标签：#agentic

From loop engineering to harnesses, squads, and open weights, the GitHub Podcast breaks down the AI terms showing up in developer conversations. The post Decoding the new AI lingo: Loops, harnesses, squads, hill climbing… oh my! appeared first on The GitHub Blog .

Why shorter outputs can cost more, and how GitHub Copilot reduces wasted work across the complete coding task. The post How we make AI coding more cost efficient without sacrificing task quality appeared first on The GitHub Blog .

What the court actually held Amazon sued Perplexity in November 2025 over its Comet browser, pleading the federal Computer Fraud and Abuse Act and California's Comprehensive Computer Data Access and Fraud Act. A district court granted a preliminary injunction in March 2026. The Ninth Circuit stayed it pending appeal, and in August 2026 vacated it. The reasoning is the part worth carrying away. On the record before the panel, the systems were being accessed by Amazon's own customers, signed into their own accounts, using software they had chosen. Perplexity was not the one accessing Amazon. On that basis Amazon was unlikely to prevail on a statute written about unauthorised access. It is the first federal appellate ruling on whether AI agents acting for a user may access an online platform, and the panel was careful to say it was deciding that record rather than announcing a doctrine. What it did not hold It did not say agents are welcome, and it did not say a site has lost control of its own front door. Contract claims were not what the panel found weak. Terms of service, trademark questions and state-law theories are all untouched. A different record with different facts, particularly one where the agent operates at scale rather than for one signed-in customer, could come out differently. The useful summary is narrow and worth stating without decoration: computer-misuse law is a weak instrument against software a customer chose to run on their own account. The distinction the ruling turns on Crawler User's agent Acting for Its operator One signed-in customer Scale Many sites, high volume One session at a time Authenticated Usually not As the customer Data ends up In the operator's product In front of the person who asked The ruling's reasoning Does not apply Applies Most blocking rules in the wild do not make this distinction. A blanket refusal of automated access catches a customer's own agent alongside the scraper it was aimed at, and those two are commercially o

I wrote this post for my entry to the All Things Agentic Hackathon. TLDR: I built a five-agent design team on Gemini (Including Gemini Flash 3.7 and Gemma 4) that takes a brief and a folder of photographs and returns finished, editable pages. The interesting engineering was not the prompts. It was deciding wh ere the run's progress lives. Code: github.com/minhthanhdang/vibes-ai . What it does Vibes AI is a design co-pilot. Upload photographs, describe what the thing is for, and it designs the pages: real crops, generated backgrounds, type in any Google Fonts family, all written as geometry that can be dragged afterwards. There are five agents. An orchestrator holds the other four as tools, so every hop is request and response, and the user reads one reply instead of a transcript of agents talking to each other. A property analyzer reads each upload in six design dimensions. An image editor cuts. An image generator draws the picture the gallery does not have. A design assistant does the actual designing. The part I want to write about is the unattended run. One form (purpose, page count, palette, vibe, size) and then no further human input until the pages are done. One long request was the wrong shape Designing six pages is minutes of model calls, not milliseconds. My first instinct was one request that loops over the pages and returns when it is finished. That shape gives nothing back. No honest progress, no Stop button that means anything, and a failure at page four throws away pages one to three. So a page became the unit of work. One job designs one page. The job is a row in an AgentRun table, a worker claims it under a lease, and when it settles it enqueues the next page inside the same transaction that marks the current one done: const chained = await db . $transaction ( async ( tx ) => { const won = await tx . agentRun . updateMany ({ where : { id : run . id , status : RunStatus . RUNNING , startedAt : run . claimedAt }, data : { status : RunStatus . SUCCEEDED

Of the Glassdoor reviews from claims adjusters that mentioned AI, a staggering 98 percent were negative. “AI is just a tool,” one person tells WIRED. “It should never be given the keys.”

The potential for AI to automate scientific research and manufacturing must be balanced with new risks, Anthropic says.

Code reviewed by WIRED reveals the company is developing a feature that enables Codex to continue working proactively until it is “put to sleep.”

I ran npx is-agentic promptway.com and the report came back 74 out of 100 . Essential was 59 of 80. Recommended 12.6 of 20. A 2.4-point bonus. The label was "Ready with a few material gaps." Earlier this week I did the same work on my personal site and wrote it up there ( I fixed my site for agents by hand. Then Vercel shipped a scoreboard ). Promptway is the publication I want agents to cite, so I pointed the grader at this host next. We already shipped the eight-layer stack I described in Optimizing Your Site for AI Agents and LLMs : robots allowlist, sitemap, llms.txt, llms-full.txt, JSON-LD, feeds, article markdown siblings. The scoreboard still found holes. Most of them were ordinary web hygiene. A couple were "developer resources" checks that assume you are a SaaS. I fixed the first group and refused to fake the second. What 74 was made of is-agentic.com wraps Ora 's agent-readiness research. Essential checks share 80 points, recommended share 20, and a small bonus can add up to 5. Checks that do not apply get excluded. The methodology page is worth reading before you argue with a number. Reports cache for six hours, so a re-scan right after a deploy can lie to you. The CLI is the useful interface: npx is-agentic promptway.com npx is-agentic promptway.com --json It returns a stored report if one exists, or starts a scan and waits. --json is the shape an agent wants. The failures that mattered on this site, in the order the report ranked them: Agent-friendly 404s. HTTP 404 already, but the body was a styled dead end. Partial credit until the 404 points at llms.txt, the sitemap, and a next step. Content without JavaScript. The homepage had an H1 and enough characters. The outline was flat, because the only nested headings lived inside card links, which the grader did not count. Markdown content negotiation. Accept: text/markdown returned text/html . Vary had the Next.js RSC list and no Accept . Failed. Developer resource discoverability. An agent searched for "p

webmcp agentic web: Why Backend Engineers Must Rethink Their Architecture Quick Answer webmcp agentic web: Agentic web workloads over MCP require stateless gateways, distributed context stores, prompt caching, and fine‑grained telemetry to keep latency below 350 ms and cost under control. Latency and State in Multi‑Agent LLMs When a Multi‑Agent System talks to an LLM over the Model Context Protocol (MCP) , the assumptions that hold for CRUD REST APIs break apart. A 200‑ms timeout that covers a simple GET request now collapses into a 2‑second latency spike because each tool call injects a new sub‑prompt, inflates the token budget, and forces the backend to stitch together dozens of partial contexts. In the field, the LLM behaves like a stateful, high‑throughput service that must be orchestrated, not a stateless function. Real‑World Example Consider a U.S. e‑commerce platform that needs to serve 12 k concurrent shopping sessions. Each session spawns up to five agents (pricing, inventory, recommendation, fraud, checkout). The platform’s existing micro‑service stack was built for single‑shot CRUD calls; when the agentic layer was added, the following issues surfaced: Context drift: stale prompts silently degraded recommendation quality. Token explosion: every tool call added 200–300 tokens, pushing the total payload past 8 k tokens. Throughput hit: the MCP service was throttled by Azure OpenAI’s per‑deployment request rate limits. After re‑architecting to a stateless MCP gateway backed by a distributed context store, the platform maintained 99th‑percentile latency under 350 ms even during a Black Friday surge. Trade‑Offs Aspect Option A Option B When to choose Context Storage Redis Cluster (in‑memory, low latency) Cosmos DB (strong consistency, global replication) Redis for ultra‑low latency, Cosmos for compliance or multi‑region writes Prompt Caching Enable KV‑cache on Azure OpenAI Re‑send system prompt on every request Enable when prompt size >20% of total token budge

DeepSeek has released a developer preview of DeepSeek Harness (dsh), an open-source execution runtime for building autonomous AI agents. The software features a micro-kernel architecture with modular plugins for various functional units. The release includes an append-only event logging system for tracking execution activities. Adoption may depend on plugin ecosystem stability and API maintenance. By Olimpiu Pop

Flock’s surveillance cameras have already sparked outrage. WIRED reconstructed its next-generation AI system, already in use by some police, to confirm it goes much further than tracking license plates.

Amazon Web Services has extended Amazon Bedrock AgentCore with runtime instances, a new compute option that gives AI agents persistent infrastructure purpose-built for complex long-running workflows and multi-agent coordination. By Matt Saunders

Cloudflare is introducing WriteGuard, now in private beta, to provide fine-grained security controls for MCP (Model Context Protocol) servers. It aims to make AI agents safer by controlling their access to tools that can modify data or perform actions, rather than simply read information. By Sergio De Simone

Netflix open-sourced an agentic workflow for Observational Causal Inference (OCI) that reduces toil in causal analysis. Given observational data and the human user's analysis plan, the agent uses an actor-critic loop to estimate causality, write a report, and suggest next steps. By Anthony Alford

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

Inside the NEXUS AI App Builder: an agentic full-stack workspace, not a code generator Published: August 4, 2026 Category: AI Builder Reading time: 11 minutes Author: NEXUS AI Team Most "AI app builders" do one thing well: turn a prompt into a first draft. Ask for a second change, a real database, or a form that actually submits, and the illusion breaks. You are back in a normal editor, debugging code nobody on your team wrote. The NEXUS AI App Builder is built around a different assumption: the first draft is the easy part. The workspace has to survive edit five, edit fifty, a broken build, a schema change, and a handoff to a teammate or another AI agent, without you ever leaving the conversation. This post walks through how the Builder actually works: the agentic edit loop, the two ways to preview a change, visual iteration, sharing and remixing, the MCP handoff that lets coding agents use it directly, and how a Builder project becomes a deployed production app. What most AI builders actually give you Tool type Generates Stops short of One-shot text-to-code A first draft from a single prompt Verifying it runs, fixing its own errors, a second coherent edit Chat-based code snippets Functions and components you copy in Anything outside the snippet: routing, schema, deployment Visual UI builders A styled interface Real backend logic, a database, form submission that persists data NEXUS AI App Builder A real Next.js and Prisma app, verified, previewed, shareable, deployable Nothing on this list. It is the full loop, in one workspace. The pattern in the first three rows is the same: something hands you code, then the responsibility for making it actually work lands back on you. The Builder is built to keep that responsibility on the agent for as long as possible. It edits files and verifies its own work The Builder is not a single prompt-to-code call. It is an agent with bounded file tools that reads and edits your actual project files, the same way a developer would. Y

The tech industry is realizing it needs to build agents based on what regular consumers want, not just what its AI models can do.

Everyone's talking about agentic AI shipping production code. Nobody's talking about what happens when you actually sit down and grade thousands of lines of it against a rubric, line by line, for months. I have. And the failure pattern that shows up over and over isn't the one Twitter/X is arguing about. The job title that didn't exist two years ago "AI evaluator." "AI trainer." "Expert contributor to frontier model training data." None of these existed as job titles when I started my career. Now they're where a chunk of the most interesting engineering signal in the industry is actually happening — quietly, behind NDAs, far from the demo videos. Here's what the job actually is: agentic coding outputs land on your desk, and you grade them against a structured rubric — correctness, instruction adherence, quality, edge-case handling. You design adversarial prompts to find where the model's reasoning breaks. You decide which checks can be programmatic and deterministic, and which genuinely need a human who's shipped production systems to make the call. This is RL environment design and LLMOps in its rawest form, and it's a completely different skill from "prompt engineer" or "ML researcher." It's closer to being a QA lead for a junior engineer who never sleeps, never gets embarrassed, and will confidently ship the wrong answer with perfect syntax. The pattern: agents are great at code, bad at consequences Here's the uncomfortable part. The failure mode people are loudest about — hallucinated APIs, made-up library functions — is the easy failure mode. It's loud, it's obvious, and any decent test suite catches it in seconds. The failure mode that actually matters, the one that slips past a surface read and even past a naive test suite, looks like this: The code is syntactically perfect and semantically wrong about failure. It handles the happy path beautifully and quietly assumes the retry, the timeout, the partial write, the duplicate message never happens. It optimises

Cloudflare built an AI agent workspace for its employees. Now it’s open source.

I still remember the frustration when our team's support bot, powered by LangGraph and MCP, couldn't retain context between user interactions. It was as if the bot had a case of conversational amnesia, forcing users to repeat themselves over and over. We later discovered that the issue stemmed from our lack of a centralized tooling server, making it impossible for the bot to access and leverage external tools in a scalable manner. This experience taught us the importance of building a robust MCP server to expose tools to our AI applications. In this post, we'll walk through the process of setting up an MCP server, focusing on exposing a single tool to any MCP-compatible AI app. Let's consider a simple tool that performs sentiment analysis on text input. We want this tool to be accessible from our support bot, allowing it to gauge user sentiment and respond accordingly. The first step in building an MCP server is to define the tool and its interface. MCP provides a set of APIs and protocols for tool definition, including the Tool class and the MCPTool interface. We'll use these to create our sentiment analysis tool. Here's a simplified example of how we might define this tool in Python: from MCP import Tool , MCPTool class SentimentAnalysisTool ( Tool , MCPTool ): def __init__ ( self ): super (). __init__ () self . name = " SentimentAnalysis " self . description = " Analyzes the sentiment of the input text " def execute ( self , input_text ): # Simplified sentiment analysis logic for demonstration if " love " in input_text or " great " in input_text : return " Positive " elif " hate " in input_text or " bad " in input_text : return " Negative " else : return " Neutral " # Create an instance of our tool sentiment_tool = SentimentAnalysisTool () Next, we need to set up an MCP server to host our tool. MCP servers can be configured to expose tools over various interfaces, including REST and gRPC. For simplicity, let's use a basic REST server. We'll use Flask, a lightweig