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Components of A Coding Agent

Components of A Coding Agent How coding agents use tools, memory, and repo context to make LLMs work better in practice In this article, I want to cover the overall design of coding agents and agent harnesses: what they are, how they work, and how the different pieces fit together in practice. Readers of my Build a Large Language Model (From Scratch) and Build a Large Reasoning Model (From Scratch) books often ask about agents, so I thought it would be useful to write a reference I can point to. More generally, agents have become an important topic because much of the recent progress in practical LLM systems is not just about better models, but about how we use them. In many real-world applications, the surrounding system, such as tool use, context management, and memory, plays as much of a…

Migrating a text agent to a voice assistant with Amazon Nova 2 Sonic

Artificial Intelligence Migrating a text agent to a voice assistant with Amazon Nova 2 Sonic Migrating a text agent to a voice assistant is increasingly important because users expect faster, more natural interactions. Instead of typing, customers want to speak and understand in real time. Industries like finance, healthcare, education, social media, and retail are exploring solutions with Amazon Nova 2 Sonic to enable natural, real-time speech interactions at scale. In this post, we explore what it takes to migrate a traditional text agent into a conversational voice assistant using Amazon Nova 2 Sonic. We compare text and voice agent requirements, highlight design priorities for different use cases, break down agent architecture, and address common concerns like tools and sub-agents for reuse and system prompt adaptation. This post helps you navigate the migration process and avoid common pitfalls. You can…

Building Workforce AI Agents with Visier and Amazon Quick

Artificial Intelligence Building Workforce AI Agents with Visier and Amazon Quick Employees across every function are expected to make faster, better-informed decisions, but the information that they need rarely lives in one place. Workforce intelligence (who is in your organization, how they are performing, and where the gaps are) is one of the most valuable signals an enterprise has, and platforms like Visier are purpose-built to surface it. However, that intelligence only reaches its full value when it’s connected to the internal policies, plans, and context that give it direction. That context also often lives somewhere else entirely. Amazon Quick is the Agentic AI workspace where that connection happens. It brings together enterprise knowledge, business intelligence, and workflow automation. Its intelligent agents retrieve information and reason across all of these layers simultaneously, interpreting live data alongside organizational context to produce…

From hours to minutes: How Agentic AI gave marketers time back for what matters

Artificial Intelligence From hours to minutes: How Agentic AI gave marketers time back for what matters Your marketing team loses hours to page assembly, coordination emails, and review cycles. These manual workflows keep teams from their most important work: identifying what problems customers face, crafting messages that resonate, and building campaigns that drive meaningful engagement. In this post, we share how AWS Marketing’s Technology, AI, and Analytics (TAA) team worked with Gradial to build an agentic AI solution on Amazon Bedrock for accelerating content publishing workflows. The solution reduced webpage assembly time from up to four hours to approximately ten minutes (a reduction of over 95%) while maintaining quality standards across enterprise content management systems (CMS). Our marketing teams can now publish content faster and more consistently, freeing them to focus on finding more effective ways to reach and serve…

How a Healthcare Provider Cuts Nurse Intake Work by 80% with Agentic AI Discover how healthcare automates patient intake using agentic AI, cutting nurse intake time by up to 80% to improve efficiency and patient experience. Alex Clay Apr 22, 2026

How a Healthcare Provider Cuts Nurse Intake Work by 80% with Agentic AI Discover how healthcare automates patient intake using agentic AI, cutting nurse intake time by up to 80% to improve efficiency and patient experience. Manual Intake Overloads Nurses and Patients Three nurses spend four hours daily on patient intake at many healthcare providers. These clinicians spend a third of their shift reading, assessing insurance eligibility, and routing forms instead of delivering care. It exhausts staff and costs money. When eligibility checks lag or forms are misrouted, patient satisfaction falls and costs rise. Intake Bottlenecks Waste Thousands of Nursing Hours On average, nurses at large health systems spend 4 hours each day on intake forms. When handling thousands of patients, this wastes thousands of nursing hours weekly. Manual workflows cause insurance verification errors above 20%, triggering denials and delayed…

How One E-Commerce Giant Automates Returns and Refunds with Agentic AI Alex Clay Apr 17, 2026

How One E-Commerce Giant Automates Returns and Refunds with Agentic AI E-commerce returns automation cuts costs and boosts refunds with multi-agent AI handling classification, verification, and response drafting efficiently. Returns Were a Cost Black Hole This major e-commerce company handles 50,000 orders daily. That means thousands of returns and refund requests overwhelm customer support. Historically this has been slow, inconsistent, and exhausting. They spent $2 million yearly on staff just to keep up, yet still faced errors, delays, and frustrated shoppers. Returns aren’t a side problem. Automated solutions struggle because returns involve order history, shifting policies, fraud risk, and customer nuances. Manual or rigid systems collapse under complexity. Return rates from 15% to 30% in some sectors increase costs, often $20 per return just to process. The pressure to cut costs while improving service is intense. A Crew of Specialist…

Agent Harnesses Are Dead. Long Live Agent Harnesses. João (Joe) Moura Apr 14, 2026

Agent Harnesses Are Dead. Long Live Agent Harnesses. I said it at a Dev Day 2025 (DeepLearning.AI conference) last year: Frameworks are cheap. A few people in the audience looked uncomfortable. But I think the statement aged well. Now it's not just frameworks. Harnesses are getting the same treatment, and the cycle is only getting faster. Building anything is getting cheaper by the month. Vibe-code an app over the weekend. Spin up an agent with a few API calls. The distance between idea and working prototype has collapsed to almost nothing, and will collapse to nothing faster than most expect. And yet the industry is spending most of its energy debating what to call the building tools. We built our harness back in 2023 when we launched, what people now all agree to be the right abstraction, of multi agentic…

How a Global CPG Automates Supply Chain Demand Forecasting with Agentic AI Alex Clay Apr 14, 2026

How a Global CPG Automates Supply Chain Demand Forecasting with Agentic AI Discover how CPG supply chains use agentic AI to automate demand forecasting, boosting accuracy and speed while cutting manual effort. Excel-Built Forecasts Stall Supply Chains In a leading global beverage company, weekly demand forecasting felt like a spreadsheet battlefield. Demand planners stitched data from SAP, Databricks, and scattered Excel files. The manual cycle was slow, clunky, and error-prone. Forecast accuracy hovered around 70%. A 30% SKU-level error causes costly stockouts or bloated inventory. This problem spans the industry,CPG forecasting errors cost billions, erode margins, and hurt customer satisfaction. Why Manual Forecasting Fails Supply Chains This multinational brewer, with dozens of brands, struggled with Excel and siloed data. The fractured picture stifled supply chain agility. Forecasting was guesswork masked as science, with 25-35% errors annually. Global inventory distortion costs…

How Enterprise AI SaaS Closes Adoption Gaps with Multi-Agent Crews João (Joe) Moura Apr 6, 2026

How Enterprise AI SaaS Closes Adoption Gaps with Multi-Agent Crews Enterprise AI SaaS automates customer enablement with a 5-agent workflow to close adoption gaps, reduce churn, and scale training across industries Most enterprise AI customers barely use what they paid for Here's a pattern I keep seeing: company buys an AI platform, gets through a painful onboarding, builds one or two use cases, then stalls. The FDE team is stretched across too many accounts to go deep on any of them. Adoption takes six months when it should take weeks. By the time the customer sees real value, if they ever do, renewal is already at risk. The instinct is to throw more training at it. More docs, more workshops, more enablement calls. It doesn't work. You can't train your way out of a product that's hard to adopt. Manual…

You're building agent security in the wrong order João (Joe) Moura Apr 2, 2026

You're building agent security in the wrong order The agent security market woke up. In two weeks, I've seen companies shipping runtime identity enforcement for autonomous agents, a full platform for discovering shadow agents, revoking permissions in real time, and much more. Serious teams behind them. I respect the work. But they're all solving step three. The sequence problem I've spent the last two years watching enterprise teams deploy agents and we've processed billions of executions at CrewAI, it seems most of the pattern is similar for sequencing problems. A team gets the mandate: "deploy AI agents." The CISO immediately asks about security. The board wants compliance answers. So the team starts there: IAM policies, authorization scopes, runtime monitoring. They build a security stack around their agents.Then they actually try to run the agents. And the agents can't reliably find…

CrewAI Selected for the Enterprise Tech 30 João (Joe) Moura Mar 31, 2026

CrewAI Selected for the Enterprise Tech 30 Year One: Vision. Year Two: Proof. For the second year in a row, CrewAI has been selected for Enterprise Tech 30! Exciting! If you're not familiar with the ET30, here's what makes it different from every other "top companies" list: it's not editorial picks, it's not pay-to-play, it's a structured vote by 98 investors across 85 firms managing a combined $2.6 trillion in assets, between small and extremely large organizations, these are the investors who fund the companies building the next decade of enterprise technology and they vote based on what they're seeing in the market. This year, CrewAI was listed under Agent Development within the AI Infrastructure & Development category, that category now represents 43% of the entire ET30, in 2019, it was 0%. Let that land for a second, zero to…

CrewAI OSS 1.0 - We are going GA

1.4 Billion Agentic Automations, 60 % of the Fortune 500, 40k GitHub stars. CrewAI OSS is going GA During our first year at CrewAI, we set ourselves a goal. Build the framework capable of orchestrating a billion autonomous agents — securely, reliably, at scale. Little over a year later, we’ve done more than that: Today we’re announcing CrewAI OSS v1.0, the same open-source core now powering 1.4 billion Agentic Automations across the world’s largest enterprises, on all degrees of complexity. Two years ago we bet that large-language models shouldn’t be micromanaged—they should be delegated real work. Last week, Andrew Ng called us visionaries for betting early on one idea: As models get better, engineers will remove the scaffolding [all rules and forced structure] and delegate more, not less. Andrew Ng invested in CrewAI over a year ago — joined by…

11/19/2025 50 Trillion Tokens Per Day: The State of Agent Environments

TL;DR — Agents and LLMs are processing 1.5 quadrillion token per month, and reached a massive scale over the past year. But the real story for the next 12 months isn't about which models are smartest—it's about the complex production environments where agents actually do work, optimizing not only the underlying models but the tools, workflows, and data in their environments. What emerges is a clear hierarchy where the ability to create high-quality environments is a determinant of market success—the companies building complete environments rather than just LLM wrappers are capturing the most value. For the last two years, the conversation around AI agents has been dominated by potential. Today, that conversation has fundamentally shifted from potential to production. Businesses have moved beyond prototyping, shipping agents that handle customer support, write enterprise-quality code, and manage complex workflows at scale. The…

6/9/2025 Reinforcement Fine Tuning (Beta): Train expert open models to surpass closed frontier models

Today, we’re excited to announce the beta release of Reinforcement Fine-Tuning (RFT), a powerful new technique to create expert models for complex tasks across agentic reasoning, function calling, coding, and more. RFT can improve model quality with just a few examples. Compared with closed frontier models, our alpha users have been able to train open models to: Fireworks makes it easy to train expert models with RFT, by specifying an evaluator function that grades model outputs, with no infrastructure setup required! RFT on Fireworks supports frontier open models like Llama, Phi3/4, Qwen 2.5/3 and even DeepSeek V3 and R1. You can get started here. Training models using RFT on Fireworks is free of charge for the next 2 weeks! RFT works best for tasks with clear answers that can be graded or verified for correctness, by building on the concept…

User Story Bilge Yücel DevRel Engineer Kelsey Sorrels Data Scientist at Telus AG How TAC Built an Agentic Chatbot with Haystack to Transform Trade Promotions Workflows See how TELUS Agriculture & Consumer Goods (TAC) gives users unprecedented access to their data with safety in mind October 6, 2025

How TAC Built an Agentic Chatbot with Haystack to Transform Trade Promotions Workflows See how TELUS Agriculture & Consumer Goods (TAC) gives users unprecedented access to their data with safety in mind October 6, 2025When a leading company like TELUS Agriculture & Consumer Goods (TAC), with a strong presence in agriculture and consumer goods, turns to AI to streamline complex processes, it’s worth taking a closer look. TELUS Agriculture & Consumer Goods helps businesses optimize everything from supply chains to retail operations. One of their latest innovations: an agentic chatbot powered by Haystack that simplifies how users interact with their trade promotions platform. We sat down with the team behind this project to learn how they built it, why they chose Haystack, and what advice they have for other teams looking to implement Retrieval-Augmented Generation (RAG) and agent-based AI solutions…

MachinaCheck: Building a Multi-Agent CNC Manufacturability System on AMD MI300X

MachinaCheck: Building a Multi-Agent CNC Manufacturability System on AMD MI300X The Problem We Solved Walk into any small CNC machine shop and ask the manager how they decide whether to accept a customer job. The answer is almost always the same: they print the drawing, read every dimension by hand, walk around the shop checking which tools are available, estimate whether their machines can hold the required tolerances, and write notes on a clipboard. The whole process takes 30 to 60 minutes per drawing. For a busy shop receiving 10 to 20 RFQs per week, that is 5 to 20 hours of skilled manager time spent on feasibility analysis alone. Sometimes they get it wrong. They accept a job, start production, and discover halfway through that they don't have the right tap or that their mill cannot hold the tolerance…

Unlocking Agentic RL Training for GPT-OSS: A Practical Retrospective

Unlocking Agentic RL Training for GPT-OSS: A Practical Retrospective LinkedIn is an AI-first company that's built agents to help professionals be more successful. In this setting, models must reason over incomplete information, interact with structured services, and adapt to evolving user intent across multiple steps rather than produce a single static response. These capabilities are especially critical for agents that support the goals of recruiters, job and knowledge seekers, and learners end users, such as retrieving information, refining queries, coordinating tools, and executing multi-step workflows. By learning robust decision policies through interaction, agentic RL provides a principled foundation for building scalable, reliable, and adaptable AI systems through end-to-end optimization. The GPT-OSS model has shown comparable performance to OpenAI o3-mini and o4-mini [ref], but its suitability for agentic reinforcement learning training has not yet been validated. Most recent work focuses on…

Open Responses: What you need to know

Open Responses: What you need to know The era of the chatbot is long gone, and agents dominate inference workloads. Developers are shifting toward autonomous systems that reason, plan, and act over long-time horizons. Despite this shift, much of the ecosystem still uses the Chat Completion format, which was designed for turn-based conversations and falls short for agentic use cases. The Responses format was designed to address these limitations, but it is closed and not as widely adopted. The Chat Completion format is still the de facto standard despite the alternatives. This mismatch between the agentic workflow requirements and entrenched interfaces motivates the need for an open inference standard. Over the coming months, we will collaborate with the community and inference providers to implement and adapt Open Responses to a shared format, practically capable of replacing chat completions. Open Responses…

NVIDIA brings agents to life with DGX Spark and Reachy Mini

NVIDIA brings agents to life with DGX Spark and Reachy Mini Today at CES 2026, NVIDIA unveiled a world of new open models to enable the future of agents, online and in the real world. From the recently released NVIDIA Nemotron reasoning LLMs to the new NVIDIA Isaac GR00T N1.6 open reasoning VLA and NVIDIA Cosmos world foundation models, all the building blocks are here today for AI Builders to build their own agents. But what if you could bring your own agent to life, right at your desk? An AI buddy that can be useful to you and process your data privately? In the CES keynote today, Jensen Huang showed us how we can do exactly that, using the processing power of NVIDIA DGX Spark with Reachy Mini to create your own little office R2D2 you can talk to…

DeepMath: A lightweight math reasoning Agent with smolagents

DeepMath: A lightweight math reasoning Agent with smolagents By Intel AI Software Group DeepMath is an aligned math reasoning agent built on Qwen3-4B Thinking and fine-tuned with GRPO (Group Relative Policy Optimization). Instead of verbose text, the model emits tiny Python snippets for intermediate steps, runs them in a secure sandbox, and folds the results back into its reasoning, reducing errors and output length. The agent is implemented using the smolagents library. We evaluate DeepMath on four math datasets: MATH500, AIME, HMMT, and HLE, and show that: 🤖 The math agent alone reduces output lengths by up to 66%, while often improving accuracy. ⚡ GRPO training improves the agent performance even further, in almost all benchmarks. 👉 Code and evaluation scripts: https://github.com/IntelLabs/DeepMath 👉 Model: https://huggingface.co/Intel/deepmath-v1 Why DeepMath? Large language models (LLMs) have advanced reasoning capabilities, but mathematical problem-solving remains challenging;…

Aligning to What? Rethinking Agent Generalization in MiniMax M2

Aligning to What? Rethinking Agent Generalization in MiniMax M2 The Real Agent Alignment Problem: Benchmarks or Reality? If you've worked with LLM Agents, you've felt this pain: the same model can feel brilliant in one framework and useless in another. An agent might crush a tool-use leaderboard but fail spectacularly at a simple, real-world task. This gap between benchmark performance and practical usability is one of the biggest challenges in the field. When we designed M2, we knew we had to tackle this problem head-on. This led us to two core, and sometimes conflicting, objectives: - Excel on Open-Source Benchmarks. Benchmarks are essential for measuring "pure" capabilities. A benchmark like BrowseComp, for instance, tests for sophisticated search skills. While users will rarely ask a question as contrived as, "Find the paper where the third letter of the nth author's name…

Building the Open Agent Ecosystem Together: Introducing OpenEnv

Building the Open Agent Ecosystem Together: Introducing OpenEnv Agentic environments define everything an agent needs to perform a task: the tools, APIs, credentials, execution context, and nothing else. They bring clarity, safety, and sandboxed control to agent behavior. These environments can be used for both training and deployment, and serve as the foundation for scalable agentic development. The Problem Modern AI agents can act autonomously across thousands of tasks. However, a large language model isn’t enough to get those tasks to actually run — it needs access to the right tools. Exposing millions of tools directly to a model isn’t reasonable (or safe). Instead, we need agentic environments: secure, semantically clear sandboxes that define exactly what’s required for a task, and nothing more. These environments handle the critical details: - Clear semantics about what a task needs - Sandboxed execution…

Accelerating Qwen3-8B Agent on Intel® Core™ Ultra with Depth-Pruned Draft Models

Accelerating Qwen3-8B Agent on Intel® Core™ Ultra with Depth-Pruned Draft Models TL;DR: Qwen3-8B is one of the most exciting recent releases—a model with native agentic capabilities, making it a natural fit for the AIPC. With OpenVINO.GenAI, we’ve been able to accelerate generation by ~1.3× using speculative decoding with a lightweight Qwen3-0.6B draft. By using speculative decoding and applying a simple pruning process to the draft, we pushed the speedup even further to ~1.4× We wrapped this up by showing how these improvements can be used to run a fast, local AI Agent with 🤗 smolagents Qwen3 Qwen3-8B is part of the latest Qwen family, trained with explicit agentic behaviors. It supports tool invocation, multi-step reasoning, and long-context handling capabilities, that make it well-suited for complex agent workflows. When integrated with frameworks like Hugging Face 🤗smolagents, QwenAgent, or AutoGen, it enables…

ScreenEnv: Deploy your full stack Desktop Agent

ScreenEnv: Deploy your full stack Desktop Agent What is ScreenEnv? Imagine you need to automate desktop tasks, test GUI applications, or build an AI agent that can interact with software. This used to require complex VM setups and brittle automation frameworks. ScreenEnv changes this by providing a sandboxed desktop environment that runs in a Docker container. Think of it as a complete virtual desktop session that your code can fully control - not just clicking buttons and typing text, but managing the entire desktop experience including launching applications, organizing windows, handling files, executing terminal commands, and recording the entire session. Why ScreenEnv? - 🖥️ Full Desktop Control: Complete mouse and keyboard automation, window management, application launching, file operations, terminal access, and screen recording - 🤖 Dual Integration Modes: Support both Model Context Protocol (MCP) for AI systems and direct Sandbox…

Import AI 447: The AGI economy; testing AIs with generated games; and agent ecologies

Import AI 447: The AGI economy; testing AIs with generated games; and agent ecologies What might a superintelligence arcology be like? Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv and feedback from readers. If you’d like to support this, please subscribe. The AGI economy - most labor goes to the machines, and humans shift to verification: …What grappling with the singularity seriously looks like… Researchers with MIT, WashU, and UCLA have written a fun paper called “Some Simple Economics of AGI” which wrestles with what happens when machines can do the vast majority of tasks in the economy. The conclusion is that our ability as humans to control and benefit from this vast machine-driven economy will rely on allocating our ability toward monitoring and verifying the actions of our myriad AI agents, and indulging…

Import AI 443: Into the mist: Moltbook, agent ecologies, and the internet in transition

Import AI 443: Into the mist: Moltbook, agent ecologies, and the internet in transition Plus, a story about agents corrupting other agents Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv and feedback from readers. If you’d like to support this, please subscribe. Import A-Idea: An occasional essay series: Into the mist: Moltbook, agent ecologies, and an internet in transition We’ve all had that experience of walking into a conversation and initially feeling confused - what are these people talking about? Who cares about what? Why is this conversation happening? That’s increasingly what chunks of the internet feel like these days, as they fill up with synthetic minds piloting social media accounts or other agents, and talking to one another for purposes ranging from mundane crypto scams to more elaborate forms of communication. So, enter…

PlugMem: Transforming raw agent interactions into reusable knowledge

At a glance - Today’s AI agents store long interaction histories but struggle to reuse them effectively. - Raw memory retrieval can overwhelm agents with lengthy, low-value context. - PlugMem transforms interaction history into structured, reusable knowledge. - A single, general-purpose memory module improves performance across diverse agent benchmarks while using fewer memory tokens. It seems counterintuitive: giving AI agents more memory can make them less effective. As interaction logs accumulate, they grow large, fill with irrelevant content, and become increasingly difficult to use. More memory means that agents must search through larger volumes of past interactions to find information relevant to the current task. Without structure, these records mix useful experiences with irrelevant details, making retrieval slower and less reliable. The challenge is not storing more experiences, but organizing them so that agents can quickly identify what matters in…

Announcing SAP’s strategic investment in n8n

Today we are announcing that SAP has invested in n8n. The investment values n8n at $5.2bn, more than double our valuation from less than a year ago. Alongside the investment, we are also embedding n8n natively inside SAP's Joule Studio. It's a significant moment, and I want to share what it means. I started n8n almost seven years ago. Since then, the community that's grown around it (now 1.7 million monthly active builders) is what's shaped the platform. What's moved fastest this past year is enterprise adoption: more than 1,400 enterprise customers, including Fortune 500 teams running mission-critical processes. This partnership is one of the first moves toward bringing n8n closer to the systems our enterprise customers run on, starting with SAP. SAP is one of the most trusted names in enterprise software, 99 of the 100 largest companies in…

n8n Partners with SAP to bring Visual AI Workflow Orchestration to Enterprise

n8n will soon be available as a fully managed environment inside the Joule Studio solution on the SAP Business AI Platform. With n8n, SAP software developers can visually build AI workflows and orchestrate agents from the n8n canvas directly within Joule Studio, combining agentic capabilities with process automation across SAP and the broader landscape of tools and services their organizations depend on. Interested in n8n for your SAP environment, or just want to stay connected? Leave your email and our team will be in touch. SAP handles identity, access control, and operations, so there’s nothing extra for teams to set up before they start building. And because the n8n embedded environment runs on SAP's cloud infrastructure, workflows stay where your most sensitive business systems already are. n8n brings enterprise-grade capabilities that complement SAP’s platform-level controls. For organizations operating under GDPR,…

How n8n is powering the next wave of AI automation at Mercedes-Benz

AI automation at enterprise scale is still more about promise than reality for most organisations. Proof-of-concept projects stay in proof-of-concept. Isolated tools never connect to the systems that matter. Mercedes-Benz is doing it differently. The German OEM has rolled out n8n as its global low-code automation platform, deploying it across business units worldwide to bring AI-powered workflows into its core operations. For other companies, this offers a clear picture of what scaling AI automation actually requires. Built for enterprises that need control For an organisation operating across multiple regions and regulatory environments, sovereignty over data and architecture isn't optional. Mercedes-Benz chose n8n in part because of its self-hosted, cloud-agnostic deployment model – meaning workflows run on their own infrastructure, sensitive data stays where it belongs, and the company retains full control over its automation layer. That model supports organisations operating…

AI Agent Architecture Patterns: From Prototype To Production

The gap between prototypes and production-ready systems usually comes down to how you structure the underlying logic. While it’s natural to focus on the specific code used to trigger a model, the real engineering challenge is selecting the right AI agent architecture patterns to maintain stability under unpredictable, real-world inputs. A strong framework prioritizes how control flows between components, how tasks execute, and how failures are contained. Instead of reacting to individual model responses, you manage how data flows and where decisions happen. Each design choice acts as a safeguard, ensuring a single hallucination or API timeout doesn't compromise the automation. Misapplying these patterns often introduces failure modes that no amount of prompt engineering can fix. Choosing an autonomous loop where a step-by-step (pre-defined) sequence is required can stall a workflow. Centralizing control in a high-latency environment can slow every…

Deploy n8n agents that show up as members of the team inside Microsoft apps

With the general availability of Microsoft Agent 365, n8n users can now build AI agents that show up inside the Microsoft 365 applications where people get work done. Now you can @mention agents built in n8n from Microsoft Teams, add them to emails in Outlook, or tag them in Word to bring them into your work. Microsoft Agent 365 gives each agent its own Entra ID, so employees can grant it access to Microsoft 365 services like SharePoint and Teams channels the same way they would a teammate. Microsoft handles identity, access policies, and compliance through Purview and Defender. Agent 365 also handles agent lifecycle management actions, rules and policies, analytics in the Microsoft 365 Admin Center; providing an effective and scalable way to control agents at scale through familiar tools. The n8n canvas is where you design what the…

Build and Update Workflows with n8n's MCP Server

Describe what you want from Claude, ChatGPT, or your IDE, and get a ready-to-run workflow in a few minutes, built directly in n8n. No more copy-paste, no more back-and-forth. n8n's MCP server can now build workflows from a prompt (and not just run them)! The MCP server has been around for a few months, but previously you could only execute existing workflows. Now you can build new ones from scratch and update existing ones, directly in your n8n instance. - Go from prompt to a ready-to-run workflow. Tell your AI client what you want. It builds the workflow, validates it, runs it, and fixes itself if something breaks. No messing with JSON files or copy-pasting errors. - Works in whatever AI client you already use. Claude, ChatGPT, Cursor, Windsurf - if it “speaks” MCP, you can point it at n8n.…

Workflow Automation vs. Orchestration: Architectural Differences That Matter at Scale

How much do workflow automation versus orchestration architectures differ at scale? Each solves different problems. Workflow automation handles individual tasks, while orchestration coordinates multiple tasks into end-to-end processes. Choosing the right approach or combining them shapes how your processes behave under real-world conditions. This article breaks down the architectural differences between workflow automation and orchestration. You’ll see how those differences affect reliability and behavior in production systems and learn how to choose the right approach for your needs. What is workflow automation? Workflow automation runs a sequence of tasks when triggered by a preset event or criteria. It’s designed for a bounded scope where logic flows from point A to point B. These systems prioritize efficiency in repetitive business processes (sending notifications, updating records, moving data between systems) by relying on stateless execution/focusing on task-level execution. Traditional (stateless) automation workflows…

Orchestration vs. Choreography: Which One to Choose – or Use Both?

Orchestration vs. choreography isn’t just an architectural choice – it’s a decision about how your system thinks. Orchestration relies on one central controller to coordinate every step of a workflow, providing full visibility and control. Choreography takes an opposite approach. Services communicate through events and act independently instead of sharing a single point of control. Both patterns solve the problem of how services collaborate, but they do so in fundamentally different ways. Choosing one over another directly impacts how you can scale, debug, and operate your system in production. In this article, we’ll compare orchestration and choreography and discover the tradeoffs between control and autonomy. Microservices orchestration vs. choreography explained In orchestration, a central controller acts like a conductor. It tells each microservice when to execute its logic and tracks the outcome. This provides a clear and predictable control flow.…

Production AI Playbook: Deterministic Steps & AI Steps

This post is part of a series that explores proven strategies and practical examples for building reliable AI systems. New to n8n? Start with the introduction. Find out when new topics are added to the Production AI Playbook via RSS, LinkedIn or X. The Reliability Gap in AI Workflows Here's a pattern that plays out across teams building with AI. You connect an LLM to your workflow, feed it some data, and get impressive results. At a glance, the summaries are sharp. The classifications generated by the AI system feel right. The generated content sounds natural. So the team ships it. Then the edge cases start showing up everywhere. A customer name with special characters breaks the parsing. A support ticket written in sarcasm gets classified as positive feedback. An LLM generates a perfectly worded email but hallucinates a product…

Production AI Playbook: Introduction

The Production AI Playbook introduces the patterns and capabilities teams use to build production AI systems with n8n. It reflects lessons learned from teams integrating AI into real operational systems, where reliability, governance, and maintainability matter as much as model capability. New sections will be added on a rolling basis, covering how to combine deterministic automation with AI, design scalable agent architectures, maintain human oversight, monitor performance, and operate AI workflows reliably in production. Find out when new topics are added via RSS, LinkedIn or X. n8n’s workflow architecture n8n is a node-based workflow automation platform where composable nodes chain together into execution pipelines. Workflows orchestrate data movement, system integrations, business logic, and AI steps in one place. This architecture makes it easier to visualize, explain, and control how automation systems operate. n8n is source-available under a fair-code license, which…

Firecrawl + n8n: real-time web data for your AI workflows

Firecrawl is offering 100,000 credits when you connect through n8n Cloud We've partnered with Firecrawl to make it easier than ever to bring web data into your n8n workflows. Connect to Firecrawl in one step, create an account without leaving the canvas, and start building immediately on n8n Cloud. No API keys to track down, no separate sign-up flow. This builds on recent improvements to n8n-managed authentication in Cloud, where n8n handles credential setup and lets you connect to dozens of supported services in one step during node setup. Already on n8n Cloud? Just add the Firecrawl node to your workflow and click "Connect to Firecrawl" when setting up credentials. Not on n8n Cloud yet? Try it now so you can take advantage of this offer from Firecrawl and explore the power of real-time web data in your workflows risk-free.…

How n8n Handles Vulnerability Disclosure - and Why We Do It This Way

As n8n grows, so does the scrutiny our codebase receives from the security community. That is a good thing. In the past months we have published many security advisories, and with that comes natural questions from our users: How much notice will I get before a vulnerability is published? Why can't I get more time? And how does all of this work when the source code is publicly available? We want to answer these questions openly, because we believe that a well-understood disclosure process builds more trust than a secretive one. The tension at the heart of open-access security n8n's source code is publicly available. This is core to who we are — it enables our community to inspect, extend, and contribute to the platform. But it also creates a specific challenge for security patches that closed-source vendors do not…

State of AI: February 2026 newsletter

State of AI: February 2026 newsletter Software stocks crater as agentic AI rewrites the playbook. Plus: Moltbook's AI theatre, OpenClaw's 157K-star security mess, and HBM runs out. Dear readers, Welcome to the latest issue of the State of AI, an editorialized newsletter that covers the key developments in AI policy, research, industry, and start-ups over the last month. First up, a few reminders: AI meetups: Join our upcoming AI meetups in Munich (17 Feb ‘26) for the Munich Security Conference and Zurich (19 Feb ‘26), as well as in Paris (11 Mar ‘26) and SF (28 Apr ‘16). RAAIS 2026: Join our 11th Research and Applied AI Summit in London on 12 June 2026, the premier global meeting for learning AI best practices and what’s coming next. Air Street Press featured the Air Street Capital Year in Review 2025, how…

Streaming Tokens and Tools: Multi-Turn Agentic Harness Support in NVIDIA Dynamo

An agentic exchange must preserve a structured interaction: assistant turns interleave reasoning with one or more tool calls, and subsequent user turns return the corresponding tool results to the model context. Reasoning replay is model- and turn-dependent: some reasoning should be retained, while some should be dropped. The inference engine is responsible for supporting this more expressive interaction model and for producing correctly segmented API results. Tool-call parsing and reasoning parsing need to happen before the attached harness consumes the response. High-value agentic workflows such as coding also depend on a responsive harness experience: reasoning segments, tool-call events, and request metadata need to stream back as the turn unfolds instead of arriving only after a final text response. This post covers lessons from running real agentic clients against NVIDIA Dynamo: how we hardened parser and API coverage, improved streaming behavior,…

Building for the Rising Complexity of Agentic Systems with Extreme Co-Design

Generative AI’s explosive first chapter was defined by humans sending requests and models responding. The agentic chapter is different. Agents don’t follow a pre-determined sequence of actions. They call tools, spawn sub-agents with different tasks and models, retain information in memory, manage their own context window, and decide for themselves when they’re finished. In doing so, these systems push token consumption, context length, and latency requirements into extremely demanding regions — exactly the pressures now shaping the NVIDIA extreme co-design stack and the NVIDIA Vera Rubin platform. This post analyzes that evolution across three parts: - How agents consume tokens - Why their economics break under conventional serving - What an infrastructure stack purpose-built for agents looks like Transition to agents from chatbots As shown in Figure 1, below, the popularization of generative AI began with a simple interaction model:…

Optimize Supply Chain Decision Systems Using NVIDIA cuOpt Agent Skills

Modern supply chains operate under the constant pressures of fluctuating demand, volatile costs, constrained capacity, and interdependent decision-making. Traditionally, specialized operations research (OR) teams solved these problems by translating business questions into mathematical models. This process can take weeks and often produces fragile solutions that struggle to adapt when conditions change. Agentic AI is changing this paradigm. Combining the reasoning capabilities of LLMs with the computational power of GPU-accelerated solvers, AI agents can interpret business problems expressed in natural language and translate them into rigorous, optimized decisions in seconds. At the heart of this approach are agent skills—an open format for extending agents with specialized knowledge and workflows. Skills serve as a packaging mechanism, dynamically loading the correct procedural context and improving agent performance on specific tasks. This post outlines core NVIDIA cuOpt agent skills, their significance, and how they…

Powering AI Factories with NVIDIA Enterprise Reference Architectures

The next wave of enterprise productivity is being built on AI factories. As organizations deploy agentic AI systems capable of reasoning, automation, and real-time decision-making at scale, competitive advantage increasingly depends on the infrastructure that supports them. Success requires more than raw compute. It demands a scalable, predictable foundation that can orchestrate intelligent agents, manage data movement efficiently, and deliver consistent performance from pilot to production. AI factories powered by NVIDIA bring industrial-grade discipline to AI, changing infrastructure into a strategic engine for speed, reliability, and accelerated innovation. Infrastructure is one of the five layers of AI, and represents the foundation for AI factories. Building that foundation, however, requires more than selecting high-performance hardware. Enterprises need proven architectural guidance that removes integration risk, reduces time to deployment, and ensures performance at scale. NVIDIA Enterprise Reference Architectures (Enterprise RAs) provide that…

24/7 Simulation Loops: How Agentic AI Keeps Subsurface Engineering Moving

The subsurface industry is at a critical point in its digital evolution. For decades, unlocking reservoir potential has relied on experts performing essential and time-intensive manual workflows. As data complexity grows, the gap between machine speed and human bandwidth has become a primary bottleneck. On-demand simulation workflows are currently hampered by both manual data overhead and inherent operational latency. The need for engineers to manually aggregate, synthesize, and translate disparate technical materials creates significant knowledge consolidation bottlenecks that stretch project cycles. This is further compounded by the asynchronous nature of simulation jobs; when simulations finish or fail during off-hours or while engineers are juggling competing priorities, dead time accumulates. Consequently, what should be a standard 24-hour turnaround often spirals into a multi-day delay, stalling progress across global teams. In this post, we explain how applying agentic AI on top of…

Mitigating Indirect AGENTS.md Injection Attacks in Agentic Environments

AI tools are significantly accelerating software development and changing how developers work with code. These tools serve as real-time copilots, automating repetitive tasks, executing tasks, writing documentation, and more. OpenAI Codex, for example, is a coding agent designed to assist developers through tasks like code generation, debugging, and automated pull request (PR) creation. Yet as agentic tools are integrated into workflows, how they affect the safety, reliability, and integrity of software development must be considered. A recent Codex vulnerability discovered by the NVIDIA AI Red Team highlights security gaps from indirect AGENTS.md injection through malicious dependencies. While this attack relies on a compromised dependency, meaning the attacker already has a form of code execution, it illustrates a new dimension of supply chain risk unique to agentic development environments. This post walks through the attack chain step-by-step—from dependency setup to instruction…

Full-Stack Optimizations for Agentic Inference with NVIDIA Dynamo

Coding agents are starting to write production code at scale. Stripe’s agents generate 1,300+ PRs per week. Ramp attributes 30% of merged PRs to agents. Spotify reports 650+ agent-generated PRs per month. Tools like Claude Code and Codex make hundreds of API calls per coding session, each carrying the full conversation history. Behind every one of these workflows is an inference stack under significant KV cache pressure. Lets take Claude Code as an example. After the first API call that writes the conversation prefix to KV cache, every subsequent call to the same worker hits 85-97% cache. Agent teams (or swarms) push this further with 97.2% aggregate cache hit rate across 4 Opus teammates. An 11.7x read/write ratio means the system reads from cache nearly 12 times for every token it writes. This is a write-once-read-many (WORM) access pattern: the…

Build a More Secure, Always-On Local AI Agent with OpenClaw and NVIDIA NemoClaw

Agents are evolving from question-and-answer systems into long-running autonomous assistants that read files, call APIs, and drive multi-step workflows. However, deploying an agent to execute code and use tools without proper isolation raises real risks—especially when using third-party cloud infrastructure due to data privacy and control. NVIDIA NemoClaw is an open-source reference stack that orchestrates NVIDIA OpenShell to run OpenClaw, a self-hosted gateway that connects messaging platforms to AI coding agents powered by open models like NVIDIA Nemotron. NemoClaw adds guided onboarding, lifecycle management, image hardening, and a versioned blueprint, providing a complete pipeline from model inference to more secure, interactive agent deployment. This tutorial walks through a NemoClaw deployment on NVIDIA DGX Spark—from configuring the runtime environment and serving the model locally, to installing the NemoClaw stack and connecting it to Telegram for remote access. You’ll build a local,…

MiniMax M2.7 Advances Scalable Agentic Workflows on NVIDIA Platforms for Complex AI Applications

The release of MiniMax M2.7 adds enhancements to the popular MiniMax M2.5 model, built for agentic harnesses, and other complex use cases in fields such as reasoning, ML research workflows, software, engineering, and office work. The open weights release of MiniMax M2.7 is now available through NVIDIA and across the open source inference ecosystem. The MiniMax M2 series is a sparse mixture-of-experts (MoE) model family designed for efficiency and capability. The MoE design keeps inference costs low while preserving the full capacity of a 230B-parameter model. It uses multi-head causal self-attention enhanced with Rotary Position Embeddings (RoPE) and Query-Key Root Mean Square Normalization (QK RMSNorm) for stable training at scale. A top-k expert routing mechanism ensures that only the most relevant experts activate for any given input, keeping inference costs low despite the model’s large total parameter count. The result…

How NVIDIA Dynamo 1.0 Powers Multi-Node Inference at Production Scale

Reasoning models are growing rapidly in size and are increasingly being integrated into agentic AI workflows that interact with other models and external tools. Deploying these models and workflows in production environments requires distributing them across multiple GPU nodes, which demands careful orchestration and coordination across GPUs. NVIDIA Dynamo 1.0—available now—addresses these problems by accelerating generative AI and reasoning models in large-scale distributed environments. The AI framework delivers low-latency, high-throughput, distributed inference for production-grade multi-node AI deployments. Dynamo supports leading open source inference engines, including SGLang, NVIDIA TensorRT LLM, and vLLM. It also has delivered strong results in trusted third-party benchmarks such as MLPerf and SemiAnalysis InferenceX, reinforcing its position as a production-grade inference platform. Dynamo can boost the number of requests served by up to 7x on NVIDIA Blackwell, as demonstrated in the recent SemiAnalysis InferenceX benchmark. SemiAnalysis InferenceX,…

Introducing Nemotron 3 Super: An Open Hybrid Mamba-Transformer MoE for Agentic Reasoning

Agentic AI systems need models with the specialized depth to solve dense technical problems autonomously. They must excel at reasoning, coding, and long-context analysis, while remaining efficient enough to run continuously at scale. Multi-agent systems generate up to 15x the tokens of standard chats, re-sending history, tool outputs, and reasoning steps at every turn. Over long tasks, this “context explosion” causes goal drift, where agents gradually lose alignment with the original objective. And using massive reasoning models for every sub-task—the “thinking tax”—makes multi-agent applications too expensive and sluggish for practical use. Today, we are releasing Nemotron 3 Super to address these limitations. The new Super model is a 120B total, 12B active-parameter model that delivers maximum compute efficiency and accuracy for complex multi-agent applications such as software development and cybersecurity triaging. This model follows the introduction of Nemotron 3 Nano…

NVIDIA RTX Innovations Are Powering the Next Era of Game Development

NVIDIA RTX ray tracing and AI-powered neural rendering technologies are redefining how games are made, enabling a new standard for visuals and performance. At GDC 2026, NVIDIA unveiled the latest path tracing innovations elevating visual fidelity, on-device AI models enabling players to interact with their favorite experiences in new ways, and enterprise solutions accelerating game development from the ground up. This post provides a detailed overview of these latest innovations, including: - Introducing a new system for dense, path-traced foliage in NVIDIA RTX Mega Geometry - Adding path-traced indirect lighting with ReSTIR PT in the NVIDIA RTX Dynamic Illumination SDK and RTX Hair (beta) for strand-based acceleration in the NVIDIA branch of UE5 - Expanding language recognition support in NVIDIA ACE; production-quality on-device text-to-speech (TTS); a small language model (SML) with advanced agent capabilities for AI-powered game characters - Enabling…

Practical Security Guidance for Sandboxing Agentic Workflows and Managing Execution Risk

AI coding agents enable developers to work faster by streamlining tasks and driving automated, test-driven development. However, they also introduce a significant, often overlooked, attack surface by running tools from the command line with the same permissions and entitlements as the user, making them computer use agents, with all the risks those entail. The primary threat to these tools is that of indirect prompt injection, where a portion of the content ingested by the LLM driving the model is provided by an adversary through vectors such as malicious repositories or pull requests, git histories with prompt injections, .cursorrules , CLAUDE/AGENT.md files that contain prompt injections or malicious MCP responses. Such malicious instructions to the LLM can result in it taking attacker-influenced actions with adverse consequences. Manual approval of actions performed by the agent is the most common way to manage…

MiniMax M2 October 28, 2025 MiniMax M2 is now available on Ollama's cloud. It's a model built for coding and agentic workflows.

MiniMax M2 October 28, 2025 MiniMax M2 is now available on Ollama’s cloud. It’s a model built for coding and agentic workflows. Get Started ollama run minimax-m2:cloud Highlights Superior Intelligence. According to benchmarks from Artificial Analysis, MiniMax-M2 demonstrates highly competitive general intelligence across mathematics, science, instruction following, coding, and agentic tool use. Its composite score ranks #1 among open-source models globally. Advanced Coding. Engineered for end-to-end developer workflows, MiniMax-M2 excels at multi-file edits, coding-run-fix loops, and test-validated repairs. Strong performance on Terminal-Bench and (Multi-)SWE-Bench–style tasks demonstrates practical effectiveness in terminals, IDEs, and CI across languages. Agent Performance. MiniMax-M2 plans and executes complex, long-horizon toolchains across shell, browser, retrieval, and code runners. In BrowseComp-style evaluations, it consistently locates hard-to-surface sources, maintains traceable evidence, and gracefully recovers from flaky steps. Efficient Design. With 10 billion activated parameters (230 billion in total), MiniMax-M2…

New coding models & integrations October 16, 2025 GLM-4.6 and Qwen3-coder-480B are available on Ollama’s cloud service with easy integrations to the tools you are familiar with. Qwen3-Coder-30B has been updated for faster, more reliable tool calling in Ollama’s new engine.

New coding models & integrations October 16, 2025 GLM-4.6 and Qwen3-coder-480B are available on Ollama’s cloud service with easy integrations to the tools you are familiar with. Qwen3-Coder-30B has been updated for faster, more reliable tool calling in Ollama’s new engine. Get started GLM-4.6 ollama run glm-4.6:cloud Qwen3-Coder-480B ollama run qwen3-coder:480b-cloud For users with more than 300GB of VRAM, qwen3-coder:480b is also available locally. Qwen3-Coder-30B ollama run qwen3-coder:30b Example prompts Create a single-page app in a single HTML file with the following requirements: Name: Ollama's Adventure Goal: Jump over obstacles to survive as long as possible. Features: Increasing speed, high score tracking, retry button, and funny sounds for actions and events. The UI should be colorful, with parallax scrolling backgrounds. The characters should look cartoonish, related to alpacas and be fun to watch. The game should be enjoyable for everyone.…

Running Codex safely at OpenAI

As AI systems become more capable, they increasingly act on behalf of users. Coding agents can autonomously review repositories, run commands, and interact with development tools. These are tasks that previously required direct human execution. With Codex, we’ve designed these capabilities alongside the controls organizations need for safe deployment. Security teams need ways to govern how agents operate: what they can access, when human approval is required, which systems they can interact with, and what telemetry exists to explain their behavior. At OpenAI, we deploy Codex with a few clear goals: keep the agent inside clear technical boundaries, let developers move quickly on low-risk actions, and make higher-risk actions explicit. We also preserve agent-native telemetry so we can understand and audit what the agent did. In practice, that means managed configuration, constrained execution, network policies, and agent-native logs. We deploy…

Parloa builds service agents customers want to talk to

Parloa builds service agents customers want to talk to Parloa uses OpenAI models to simulate, evaluate, and run voice-driven customer service systems for the enterprise. In Parloa’s early days, Co-founder Stefan Ostwald spent a day inside an insurance call center, where his team had been building early voice experiences. Sitting alongside agents, he listened to the same conversations play out again and again: password resets, policy questions, routine changes. He realized much of that work could be automated. After that experience, Berlin-based Parloa(opens in a new window) began building rule-based voice agents to automate high-volume customer interactions. With the emergence of ChatGPT, the company evolved to build what is now its AI Agent Management Platform (AMP), built on a new generation of models including GPT‑5.4. AMP gives enterprises a way to design, deploy, and manage customer service interactions at scale.…

OpenAI and PwC collaborate to reimagine the office of the CFO

OpenAI and PwC collaborate to reimagine the office of the CFO Finance teams sit at the center of how organizations plan, allocate capital, manage risk, and make decisions. To help them keep up with growing demands, PwC and OpenAI are collaborating to help enterprises reimagine the office of the CFO with AI agents that can automate workflows, coordinate across systems, surface risks and insights, and support better decisions with strong governance and human oversight. Together, PwC and OpenAI are building AI agents around the core operating rhythms of finance, from planning, forecasting, and reporting to procurement, payments, treasury, tax, and the accounting close. What sets this collaboration apart is its focus on building in the real world, not just designing in theory. For example, PwC and OpenAI are building a procurement agent inside the OpenAI finance organization, and are applying…

Enterprises power agentic workflows in Cloudflare Agent Cloud with OpenAI

Enterprises power agentic workflows in Cloudflare Agent Cloud with OpenAI Key Takeaways: - Millions of enterprises can now access OpenAI frontier models directly within Cloudflare Agent Cloud. - With OpenAI, enterprises using Cloudflare’s Agent Cloud can deploy agents powered by models like GPT‑5.4 to perform real work. - Enterprises can now deploy agents built on Codex harness to Cloudflare. Cloudflare is expanding access to OpenAI frontier models, including GPT‑5.4, making them available to millions of customers across Agent Cloud. Agent Cloud is a platform that enables businesses to deploy AI agents powered by OpenAI models to perform real work. For example, companies can use it with OpenAI to deploy agents that automatically handle tasks like responding to customers, updating systems, and generating reports - all within a secure, production-ready environment. Agent Cloud runs on top of Cloudflare Workers AI(opens in…

CyberAgent moves faster with ChatGPT Enterprise and Codex

CyberAgent moves faster with ChatGPT Enterprise and Codex CyberAgent uses ChatGPT Enterprise and Codex to help teams work faster, raise quality, and improve decisions across its businesses. Results 93% Monthly active usage of ChatGPT Enterprise CyberAgent is a Japanese internet company engaged in businesses such as internet advertising, media & IP, and gaming. Guided by its vision of “creating a company that represents the 21st century,” the company leverages its strengths in technology and creativity to generate new value both domestically and internationally. At CyberAgent, AI is positioned not as a set of limited advanced initiatives, but as a foundational technology that supports both business growth and operational design. The company has made continuous investments in this area. In 2016, it established “AI Lab” to conduct research and development of a wide range of AI technologies related to digital marketing.…

Introducing the OpenAI Safety Bug Bounty program

Today, OpenAI is launching a public Safety Bug Bounty(opens in a new window) program focused on identifying AI abuse and safety risks across our products. As AI technology rapidly evolves, so do the potential ways it can be misused. Our goal is to ensure our systems remain safe and secure against misuse or abuse that could lead to tangible harm. This new program will complement OpenAI’s Security Bug Bounty(opens in a new window) by accepting issues that pose meaningful abuse and safety risks, even if they don’t meet the criteria for a security vulnerability. Through this program, we look forward to continuing to partner with safety and security researchers to help us identify and address issues that fall outside conventional security vulnerabilities but still pose real risks. Submissions will be triaged by OpenAI’s Safety and Security Bug Bounty teams, and…

Powering product discovery in ChatGPT

Powering Product Discovery in ChatGPT Launching richer, more visually immersive shopping experiences powered by the Agentic Commerce Protocol More and more, people are starting their shopping in ChatGPT—to explore, compare, and figure out what to buy. Shopping on the web is easy if you already know what you want. But when you’re still deciding, it often means jumping between tabs, reading the same “best of” lists, and trying to piece together the right answer. ChatGPT solves that: figuring out what to buy. You can describe what you’re looking for, refine it in a conversation, and quickly compare options that fit your specific needs. Today, we’re making that experience better with richer and more visual shopping in ChatGPT. You can now browse products visually, compare options side-by-side, and get detailed, up-to-date information—all in one place. What used to take hours of…

Designing AI agents to resist prompt injection

Designing AI agents to resist prompt injection What social engineering teaches us about securing AI agents. AI agents are increasingly able to browse the web, retrieve information, and take actions on a user’s behalf. Those capabilities are useful, but they also create new ways for attackers to try to manipulate the system. These attacks are often described as prompt injection: instructions placed in external content in an attempt to make the model do something the user did not ask for. In our experience, the most effective real-world versions of these attacks increasingly resemble social engineering more than simple prompt overrides. That shift matters. If the problem is not just identifying a malicious string, but resisting misleading or manipulative content in context, then defending against it cannot rely only on filtering inputs. It also requires designing the system so that the…

Introducing the Stateful Runtime Environment for Agents in Amazon Bedrock

Introducing the Stateful Runtime Environment for Agents in Amazon Bedrock AI agents excel at reasoning. The harder part is operational: running multi-step work reliably over time, across real tools and real systems, with the right controls. Today, we’re making this easier for customers through a partnership and joint collaboration with Amazon to deliver the new Stateful Runtime Environment that runs natively in Amazon Bedrock. AWS customers will have access to the Runtime Environment, powered by OpenAI models, optimized for AWS infrastructure and tailored for agentic workflows, with the state, reliability, and governance needed for production work. A lot of agent prototypes based on stateless APIs tackle simple use cases: one prompt, one answer, maybe one tool call. Production work is different. Real workflows unfold across many steps, require context from previous actions, depend on multiple tool outputs, approvals, and system…

Achieving 10x growth with agentic sales prospecting

Clay Clay achieves 10x growth by reinventing data enrichment and sales outreach with OpenAI. Successful go-to-market (GTM) teams need comprehensive, high-quality data, but the process of gathering, validating, and enriching this data is typically fragmented across many tools. This bottleneck slows the pace of sales outreach. Clay(opens in a new window) helps GTM teams scale their outreach by centralizing lead information and enabling personalized messaging. Clay integrated with GPT‑4 to create Claygent, an AI agent that can research anything. Claygent visits websites to find and summarize relevant information, replicating how sales development researchers operate, but much faster and cheaper. With Claygent, a single person can handle the work of an entire team. The company has achieved 10x year-over-year growth for each of the past two years, with over 100 thousand users including major customers like Intercom, Verkada and Notion. Building…

GPT-5.3-Codex System Card

GPT‑5.3‑Codex is the most capable agentic coding model to date, combining the frontier coding performance of GPT‑5.2‑Codex with the reasoning and professional knowledge capabilities of GPT‑5.2. This enables it to take on long-running tasks that involve research, tool use, and complex execution. Much like a colleague, you can steer and interact with GPT‑5.3‑Codex while it’s working, without losing context. Like other recent models, it is being treated as High capability on biology, and is being deployed with the corresponding suite of safeguards we use for other models in the GPT‑5 family. It does not reach High capability on AI self-improvement. This is the first launch we are treating as High capability in the Cybersecurity domain under our Preparedness Framework, activating the associated safeguards. We do not have definitive evidence that this model reaches our High threshold, but are taking a…

Introducing GPT-5.3-Codex

We’re introducing a new model that unlocks even more of what Codex can do: GPT‑5.3‑Codex, the most capable agentic coding model to date. The model advances both the frontier coding performance of GPT‑5.2‑Codex and the reasoning and professional knowledge capabilities of GPT‑5.2, together in one model, which is also 25% faster. This enables it to take on long-running tasks that involve research, tool use, and complex execution. Much like a colleague, you can steer and interact with GPT‑5.3‑Codex while it’s working, without losing context. GPT‑5.3‑Codex is our first model that was instrumental in creating itself. The Codex team used early versions to debug its own training, manage its own deployment, and diagnose test results and evaluations—our team was blown away by how much Codex was able to accelerate its own development. With GPT‑5.3‑Codex, Codex goes from an agent that can…

Inside OpenAI’s in-house data agent

Data powers how systems learn, products evolve, and how companies make choices. But getting answers quickly, correctly, and with the right context is often harder than it should be. To make this easier as OpenAI scales, we built our own bespoke in-house AI data agent that explores and reasons over our own platform. Our agent is a custom internal-only tool (not an external offering), built specifically around OpenAI’s data, permissions, and workflows. We’re showing how we built and use it to help surface examples of the real, impactful ways AI can support day-to-day work across our teams. The OpenAI tools we used to build and run it (Codex, our GPT‑5 flagship model, the Evals API(opens in a new window), and the Embeddings API(opens in a new window)) are the same tools we make available to developers everywhere. Our data agent…

Unrolling the Codex agent loop

Codex CLI(opens in a new window) is our cross-platform local software agent, designed to produce high-quality, reliable software changes while operating safely and efficiently on your machine. We’ve learned a tremendous amount about how to build a world-class software agent since we first launched the CLI in April. To unpack those insights, this is the first post in an ongoing series where we’ll explore various aspects of how Codex works, as well as hard-earned lessons. (For an even more granular view on how the Codex CLI is built, check out our open source repository at https://github.com/openai/codex(opens in a new window). Many of the finer details of our design decisions are memorialized in GitHub issues and pull requests if you’d like to learn more.) To kick off, we’ll focus on the agent loop, which is the core logic in Codex CLI…

Cisco and OpenAI redefine enterprise engineering with AI agents

Cisco and OpenAI redefine enterprise engineering with AI agents By deploying Codex broadly, Cisco made AI-native development a core part of how enterprise software gets built. For decades, Cisco has built and operated some of the world’s most complex, mission-critical software systems. As generative AI matured from experimentation to real operational capability, Cisco leaned into what it knows best: scaling advanced technology inside demanding, real-world environments. That mindset led Cisco to begin working closely with OpenAI around Codex, helping define what enterprise-grade AI for software engineering should look like in practice—and how Codex could be applied to real, large-scale engineering work inside complex production environments. Rather than treat Codex as a standalone developer tool, Cisco began integrating it directly into production engineering workflows, exposing it to massive multi-repository systems, C/C++-heavy codebases, and the security, compliance, and governance requirements of a…

Continuously hardening ChatGPT Atlas against prompt injection

Continuously hardening ChatGPT Atlas against prompt injection attacks Automated red teaming—powered by reinforcement learning—helps us proactively discover and patch real-world agent exploits before they’re weaponized in the wild. Agent mode in ChatGPT Atlas is one of the most general-purpose agentic features we’ve released to date. In this mode, the browser agent views webpages and takes actions, clicks, and keystrokes inside your browser, just as you would. This allows ChatGPT to work directly on many of your day-to-day workflows using the same space, context, and data. As the browser agent helps you get more done, it also becomes a higher-value target of adversarial attacks. This makes AI security especially important. Long before we launched ChatGPT Atlas, we’ve been continuously building and hardening defenses against emerging threats that specifically target this new “agent in the browser” paradigm. Prompt injection is one of…

How We Used Codex to Ship Sora for Android in 28 Days

How we used Codex to build Sora for Android in 28 days By Patrick Hum and RJ Marsan, Members of the Technical Staff In November, we launched the Sora Android app to the world, giving anyone with an Android device the ability to turn a short prompt into a vivid video. On launch day, the app reached #1 in the Play Store. Android users generated more than a million videos in the first 24 hours. Behind the launch is a story: the initial version of Sora’s production Android app was built in 28 days, thanks to the same agent that’s available to any team or developer: Codex. From October 8 to November 5, 2025, a lean engineering team working alongside Codex and consuming roughly 5 billion tokens, shipped Sora for Android from prototype to global launch. Despite its scale, the…

Introducing GPT-5.2

We are introducing GPT‑5.2, the most capable model series yet for professional knowledge work. Already, the average ChatGPT Enterprise user says AI saves them 40–60 minutes a day, and heavy users say it saves them more than 10 hours a week. We designed GPT‑5.2 to unlock even more economic value for people; it’s better at creating spreadsheets, building presentations, writing code, perceiving images, understanding long contexts, using tools, and handling complex, multi-step projects. GPT‑5.2 sets a new state of the art across many benchmarks, including GDPval, where it outperforms industry professionals at well-specified knowledge work tasks spanning 44 occupations. Notion(opens in a new window), Box(opens in a new window), Shopify(opens in a new window), Harvey(opens in a new window) and Zoom(opens in a new window) observed GPT‑5.2 demonstrates state-of-the-art long-horizon reasoning and tool-calling performance. Databricks(opens in a new window), Hex(opens…

OpenAI co-founds Agentic AI Foundation, donates AGENTS.md

OpenAI co-founds the Agentic AI Foundation under the Linux Foundation AAIF aims to advance open-source agentic AI with the donation of standards including OpenAI’s AGENTS.md Today, OpenAI is co-founding the Agentic AI Foundation (AAIF)(opens in a new window) under the Linux Foundation, alongside Anthropic and Block, and with the support of Google, Microsoft, AWS, Bloomberg, and Cloudflare. The AAIF is designed to provide neutral stewardship for open, interoperable infrastructure as agentic AI systems move from experimentation into real-world production. As part of this effort, we’re contributing AGENTS.md(opens in a new window)—a simple, open format for providing agents with project-specific instructions and context—to the foundation to ensure long-term support and adoption across the community. Developers are rapidly adopting AI to build more capable agentic systems—from coding assistants to workflow automation and customer service agents. In 2025, these systems have begun to…

Instacart and OpenAI partner on AI shopping experiences

Instacart and OpenAI partner on AI shopping experiences Key takeaways: - With the Instacart app in ChatGPT, users can browse groceries, build their cart, and check out seamlessly through OpenAI Instant Checkout without ever leaving the chat. - The new experience builds on a longstanding partnership between OpenAI and Instacart to facilitate AI-powered shopping. OpenAI and Instacart are deepening their longstanding partnership by bringing the first fully integrated grocery shopping and Instant Checkout payment app to ChatGPT. With the Instacart app in ChatGPT, users can go from meal inspiration to doorstep delivery without ever leaving the conversation. The integration enables Instacart’s real-time grocery network and fulfillment capabilities with the help of OpenAI’s frontier models. Instacart is the first app to offer a checkout experience directly within ChatGPT, powered by the Agentic Commerce Protocol. “Instacart and ChatGPT are redefining what’s possible…

Inside Mirakl's agentic commerce vision

Inside Mirakl’s agentic commerce vision Mirakl is making AI a company-wide capability—powering faster workflows, stronger products, and the next wave of agent-driven commerce. Mirakl powers marketplaces and retail media for leading retailers and brands globally. As AI capabilities advance, Mirakl has taken a distinct approach: AI isn’t just a tool for specialists—it’s a capability every employee is expected to build with. We sat down with Adrien Nussenbaum, Co-founder & Co-CEO, and Anne-Claire Baschet, Chief Data & AI Officer, to hear how Mirakl is making AI core to both its products and the way its teams work. “The initial vision was 100% of Mirakl workers use AI. We shifted a few months ago to 100% of Mirakl workers being builders of agents—for their individual purpose or to redefine workflows in their teams to bring more value to the user.” - 70%…

Accenture and OpenAI accelerate enterprise AI success

Accenture and OpenAI accelerate enterprise AI success Key Takeaways: - Accenture to equip tens of thousands of its professionals with ChatGPT Enterprise, the largest number of professionals upskilled through OpenAI Certifications. - OpenAI will be one of Accenture’s primary AI partners for its next generation of AI-powered services. - Flagship AI client program launched to help organizations bring AI into every part of their business. Accenture and OpenAI are collaborating to help enterprises bring agentic AI capabilities into the core of their business and unlock new levels of growth. As part of the agreement, Accenture will equip tens of thousands of its professionals with ChatGPT Enterprise so the firm can leverage it in consulting, operations and delivery work and help OpenAI scale its capabilities to enterprises. By embedding OpenAI technology and practices in Accenture’s consulting, operations and delivery work, Accenture…

Building more with GPT-5.1-Codex-Max

We’re introducing GPT‑5.1‑Codex‑Max, our new frontier agentic coding model, available in Codex today. GPT‑5.1‑Codex‑Max is built on an update to our foundational reasoning model, which is trained on agentic tasks across software engineering, math, research, and more. GPT‑5.1‑Codex‑Max is faster, more intelligent, and more token-efficient at every stage of the development cycle–and a new step towards becoming a reliable coding partner. GPT‑5.1‑Codex‑Max is built for long-running, detailed work. It’s our first model natively trained to operate across multiple context windows through a process called compaction, coherently working over millions of tokens in a single task. This unlocks project-scale refactors, deep debugging sessions, and multi-hour agent loops. GPT‑5.1‑Codex‑Max is available in Codex today for use in the CLI, IDE extension, cloud, and code review, and API access is coming soon. GPT‑5.1‑Codex‑Max was trained on real-world software engineering tasks, like PR creation,…

GPT-5.1-Codex-Max System Card

GPT‑5.1‑Codex‑Max is our new frontier agentic coding model. It is built on an update to our foundational reasoning model trained on agentic tasks across software engineering, math, research, medicine, computer use and more. It is our first model natively trained to operate across multiple context windows through a process called compaction, coherently working over millions of tokens in a single task. Like its predecessors, GPT‑5.1‑Codex‑Max was trained on real-world software engineering tasks like PR creation, code review, frontend coding and Q&A. This system card outlines the comprehensive safety measures implemented for GPT‑5.1-Codex-Max. It details both model-level mitigations, such as specialized safety training for harmful tasks and prompt injections, and product-level mitigations like agent sandboxing and configurable network access. GPT‑5.1‑Codex‑Max was evaluated under our Preparedness Framework. It is very capable in the cybersecurity domain but does not reach High capability on…

How Chime is redefining marketing through AI

How Chime is redefining marketing through AI A conversation with Vineet Mehra, Chief Marketing Officer, Chime. Chime is a leading financial technology company that addresses the spending, saving, liquidity, and credit needs of millions of everyday people. We spoke with Vineet Mehra, Chief Marketing Officer at Chime about technology enabling a golden era of marketing, marketers developing AI literacy, and driving AI adoption from the top. As someone with experience leading marketing teams in multiple industries, how have you seen the role of marketing and the CMO evolve? We are entering the era of AI and the Agentification of Marketing—the next paradigm shift for CMOs. AI is not just another tool—it’s redefining the marketing operating model. The traditional campaign-centric structure is giving way to an agentic model, where AI agents operate as an extension of the brand—adapting in real time,…

How we built OWL, the new architecture behind our ChatGPT-based browser, Atlas

How we built OWL, the new architecture behind our ChatGPT‑based browser, Atlas Inside our new process architecture, which gives you a faster, smarter way to use the web. By Ken Rockot, Member of the Technical Staff and Ben Goodger, Head of Engineering, ChatGPT Atlas Last week, we launched ChatGPT Atlas, a new way to browse the web with ChatGPT by your side. In addition to being a full-featured web browser, Atlas offers a glimpse into the future: a world where you can bring ChatGPT with you across the internet to ask questions, make suggestions, and complete tasks for you. In this post, we unpack one of the most complex engineering aspects of the product: how we turned ChatGPT into a browser that gets more useful as you go. Making ChatGPT a true co-pilot for the web meant reimagining the entire…

Buy it in ChatGPT: Instant Checkout and the Agentic Commerce Protocol

Buy it in ChatGPT: Instant Checkout and the Agentic Commerce Protocol We’re taking first steps toward agentic commerce in ChatGPT with new ways for people, AI agents, and businesses to shop together. More than 700 million people turn to ChatGPT each week for help with everyday tasks, including finding products they love. Starting today, we’re taking the first steps toward ChatGPT helping people buy them too—beginning with Instant Checkout, powered by the Agentic Commerce Protocol, built with Stripe. U.S. ChatGPT Plus, Pro, and Free users can now buy directly from U.S. Etsy sellers right in chat, with over a million Shopify merchants, like Glossier, SKIMS, Spanx and Vuori, coming soon. Today, Instant Checkout supports single-item purchases. Next, we’ll add multi-item carts and expand merchants and regions. We’re also open-sourcing(opens in a new window) the technology that powers Instant Checkout, the…

Addendum to GPT-5 system card: GPT-5-Codex

Addendum to GPT‑5 system card: GPT‑5‑Codex GPT‑5‑Codex is a version of GPT‑5 optimized for agentic coding in Codex. Like its predecessor, codex-1, this model was trained using reinforcement learning on real-world coding tasks in a variety of environments to generate code that closely mirrors human style and PR preferences, adhere precisely to instructions, and iteratively run tests until passing results are achieved. This model is available locally in the terminal or IDE through Codex CLI and IDE extension, and on the cloud via the Codex web, GitHub, and the ChatGPT mobile app. This addendum outlines the comprehensive safety measures implemented for GPT‑5‑Codex. It details both model-level mitigations, such as specialized safety training for harmful tasks and prompt injections, and product-level mitigations like agent sandboxing and configurable network access.

PyTorch Foundation Announces Safetensors as Newest Contributed Project to Secure AI Model Execution

Safetensors is welcomed into the PyTorch Foundation to secure model distribution and build trusted agentic solutions PARIS – PyTorch Conference EU – April 8, 2026 – The PyTorch Foundation, a community-driven hub for open source AI under the Linux Foundation, today announced that Safetensors has joined the Foundation as its newest foundation-hosted project alongside DeepSpeed, Helion, PyTorch, Ray, and vLLM. Safetensors’ contribution by Hugging Face prevents arbitrary code execution risks and enhances model performance across multi-GPU and multi-node deployments, addressing growing technical needs of the AI era. As AI model development accelerates, security risks in the production pipeline inherently increase, necessitating secure, high-performance formats that can keep pace with deployment. Safetensors joining the Foundation minimizes security risks associated with model architectures and execution, providing developers with a trusted path to production. “Safetensors’ contribution to the PyTorch Foundation is an important…

Quoting Boris Mann

13th May 2026 “11 AI agents” is meaningless as a phrase. If I said “I have 11 spreadsheets” or “I have 11 browser tabs” to do my work, it means about the same thing. Recent articles - Notes on the xAI/Anthropic data center deal - 7th May 2026 - Live blog: Code w/ Claude 2026 - 6th May 2026 - Vibe coding and agentic engineering are getting closer than I'd like - 6th May 2026

Welcome to the Datasette blog

13th May 2026 - Link Blog Welcome to the Datasette blog. We have a bunch of neat Datasette announcements in the pipeline so we decided it was time the project grew an official blog. I built this using OpenAI Codex desktop, which turns out to have the Markdown session transcript export feature I've always wanted. Here's the session that built the blog. See also issue 179. Recent articles - Notes on the xAI/Anthropic data center deal - 7th May 2026 - Live blog: Code w/ Claude 2026 - 6th May 2026 - Vibe coding and agentic engineering are getting closer than I'd like - 6th May 2026

Quoting Mitchell Hashimoto

12th May 2026 The thing about 90% of TDMs [Technical Decision Makers] is that they're motivated primarily by NOT GETTING FIRED. These aren't people who browser Lobsters or push to GH on the weekend. These are people that work 9 to 5, get paid, go home, and NEVER THINK ABOUT WORK AGAIN. So to achieve all that, they follow secular trends supported by analysts and broad public sentiment. Oh, Gartner said that "AI strategy" is most important? McKinsey said "context" needs to be managed? Well, "Context Engine for AI Apps" is going to be defensible. Buy it. — Mitchell Hashimoto, in a conversation about the design of the Redis homepage Recent articles - Notes on the xAI/Anthropic data center deal - 7th May 2026 - Live blog: Code w/ Claude 2026 - 6th May 2026 - Vibe coding and agentic…

Learning on the Shop floor

11th May 2026 - Link Blog Learning on the Shop floor. Tobias Lütke describes Shopify's internal coding agent tool, River, which operates entirely in public on their Slack: River does not respond to direct messages. She politely declines and suggests to create a public channel for you and her to start working in. I myself work with river in #tobi_river channel and many followed this pattern. Every conversation is therefore searchable. Anyone at Shopify can jump in. In my own channel, there are over 100 people who, react to threads, add color and add context, pick up the torch, help with the reviews, remind me how rusty I am, and importantly, learn from watching. [...]As so often with German, there is a word for the kind of environment: Lehrwerkstatt. Literally: A teaching workshop. The whole shop floor is the classroom.…

GitLab Act 2

11th May 2026 - Link Blog GitLab Act 2 (via) There's a lot going on in this announcement from GitLab about the "workforce reduction" and "structural and strategic decisions" they are making with respect to the agentic era. - They're "planning to reduce the number of countries by up to 30% where we have small teams". One of the most interesting things about GitLab is that they have employees spread across a large number of countries - 18 are listed in their public employee handbook but this post says they are "operating in nearly 60 countries". That handbook used to document their payroll workflows for those countries too - they stopped publishing that in 2023 but the last public version (hooray for version control) remains a fascinating read. Since we don't know which of those 60 countries have small teams,…

Quoting New York Times Editors’ Note

10th May 2026 This article was updated after The Times learned that a remark attributed to Pierre Poilievre, the Conservative leader, was in fact an A.I.-generated summary of his views about Canadian politics that A.I. rendered as a quotation. The reporter should have checked the accuracy of what the A.I. tool returned. The article now accurately quotes from a speech delivered by Mr. Poilievre in April. [...] He did not refer to politicians who changed allegiances as turncoats in that speech. Recent articles - Notes on the xAI/Anthropic data center deal - 7th May 2026 - Live blog: Code w/ Claude 2026 - 6th May 2026 - Vibe coding and agentic engineering are getting closer than I'd like - 6th May 2026

Vibe coding and agentic engineering are getting closer than I'd like

Vibe coding and agentic engineering are getting closer than I’d like 6th May 2026 I recently talked with Joseph Ruscio about AI coding tools for Heavybit’s High Leverage podcast: Ep. #9, The AI Coding Paradigm Shift with Simon Willison. Here are some of my highlights, including my disturbing realization that vibe coding and agentic engineering have started to converge in my own work. One thing I really enjoy about podcasts is that they sometimes push me to think out loud in a way that exposes an idea I’ve not previously been able to put into words. Vibe coding and agentic engineering are starting to overlap A few weeks after vibe coding was first coined I published Not all AI-assisted programming is vibe coding (but vibe coding rocks), where I firmly staked out my belief that “vibe coding” is a very…

Codex CLI 0.128.0 adds /goal

30th April 2026 - Link Blog Codex CLI 0.128.0 adds /goal. The latest version of OpenAI's Codex CLI coding agent adds their own version of the Ralph loop: you can now set a /goal and Codex will keep on looping until it evaluates that the goal has been completed... or the configured token budget has been exhausted. It looks like the feature is mainly implemented though the goals/continuation.md and goals/budget_limit.md prompts, which are automatically injected at the end of a turn. Recent articles - LLM 0.32a0 is a major backwards-compatible refactor - 29th April 2026 - Tracking the history of the now-deceased OpenAI Microsoft AGI clause - 27th April 2026 - DeepSeek V4 - almost on the frontier, a fraction of the price - 24th April 2026

Quoting Andrew Kelley

30th April 2026 It's a common misconception that we can't tell who is using LLM and who is not. I'm sure we didn't catch 100% of LLM-assisted PRs over the past few months, but the kind of mistakes humans make are fundamentally different than LLM hallucinations, making them easy to spot. Furthermore, people who come from the world of agentic coding have a certain digital smell that is not obvious to them but is obvious to those who abstain. It's like when a smoker walks into the room, everybody who doesn't smoke instantly knows it. I'm not telling you not to smoke, but I am telling you not to smoke in my house. — Andrew Kelley, Creator of Zig Recent articles - LLM 0.32a0 is a major backwards-compatible refactor - 29th April 2026 - Tracking the history of the now-deceased…

Quoting Matthew Yglesias

28th April 2026 Five months in, I think I've decided that I don't want to vibecode — I want professionally managed software companies to use AI coding assistance to make more/better/cheaper software products that they sell to me for money. Recent articles - Tracking the history of the now-deceased OpenAI Microsoft AGI clause - 27th April 2026 - DeepSeek V4 - almost on the frontier, a fraction of the price - 24th April 2026 - Extract PDF text in your browser with LiteParse for the web - 23rd April 2026

Adding a new content type to my blog-to-newsletter tool

Guides > Agentic Engineering Patterns Adding a new content type to my blog-to-newsletter tool Here's an example of a deceptively short prompt that got a quite a lot of work done in a single shot. First, some background. I send out a free Substack newsletter around once a week containing content copied-and-pasted from my blog. I'm effectively using Substack as a lightweight way to allow people to subscribe to my blog via email. I generate the newsletter with my blog-to-newsletter tool - an HTML and JavaScript app that fetches my latest content from this Datasette instance and formats it as rich text HTML, which I can then copy to my clipboard and paste into the Substack editor. Here's a detailed explanation of how that works. I recently added a new type of content to my blog to capture content that…

OpenAI just released its answer to Claude Mythos

OpenAI is launching Daybreak, an AI initiative focused on detecting and patching vulnerabilities before attackers find them. Daybreak uses the Codex Security AI agent that launched in March to create a threat model based on an organization’s code and focus on possible attack paths, validate likely vulnerabilities, and then automate the detection of the higher risk ones. OpenAI just released its answer to Claude Mythos OpenAI’s Daybreak combines GPT-5.5-Cyber and Codex Security. OpenAI’s Daybreak combines GPT-5.5-Cyber and Codex Security. Its launch comes just over a month after rival Anthropic announced Claude Mythos, a security-focused AI model it claimed was too dangerous to publicly release and only shared privately as a part of its own initiative, dubbed Project Glasswing. Still, that didn’t stop at least a few unauthorized parties from getting access. However, OpenAI has so far lacked a similar security…

Microsoft wants lawyers to trust its new AI agent in Word documents

Microsoft is launching a new AI agent inside Word that’s specifically designed for legal teams. Legal Agent handles document edits, negotiation history, and complex documents to help legal teams handle tasks like reviewing contracts. Microsoft wants lawyers to trust its new AI agent in Word documents Microsoft’s Legal Agent comes from the work of former Robin AI engineers. Microsoft’s Legal Agent comes from the work of former Robin AI engineers. “Instead of relying on general AI models to interpret commands, the agent follows structured workflows shaped by real legal practice, managing clearly defined, repeatable tasks like reviewing contracts clause by clause against a playbook,” explains Sumit Chauhan, corporate vice president of Microsoft’s Office Product Group. The Legal Agent can work with existing documents that have tracked changes, and analyze agreements and contracts to “spot risks and obligations.” Microsoft is releasing…

OpenAI Really Wants Codex to Shut Up About Goblins

OpenAI has a goblin problem. Instructions designed to guide the behavior of the company’s latest model as it writes code have been revealed to include a line, repeated several times, that specifically forbids it from randomly mentioning an assortment of mythical and real creatures. “Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user’s query,” read instructions in Codex CLI, a command-line tool for using AI to generate code. It is unclear why OpenAI felt compelled to spell this out for Codex—or indeed why its models might want to discuss goblins or pigeons in the first place. The company did not immediately respond to a request for comment. OpenAI’s newest model, GPT-5.5, was released with enhanced coding skills earlier this month. The company is in a…

The Race Is on to Keep AI Agents From Running Wild With Your Credit Cards

Between malware, online impersonation, and account takeovers, there are enough digital security problems out there as it is. And with the rise of agentic AI, more activity is being carried out by agents on behalf of humans—creating different risks that something could go awry. Now, working with initial contributions from Google and Mastercard, the authentication-focused industry association known as the FIDO Alliance said on Tuesday that it will launch a pair of working groups to develop industry standards for validating and protecting payments and other transactions carried out by AI agents. The goal is to produce a protective baseline that can be adopted across industries. This way, users can authorize agent actions using mechanisms that can't easily be phished, or taken over by a bad actor to give an agent rogue instructions. The standards would also include cryptographic tools that…