , which never gets validated, leaving the chain open for injection. Proof of Concept Availability 🔐 A fully functional Python PoC was released by Rapid7 earlier this week. # forge_jwt.py – minimal example import jwt , requests def craft_token (): header = { " alg " : " none " , " typ " : " JWT " } payload = { " iss " : " https://sharepoint.com " , " aud " : " https://sts.sharepoint.com " } return jwt . encode ( payload , key = None , algorithm = " none " , headers = header ) token = craft_token () print ( " Forged token: " , token ) ⚙️ The script demonstrates forging the JWT chain, querying a target domain controller, enumerating user SIDs, and automatically identifying site administrators. 📂 Full source code is available at hxxps://githubcom/sfewer-r7/CVE-2026-55040. Active Exploitation La

The amorphous corporate mascot will no longer haunt Copilot Voice.

Microsoft Copilot will no longer show its emotive yellow blob, Mico, when you use the chatbot's voice mode. In a support page, Microsoft says it's going to move Mico to its Learn Live platform, where the avatar will have "more to react to," as reported earlier by GeekWire. Mico launched in Copilot's voice mode last […]

Microsoft is simplifying Copilot by combining its consumer and business apps, and dropping AI-generated podcasts, Group Chats, Deep Research, and its Mico character.

Microsoft is finally beginning to combine its consumer and commercial Copilot AI assistants into a single "super app" interface, starting with the Copilot and Microsoft 365 Copilot apps. Both personal and work accounts will be moved to the new unified app, which recycles the "Microsoft Copilot" name but features an updated app icon. The single […]

This is the latest zero-day released by security researcher Nightmare Eclipse, despite Microsoft publicly threatening to take legal action against them.