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(GA) to reuse your connectors in developer interfaces; Connectors in Workflows (Public Preview) allow for uninterrupted long-running tasks powered by all the tools you need Async agents are moving into everyday work. For an agent to be trustworthy and useful inside an organization, it needs real enterprise data: CRM records, repositories, inboxes, knowledge bases. Connecting an agent to that data in a demo is easy. Running it in production is where most setups stall. Production connectivity has

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AI and ML The Claude in Slack app is dead, long live Claude in Slack Anthropic is killing off its existing Claude in Slack app in favor of an always-on agentic alternative dubbed Claude Tag that will listen and learn from everything it’s given access to. Claude Tag can now join organizational Slack instances “as a team member,” according to Anthropic’s announcement, where it will have access to whatever channels domain administrators decide upon, as well as the tools, data, and codebases they co

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entire autoscaling, multi-replica service behaves under load. Of course, there is no universal configuration for inference. A low-latency classification endpoint and a multi-turn agentic loop do not want the same serving setup. Modal Auto Endpoints start you at the configuration we’d start from before pulling traces: clean, inspectable, benchmarked, and ready to tune against a workload. Engine-level observability Modal provides metrics (in-Dashboard and via OTEL export) to understand endpoint p

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Today we are releasing prime-rl version 0.6.0. This version enables us (and you) to train models of trillion-parameter scale on heavy agentic workloads at the highest efficiency. We have been relentlessly optimizing our RL infrastructure to maximize performance on large MoE models, reducing the cost, time and suffering required to post-train OSS models on agentic workflows. We are able to train GLM-5 on SWE tasks at up to 131k sequence length, with sub-5-minute step times and a batch size of 256

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Today we're introducing /goal in Grok Build. This new mode yields long-running, autonomous execution, helping you hand off larger implementation tasks to the agent. Available now in Grok Build. Install with a single command and sign in with your account: Install Grok Build Upgrade Introducing /goal Most coding sessions require back-and-forth execution and verification. With /goal , the agent continues until a task is completed and verified, whether that means reviewing code, inspecting webpages,

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ulation at 250 tok/s, using the token timing simulator from our LLM Engineer’s Almanac: Furthermore, they better preserve their acceptance lengths on very long context tasks, like agentic software engineering. Below, we explain why we’re bullish on speculation for LLM inference acceleration — and as part of the whole continuous improvement cycle for AI applications. But first, a tl;dr with the high-level takeaways. To first order, speculative decoding is the only engine optimization that matters

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s republished it twice as the investigation progressed, incorporating the latest information. With artifacts, team members and stakeholders don’t have to "walk us through what the agent found" because they're all looking at the same view, with the same context. Private to your organization Every artifact is private to its author by default. When you're ready, share it with your teammates and your organization directly from the page. Artifacts are viewable only by authenticated members of your or

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stack, context-rich apps that combine live Databricks warehouse data with information created and updated through the app. When an app built with Replit is ready to deploy, Replit Agent automatically provisions its Lakebase database, eliminating the need for manual setup. Automated Preview Deploys: Test your apps before they go live. Replit automatically creates a separate preview environment that keeps test data isolated from live business data in Databricks, giving teams a safe place to build,

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through Bedrock’s secure and reliable inference engine. Leading Performance in Enterprise Grok 4.3 is one of the strongest models available today for building enterprise-ready AI agents: 1 on Artificial Analysis Omniscience benchmark , achieving the lowest hallucination rate among frontier models. 1 on Artificial Analysis Tau2 Telecom benchmark , which evaluates real-world tool-calling performance in customer support agent scenarios. 1 on Vals AI Case Law and Corporate Finance benchmarks, which

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A framework for verifiable analysis of AI behavior Developing an AI agent is a complex data analysis problem. To know if the agent is working correctly, we need to track not just benchmark scores but the details of its behavior: how do the strategies change over the course of training? Why does the new scaffold perform worse? Is there reward hacking? Answering these questions requires a combination of quantitative and qualitative analysis tailored to the dataset at hand. Coding agents have the p

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をご覧ください。 開発の背景：研究と実装の統合 Sakana Marlinは、Sakana AIがこれまで蓄積してきた研究知見と実装経験を統合して開発したプロダクトです。 S研究領域では、科学的発見のプロセスを自動化する「AI Scientist」、複数のモデルを協調させて推論能力を高める「AB-MCTS」、アルゴリズムエンジニアリングを自動化する「ALE-Agent」などを発表してきました。同時に、国内の各産業へのAIエージェント実装をはじめとする実務適用を通じて、高度なワークフローをエージェントが自律的に実行する仕組みの構築を進めてきました。これらの長期推論・複数モデルの最適制御技術が、Sakana Marlinに結実しました。技術的な詳細はベータリリース時のブログでご覧いただけます。 【Nature誌掲載】アイデア創出から査読までの研究サイクルを自律完遂する「AIサイエンティスト」。この最先端の知見が、Marlinの高度なリサーチ能力を支えています。（Credit: Artwork by CERTO, Inc.） 約300名のβテスターとの協働 Sakana Marlinは、20

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ons are now fused into single Metal kernels via MLX’s just-in-time compiler features, and we’ve reworked Ollama’s GPU-backed sampling to run more efficiently. More responsive with agent workflows Agent workloads are dominated by prompt processing. Every tool call is a new request, and every request resends the whole transcript: system prompt, tool definitions, and every file read so far. Over a single task the model ends up processing the same context dozens of times. Prefix caching avoids the r

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MiMo Code is a terminal-based coding agent built by Xiaomi's MiMo team on top of OpenCode and open-sourced under the MIT license. It is designed for long-horizon automated programming tasks, with a core focus on how to maintain decision quality and state continuity over dozens or even hundreds of execution steps. This article introduces the core technical design of MiMo Code through three themes: computation, memory, and evolution. 1. Design Motivation The basic structure of a coding agent is to

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t announcements Build plugins for Claude Build plugins for Claude Build plugins for Claude Build plugins for Claude Sep 23, 2026 Claude Marketplace: one place to discover plugins, agents, and services from our partners Product announcements Claude Marketplace: one place to discover plugins, agents, and services from our partners Claude Marketplace: one place to discover plugins, agents, and services from our partners Claude Marketplace: one place to discover plugins, agents, and services from ou

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ion will fund your usage up to $10M until it does. We're calling it the AI Productivity Guarantee, and we hope other AI companies will move in a similar direction. How it works An agent reviews each completed Devin session and estimates two things: Did this session result in useful output?; If so: how long would a human engineer have taken to produce the same work? We measure in hours of productive output because lines of code don’t correspond to effort: a critical bug that takes hours to invest

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AI has moved from models to agents that go beyond solving individual problems and instead can execute on full broad scope projects through tool calls, sub-agent delegation, and iterations over long horizons. NVIDIA's Nemotron family is built for this paradigm. Nemotron 3 Ultra — a 550B-parameter Mixture-of-Experts model with 55B active parameters — is built for frontier reasoning and orchestration, with Nemotron 3.5 ASR for voice and Nemotron 3.5 Content Safety for guardrailing. Ultra sits at th

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Amp has a new UI and sidebar, built for watching and driving all of your Amp agents, on web, mobile, and CLI. We rebuilt Amp's foundation last month, so that your agents can run for longer, with less handholding needed. Amp is now a distributed system with durable execution for the agent loop and a plugin API. Now, with this new foundation in place, we're pulling ourselves back above ground to shape how you use Amp to get the most out of these models. We want you to be happy and productive runni

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AI AND ML Always-on agent promises to keep work moving, provided you trust it with practically everything Move over, Copilot: Microsoft is introducing a new category of agentic AI called "Autopilot," starting with Scout, its first agent. And it doesn't take much guessing to understand how Microsoft expects these things to operate: By constantly watching your every move and taking action in the background to ostensibly streamline your workday. Microsoft announced Autopilot, and the first Autopilo

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Claude Code can now write and orchestrate its own multi-agent harness on the fly. Here's how dynamic workflows work, and the patterns that get the most out of them. Last week, we released dynamic workflows in Claude Code. Claude can now write its own harness on the fly, custom-built for the task at hand. While the default Claude Code harness is built for coding, it is also useful for many other types of tasks because, as it turns out, many tasks resemble coding tasks. But there are certain class

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istral unveiled a new AI stack for industrial engineering, partnering with Airbus, BMW, and ASML to optimize design, simulation, and production while ensuring data security. Their agentic AI tool, Vibe, now handles long-horizon tasks like coding and research, while a new 10 MW data center in France will enhance compute control and security by 2026. These advancements reinforce Mistral’s focus on secure, high-impact AI solutions for critical enterprise workflows. Mistral for Industrial Engineerin

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