Kubernetes has always relied on agents, just not the kind dominating today’s AI conversations. A controller watches the current state, The post AI agents vs. Kubernetes: where each belongs in the control loop appeared first on The New Stack .
Autonomous agents are the hottest thing in AI. OpenAI, Meta, Google, Instinct, and others promise their agents can scour the internet on your behalf, doing anything from booking a dinner reservation and cleaning out your inbox to buying new shoes. But what happens when the rest of the internet won't play nice? In recent weeks, […]
Reverse Engineer Anything (REA) is an open-source project with over 200K GitHub stars that connects coding agents such as Claude Code and Codex to reverse-engineering tools like Ghidra, Hopper, and IDA. The project lets developers inspect compiled binaries, trace function calls, and investigate how applications implement specific features. By Renato Losio
The deployment of a model or an agent is just the first step. Once it starts processing requests, you need a way to track how often the model is called, how long requests take, how many tokens are used, and whether there are errors. For agents, we also want to know what's happening at each step. Gemini Enterprise Agent Platform offers various methods for gaining such visibility.
Welcome to another edition of Road to KubeCon, where we’re tracking interesting movements in the cloud-native and Kubernetes ecosystem as The post AI agents turn one request into about 20. Kubernetes is evolving to handle it. appeared first on The New Stack .
Grafana Labs is proposing a familiar reliability engineering approach for one of the less familiar problems in modern software: how to measure whether an AI agent is actually behaving well. By Craig Risi
AWS has introduced two open-source projects for AI agent development: Strands Decider 2B, a small model designed to make decisions within agent workflows, and Strands Box, a sandbox for controlling agent execution. Both projects address different aspects of building agents that use tools, execute code, and interact with external systems. By Daniel Dominguez
In LLM-based multi-agent systems (MAS), a local mistake can propagate as other agents build on it, turning a correctable error into a failed task. Diagnosing completed runs or repeatedly re-executing them to search for corrections does not provide a learned policy for intervening while execution is still in progress. We present Agent Runtime Inspection and Amendment (ARIA), a framework for learning when and how to repair ongoing multi-agent executions.
Multi-repo support in Slack and Microsoft Teams GitHub Copilot in Slack and Microsoft Teams now supports working across multiple repositories in a single conversation. Whether you’re tracking pull requests, investigating… The post Multi-repo support and other improvements for GitHub Copilot in Slack and Microsoft Teams appeared first on The GitHub Blog .
Anant Kumar builds tools to compare loan valuations and analyze credit agreements, using Codex for code review and faster follow-ups.
How one business development representative clears his inbound queue and drafts bilingual customer emails with Claude Managed Agents and Claude Tag.
Tesla Roadster 🚗, Muse Spark Open ✨, internal vibe coding 👨💻
How do you review code at the volume agents produce it? The Claude Code team shares its review process, from Claude verifying its own work to risk-tiered human approval, plus a checklist for adopting it.
We will deprecate the following models across all GitHub Copilot experiences (including Copilot Chat, inline edits, ask and agent modes, and code completions) on November 16th, 2026: Model Deprecation date… The post Upcoming deprecation of selected GitHub Copilot models in mid-November appeared first on The GitHub Blog .
Meta launches Muse Spark Open, a coding-optimized 816B mixture-of-experts (MoE) model with 42B active parameters and a 1M token context window. Trained on...
Stronger language models may increasingly construct useful procedures for themselves, reducing the advantage of developer-supplied scaffolding. We investigate why this advantage persists and whether an environment-agnostic agent can be improved without environment-specific engineering. We evaluate a Codex-based agent in two nearly identical prompt variants on ARC-AGI-3, AGI Maze, and Terminal-Bench.
There's a growing trend in the consumer tech world: gadgets designed to spy on everything and everyone around you, regardless of whether or not anyone else actually consented to it. Now, with the advent of "AI personal assistants" like Meta's Muse, you're being cheerfully asked not just to give up your own data, but also […]
We'll likely learn more about how this will work at the company's upcoming "AI & Autonomy Day" event in December.
Agentic autofix for code scanning alerts is now generally available. It remediates alerts by working across your codebase the way a developer would: it explores relevant files, proposes a fix,… The post Agentic autofix for code scanning alerts general availability appeared first on The GitHub Blog .
Vector stores have been part of the Python experience in Semantic Kernel for some time. With Microsoft Agent Framework, we have ported that work forward rather than starting over. This post focuses on Python. For the .NET equivalent, see Use vector stores in .NET AI apps. The foundation remains familiar: typed record definitions, stores and […] The post Vector stores in Agent Framework for Python: familiar foundations, streamlined for agents appeared first on Microsoft Agent Framework .
Netflix plugged JetBrains’ headless code-intelligence engine – the same one behind IntelliJ IDEA – into Claude Code and Codex, then benchmarked it against a variety of scenarios. The details and numbers are available below. At a glance Company Netflix – Application Development Platform (ADP), GenAI Platform, and Agentic SDLC teams Challenge Coding agents were struggling […]
Catch more bugs before they reach production.
Linux creator and longtime lead maintainer Linus Torvalds turned to Anthropic’s Claude Code to tackle a kernel performance problem that The post Linus Torvalds let Claude Code fix the Linux page cache. Then reviewers found a race. appeared first on The New Stack .
Learn the top AI agent security risks, from prompt injection to excessive autonomy and credential exposure, and how to contain them.
Most companies use AI agents to ship products faster or speed up workflows. Apate.ai sends them undercover to get scammed.
The problem Floqer is trying to solve is familiar to anyone who has worked in sales or revenue operations.
Epic Games is announcing a big refresh of its PC games launcher today in preview, saying that the new version is "way faster" and now supports Linux. But the company, which has been publicly making a big push into AI-assisted development, also notes that the refresh was "vibe coded." "Programming is hard," Epic says in […]
See how Amp pushes the frontier of what agents can do with GPT-6 Astra, Ultrafast, GPT-Live-1, and Sign in with ChatGPT.
TV4 postpones show after AI agent erases edited material
LLM agents' capabilities depend not only on their underlying models but also on their execution harnesses. Recursive self-improvement (RSI) offers a promising approach to automatically evolve these harnesses. However, deploying RSI in open-world settings still faces two critical challenges. First, open-world tasks are typically unseen during the harness design phase and novel at test time, leaving no ground-truth verifiers to reliably guide online harness edits.
Type at least 2 characters
This site uses essential cookies for functionality and analytics cookies to improve your experience. You can accept all, essential only, or customize. Cookie Policy | Privacy Policy
