全部 AI 动态

旧金山将举办编码智能体工程主题开发者交流会

该线下交流会定于10月14日在旧金山举行，由主办方与 Jesse Vincent 共同发起，专为利用编码智能体（coding agents）进行创新与前沿实验的开发者打造。活动采用非正式的研讨展示（Birds of a Feather）形式，重点聚焦未公开的技术实验、踩坑经验及未商业化的半成品项目，旨在为一线构建者提供交流智能体工程实践心得与探索方向的平台。

Introducing Amazon CloudWatch Omni: collaborative AI-powered observability for your applications

Amazon CloudWatch Omni is the next evolution of CloudWatch — unified observability that brings your applications and AI agents into one reimagined experience, with auto-discovered topology, natural language queries, and AI-guided investigation powered by AWS DevOps Agent.

Introducing Amazon CloudWatch Omni: AI-powered observability for generative AI and agentic workloads

Learn how Amazon CloudWatch Omni delivers AI-powered observability purpose-built for generative AI and agentic workloads. Trace, evaluate, and experiment with AI agents across any framework—directly from your IDE or a standalone web experience—using open standards and built-in evaluators for quality, correctness, and coherence.

Claude Opus 5.5 is now available on AWS

Claude Opus 5.5, Anthropic's most capable Opus model for agentic coding, knowledge work, and long-running tasks, is now available on Amazon Bedrock and Claude Platform on AWS. This post covers what's new in Opus 5.5, practical guidance, and how to start building with the model on Amazon Bedrock.

Evaluate skill-equipped agents with Strands Evals and Amazon Bedrock AgentCore

Skills let you encode domain-specific procedures as reusable, portable instructions for agents, but a fluent answer doesn't prove the agent picked the right skill or followed it. Learn how to measure skill selection and instruction following with Strands Evals and Amazon Bedrock AgentCore Evaluations.

How Reactiv automates mobile commerce 80% faster with Amazon Bedrock AgentCore

Reactiv used Amazon Bedrock AgentCore to build a multi-agent AI Scheduler that autonomously refreshes Shopify merchants' mobile apps on a schedule, reducing merchant configuration time by 80% and getting to production 33% faster.

How Trane gets building insights 60x faster with Amazon Bedrock AgentCore

In about four weeks, Trane Technologies built an AI-powered agentic solution on Amazon Bedrock AgentCore that reduced a 20-minute, multi-screen building diagnostic workflow to a 20-second natural language interaction, a 60x improvement in time-to-insight. This post shares the architectural approach and key design decisions behind the solution.

Extending public sector intelligence with Agentforce and AWS

Public sector agencies process large volumes of unstructured evidence, such as body camera footage and scanned documents. This post shows how to combine Amazon Bedrock Data Automation with the Model Context Protocol (MCP) to turn that data into structured insights and surface them through natural language queries in Salesforce Agentforce.

Introducing Worker Previews: Isolated preview environments for every change your agent makes

Worker Previews gives every branch its own URL, configuration, state, and observability, so you and your agents can test changes in parallel without affecting production.

NVIDIA Isaac ROS 5.0 Advances Agentic, Open Source Robotics Development

To build and deploy sophisticated robotics applications that can perceive, reason and act in dynamic environments, developers need new physical AI models and tools. The ROS open framework is a project from Open Robotics that helps humans build robots. NVIDIA Isaac ROS 5.0 — a collection of GPU-accelerated packages built on ROS, released today at […]

Parallel cut research time and cost in half with GPT‑6 Astra

GPT‑6 Astra allowed Parallel’s agents to research and synthesize labor-market data in half the time and at half the cost vs. prior models.

Accelerating a ROS 2 Node with an AI Agent and NVIDIA Isaac ROS

GPU acceleration can speed up compute-intensive robotics workloads, but a fast CUDA kernel alone does not guarantee a fast ROS 2 graph. As messages move between...

How to Evaluate AI Agents From Tool Calls to Task Completion

When you ship an AI agent, the key question is whether it can execute a chain of work across dozens of sequential tool calls against a live environment, and...

xAI’s Grok 4.6 is now available in Amazon Bedrock

xAI's Grok 4.6 is now available in Amazon Bedrock: a frontier model for long-running agents, coding, and knowledge work, with a 500K token context window and four reasoning effort levels. It runs on both the bedrock-mantle and bedrock-runtime endpoints, with Converse API and cross-Region inference support.

How Benchling secured multi-tenant AI agents with Amazon Bedrock AgentCore

Learn how Benchling built a defense-in-depth security architecture to run untrusted, AI agent-generated scientific code across thousands of life sciences tenants using Amazon Bedrock AgentCore Code Interpreter in VPC mode, combined with Amazon Route 53 Resolver DNS Firewall and VPC endpoint policies to block data exfiltration, including through DNS.

Announcing vllm-metal: Concurrent Serving on Apple Silicon

vllm-metal brings vLLM's paged, continuously batched serving stack to Apple Silicon, with flatter TTFT under concurrent agent load, batched MTP, and automatic M

AI Security Is an Engineering Problem — How to Solve It at Every Layer of the Agent Stack

AI security is an engineering problem. That means defined security requirements, enforceable controls, named owners and evidence that protections work. As AI becomes more capable, the industry must accelerate security engineering, broaden access to defensive tools and share what works faster. Technology Changes, Security Fundamentals Endure The internet and cloud computing changed how software operates, […]

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.

MCP was always a bad idea?

My comment on MCP was always a bad idea? — Hacker News. This article entirely misses the value that MCP brings today. Sure, there's almost no reason to use MCPs if you are running a full-blown terminal agent (Claude Code, Codex, Meta Muse, OpenClaw etc) with unfettered internet access - just let it call APIs directly. If you want to operate something that's less YOLO than that, you'll find yourself wanting: Control over exactly which external services it can access A way to handle authentication that doesn't allow the agent to directly access API keys A sensible UI to allow users to connect and authenticate further services Strong audit loggi

llm-keys-ui 0.1

Release: llm-keys-ui 0.1 This plugin solves a very specific problem. I've started using Codex Remote to run coding agents on various machines while controlling them from my phone. Sometimes I use those machines to hack on LLM projects, and occasionally that means I need to configure an API key. I don't like pasting API keys into agent sessions, so I wanted a way to get those keys onto a machine without pasting them into the ChatGPT app directly. With this plugin, I can tell Codex to run: uvx --with llm-keys-ui llm keys-ui --all Then have it tell me the URL - including local network or Tailscale device IPs - for an interface to save additional

Jev-as-a-Judge for Agent Evals

datasette-auth-github 1.0

Release: datasette-auth-github 1.0 I run this GitHub login plugin on the agent.datasette.io demo site and I noticed that my authenticated sessions weren't lasting very long. It turned out that the plugin was setting cookies without a Max-Age parameter, so they were expiring at the end of a browser session (which in Mobile Safari seems to happen pretty often, independently of how you are using the app.) I fixed that in #80 and, since this plugin has been around for quite a while and is tested against both Datasette 0.65.x and Datasette 1.0ax, I decided to bump it up to a 1.0 release. I'm trying to get better at promoting stable plugins to 1.0.

Quoting Thariq Shihipar

We're adding support for AGENTS.md to Claude Code. Starting today in version 2.1.277, if there is no CLAUDE.md in a folder, Claude will check for and use AGENTS.md. AGENTS.md support is built off of Claude Code mods, our upcoming way to customize the Claude Code harness. This is a built-in mod, but you’ll be able to build custom versions of project instructions yourself as you’d like too. You can see the source for the mod here ! — Thariq Shihipar , there are more mods here Tags: thariq-shihipar , coding-agents , anthropic , claude-code , generative-ai , ai , llms

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 -

Wake up, people. What we should actually fear, near term, is not so much rogue superintelligence as unleashed agentic AI causing hacking the internet at scale.

Rome is burning and people are fantasizing about Skynet.

Self-generated prompt injections in compaction summaries

Self-generated prompt injections in compaction summaries In Our framework for reporting model misalignment OpenAI provide "six reports on unexpected or concerning model behavior we’ve observed in the last six months". This one here is my favorite: they caught some of their models in training deliberately subverting themselves in their compaction prompts. Compaction is the process agent systems use when they are running out of tokens in their context window, so they summarize everything that has gone before so they can keep going with more token headroom. In one of the observed instances, a model undergoing reinforcement learning was working o

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 .