# ai-agents Published articles for ai-agents. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Why human oversight is shifting from writing code to defining requirements DevFeed: [Why human oversight is shifting from writing code to defining requirements]() Original publisher: [Read original article]() Author: Naseeb Ahmed Mian Published: 2026-09-17T13:00:00Z Content type: opinion Language: en Sources: [The New Stack]() Topics: [Requirements](), [AI Agent](), [Code](), [Availability]() Tags: [agent](), [agents](), [ai-agents](), [ai-engineering](), [andela](), [automated](), [availability](), [code](), [pipeline](), [post-contributed](), [requirements](), [review](), [software-development](), [sponsor-andela](), [sponsored-post-contributed](), [tests]() ### AI overview The article argues that human oversight of AI-assisted software development should focus on defining and reviewing requirements, not only checking whether generated code conforms to them. It illustrates the risk with a flawed availability-related requirement that passed specification review, generated six passing tests, traceability checks, and automated QA while violating the feature's intended outcome. ### Source excerpt This walks through the pipeline our agents operate inside--from a recorded scoping meeting through unit specs, spec review, generated code, The post Why human oversight is shifting from writing code to defining requirements appeared first on The New Stack. ## How API Design Is Evolving for AI Agents, Security, Reliability, and Compliance DevFeed: [How API Design Is Evolving for AI Agents, Security, Reliability, and Compliance]() Original publisher: [Read original article]() Author: J Simpson Published: 2026-09-17T07:00:00Z Content type: article Language: en Sources: [Nordic APIs]() Topics: [API](), [Architecture & Design](), [Artificial Intelligence](), [reliability](), [Security]() Tags: [agents](), [ai-agents](), [api](), [api-architecture](), [api-as-a-product](), [api-design](), [api-discovery](), [api-security](), [architecture](), [authorization](), [blog](), [mcp](), [microservices](), [rate-limiting](), [reliability](), [security]() ### AI overview This article examines six ways API design is evolving as APIs serve AI agents and other machine consumers, with implications for security, reliability, and regulatory compliance. ### Source excerpt For over two decades, API design and architecture remained remarkably consistent. Perhaps it's the outsized influence of Roy Fielding's RESTful dissertation, but API designers have stuck to the principles of stateless architecture, resource-based endpoints, and HTTP commands to an impressive degree. That's all starting to change, now that we've radically recontextualized the way we use ... ## Migrating the GitHub Copilot runtime to Rust, using Copilot DevFeed: [Migrating the GitHub Copilot runtime to Rust, using Copilot]() Original publisher: [Read original article]() Author: Stephen Toub Published: 2026-09-17T00:26:43Z Content type: article Language: en Sources: [GitHub Engineering]() Topics: [GitHub Copilot](), [Rust](), [Agent Harness](), [GitHub Copilot SDK](), [GitHub]() Tags: [agent-harness](), [ai-agents](), [ai-ml](), [developer-experience](), [generative-ai](), [github](), [github-copilot](), [github-copilot-sdk](), [rust]() ### AI overview The GitHub Copilot agent runtime was rewritten from TypeScript on Node.js and V8 into more than 800,000 lines of production Rust. AI agents wrote most of the code across 128 pull requests, and the runtime was shipped incrementally while regressions were fixed and performance improved by orders of magnitude. ### Source excerpt A rewrite this size wasn't affordable before agents. Here's what porting the Copilot agent runtime to 800,000 lines of production Rust actually took. The post Migrating the GitHub Copilot runtime to Rust, using Copilot appeared first on The GitHub Blog. ## Intern 2 offers an isolated environment and cloud inference for running personal bots DevFeed: [Intern 2 offers an isolated environment and cloud inference for running personal bots]() Original publisher: [Read original article]() Author: Thomas Claburn Published: 2026-09-16T23:28:59Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [Bots](), [Mac Mini](), [Inference]() Tags: [ai-agents](), [ai-and-ml](), [automate](), [autonomous-ai](), [bots](), [edge-computing](), [inference](), [intern-2](), [mac-mini](), [open-source](), [personal-tech]() ### AI overview Intern 2 provides an isolated environment and cloud inference for automating tasks with personal bots. ### Source excerpt 'Intern 2' offers an isolated environment and cloudy inference to automate your world ## Perplexity's AI agents helped build a database. They weren't allowed to run it. DevFeed: [Perplexity's AI agents helped build a database. They weren't allowed to run it.]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-16T21:51:15Z Content type: article Language: en Sources: [The New Stack]() Topics: [Database](), [DynamoDB](), [Rust](), [Artificial Intelligence](), [rocksdb](), [Amazon S3]() Tags: [agents](), [ai](), [ai-agents](), [ai-infrastructure](), [api](), [coding](), [coding-agents](), [database](), [databases](), [dynamodb](), [open-source](), [performance](), [perplexity](), [rocksdb](), [rust](), [s3]() ### AI overview Perplexity built CobbleDB, a Rust key-value store, after finding DynamoDB too costly and insufficiently controllable for its search workload. Coding agents helped develop it, but were not allowed to run it in production. Perplexity measured lower read latency and expects lower costs, with plans to open-source the database. ### Source excerpt Perplexity decided it was paying too much for DynamoDB and wasn't getting the control it wanted over read performance. So The post Perplexity's AI agents helped build a database. They weren't allowed to run it. appeared first on The New Stack. ## Zed launches Delta public beta for collaborative code review with developers and coding agents DevFeed: [Zed launches Delta public beta for collaborative code review with developers and coding agents]() Original publisher: [Read original article]() Author: Paul Sawers Published: 2026-09-16T19:45:30Z Content type: article Language: en Sources: [The New Stack]() Topics: [GitHub](), [AI-assisted coding](), [pull-requests](), [Developer Platform](), [Tooling]() Tags: [ai](), [ai-agents](), [beta](), [coding-agents](), [collaboration](), [developer](), [developer-tools](), [github](), [pull-requests](), [review](), [software-development]() ### AI overview Zed has announced the public beta of Delta, a collaborative environment where developers and coding agents work, review, and revise code together in shared threads rather than pull requests. The article presents Delta as a response to the volume of agent-generated code and limitations of the pull-request model. ### Source excerpt Something of a consensus has emerged from the developer fraternity in 2026 -- GitHub, a platform built substantively for human The post "Everyone's in a race to replace GitHub": Zed launches Delta because agents made pull requests obsolete appeared first on The New Stack. ## Improving HCLS AI reasoning with open-source agent skills DevFeed: [Improving HCLS AI reasoning with open-source agent skills]() Original publisher: [Read original article]() Author: Michael Hsieh Published: 2026-09-16T19:00:00Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Agent Skills](), [Artificial Intelligence](), [foundation-models](), [Open Source](), [Bioinformatics](), [Medical imaging]() Tags: [agent-skills](), [agents](), [ai](), [ai-agents](), [amazon-bedrock](), [amazon-quick-suite](), [announcements](), [artificial-intelligence](), [foundation-models](), [generative-ai](), [healthcare](), [healthcare-and-life-sciences](), [intermediate-200](), [kiro](), [life-sciences](), [medical-imaging](), [open-source](), [strands-agents]() ### AI overview This post presents 38 open-source agent skills spanning 11 healthcare and life sciences domains. The skills encode domain decision procedures for AI agents, and the reported evaluation found a 70-86% head-to-head win rate over agents without the skills. ### Source excerpt AI agents on foundation models often misapply healthcare and life sciences decision frameworks, citing the right guideline but applying it incorrectly. This post shares 38 open-source agent skills across 11 HCLS domains that close this gap, with installation steps, three worked use cases, and a 410-prompt evaluation showing a 70-86% win rate. ## Mem0 joins the Vercel Marketplace DevFeed: [Mem0 joins the Vercel Marketplace]() Original publisher: [Read original article]() Author: Tony Pan Published: 2026-09-16T17:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [Artificial Intelligence](), [App](), [context](), [API keys](), [Command-line interface](), [Environment Variables]() Tags: [ai](), [ai-agents](), [app](), [billing](), [cli](), [context](), [environment-variables](), [free](), [giving](), [install](), [integration](), [vercel](), [volume]() ### AI overview Mem0 is now a native integration on the Vercel Marketplace, providing long-term memory for AI agents and apps across sessions. The integration includes billing through Vercel, automatic provisioning of a scoped Mem0 project and API key, and environment-variable configuration. Mem0 offers a free plan with usage limits and a $20-per-month plan for higher volume. ### Source excerpt Mem0 is now available as a native integration on the Vercel Marketplace, giving your AI agents and apps long-term memory. Mem0 remembers user preferences, facts, and context across sessions, so your app stops starting from scratch. Install from the Marketplace with integrated billing on your Vercel invoice, no separate account or key management. A scoped Mem0 project and API key are provisioned automatically and added to your project as environment variables, including MEM0_API_KEY. Mem0 offers a free plan with usage limits and a $20 per month plan for higher volume. To see it end to end, deploy the eve Memory Agent template, an eve agent with long-term memory powered by Mem0. Get started with Mem0 on the Vercel Marketplace or through the Vercel CLI, available to customers on all plans. Read more ## Anthropic merges Claude Chat and Cowork into one interface DevFeed: [Anthropic merges Claude Chat and Cowork into one interface]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-16T16:46:36Z Content type: news Language: en Sources: [The New Stack]() Topics: [anthropic](), [Claude]() Tags: [ai-agents](), [ai-infrastructure](), [anthropic](), [chat](), [claude](), [connectors](), [context](), [developer-tools](), [tools](), [workflow]() ### AI overview Anthropic is merging Claude Chat and Cowork into a single interface, allowing conversations to handle simple questions, multi-step projects, connected tools, and background execution. Claude Docs and Claude Slides are launching in beta on paid plans, while Claude Design is moving into conversations. ### Source excerpt Using Claude for anything beyond a quick question has always started with a routing decision to use Chat or Cowork? The post Anthropic bet users were choosing wrong. So it removed the choice. appeared first on The New Stack. ## Rider and ReSharper 2026.2.2 Are Out! DevFeed: [Rider and ReSharper 2026.2.2 Are Out!]() Original publisher: [Read original article]() Author: Alexander Kurakin Published: 2026-09-16T14:12:44Z Content type: release Language: en Sources: [The JetBrains Blog]() Topics: [rider](), [resharper](), [AI-assisted coding](), [test-coverage]() Tags: [2026](), [ai-agent-hooks](), [ai-agents](), [code-coverage](), [coverage](), [hooks](), [jetbrains](), [macos](), [mcp](), [net](), [net-tools](), [releases](), [resharper](), [rider](), [update]() ### AI overview JetBrains releases Rider, ReSharper, and .NET tools 2026.2.2, adding an AI Agent Setup widget in Rider, more targeted hook feedback for coding agents, TUnit coverage support, and fixes including a macOS freezing issue. ### Source excerpt We've released version 2026.2.2 of ReSharper, Rider, and .NET tools. You can install this update from inside the tools themselves, through the Toolbox App, or on our website. Here's what's new in this update. Rider 2026.2.2 AI Agent Setup widget We've recently added a wide range of features to help you work with coding agents [...] ## The Future of Data Engineering in the Age of AI | Erfan Hesami DevFeed: [The Future of Data Engineering in the Age of AI | Erfan Hesami]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-09-16T13:21:19Z Content type: article Language: en Sources: [Data Engineering Central]() Topics: [data-engineering](), [AI Engineering](), [agent observability](), [coding](), [Security]() Tags: [agents](), [ai](), [ai-agents](), [ai-engineering](), [coding](), [data-engineering](), [fundamentals](), [governance](), [security]() ### AI overview An interview with Erfan Hesami examines how AI and agents may change data engineering, including the evolving role of data engineers, the overlap with AI engineering, the continuing importance of fundamentals, and the need to manage governance, security, costs, technical debt, and human judgment. ### Source excerpt AI Agents, Coding & Fundamentals ## Constraining AI agents with Red Hat AI: Containment, identity, and governance DevFeed: [Constraining AI agents with Red Hat AI: Containment, identity, and governance]() Original publisher: [Read original article]() Author: Grace Ableidinger Published: 2026-09-16T13:01:59Z Content type: tutorial Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Artificial Intelligence](), [Security](), [Zero Trust](), [Containers](), [Kubernetes](), [Architecture & Design]() Tags: [ai](), [ai-agents](), [containers](), [deployment](), [kubernetes](), [prompt-injection](), [security](), [trust]() ### AI overview This tutorial explains how to secure AI agents running on Red Hat OpenShift using containment, verifiable identity, and governance. It covers namespace isolation, quotas, sandboxing, workload identity, and admission control, with OpenClaw used in the demo. ### Source excerpt When an agent process runs on your laptop, it typically inherits anything your user has access to. Often this includes the full network stack, the file system, and the credentials sitting in memory. When integrating with GitHub, Slack, or a cloud provider, you could be one faulty permission or well-crafted prompt injection away from a security incident. The post Constraining AI agents with Red Hat AI: Containment, identity, and governance appeared first on Red Hat Developer. ## TypeSafe AI debuts model for machines that plays Doom DevFeed: [TypeSafe AI debuts model for machines that plays Doom]() Original publisher: [Read original article]() Author: Thomas Claburn Published: 2026-09-16T01:35:16Z Content type: release Language: en Sources: [www.theregister.com - Articles]() Topics: [Artificial Intelligence](), [ai and ml]() Tags: [ai](), [ai-agents](), [ai-and-ml](), [artificial-intelligence](), [jev](), [large-language-models](), [model](), [typesafe-ai]() ### AI overview TypeSafe AI introduced Jev, a model that produces typed probabilistic decisions rather than conversational responses. ### Source excerpt 'Jev' doesn't chat. It produces typed probabilistic decisions ## Build zero-trust AI agents that judge intent, not just syntax DevFeed: [Build zero-trust AI agents that judge intent, not just syntax]() Original publisher: [Read original article]() Author: Eric Dong; Shubham Saboo Published: 2026-09-16T01:23:36.799575Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [Artificial Intelligence](), [Security](), [Zero Trust]() Tags: [agents](), [ai](), [ai-agents](), [gemini](), [model-context-protocol](), [python](), [security](), [zero-trust]() ### AI overview This blog post explains how to move AI-agent security from static, build-time controls to dynamic runtime governance on the Gemini Enterprise Agent Platform. It covers Model Armor, Semantic Governance Policies, and Agent Anomaly Detection with Closed-Loop Remediation for evaluating intent, enforcing business rules, and detecting multi-turn attacks. ### Source excerpt This blog post explores how to transition AI agents from static, build-time security controls to dynamic runtime governance using the Gemini Enterprise Agent Platform. It highlights three primary managed defenses: Model Armor for screening edge prompts, Semantic Governance Policies for evaluating tool intent against business rules, and Agent Anomaly Detection for catching multi-turn exploits. By shifting these capabilities to the platform level, security administrators can dynamically enforce policies and neutralize complex attacks without needing to modify or redeploy the agent's underlying code. ## Turso Accident Protection Enables Database Restoration for Up to 5 Days DevFeed: [Turso Accident Protection Enables Database Restoration for Up to 5 Days]() Original publisher: [Read original article]() Author: Pedro Muniz Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Turso Blog]() Topics: [Turso](), [Databases](), [MCP Server]() Tags: [ai-agents](), [database](), [databases](), [feature](), [mcp](), [mcp-server](), [turso]() ### AI overview Turso has released Accident Protection, allowing databases deleted on a paid plan to be restored for up to five days at no additional cost. The feature is available through the UI and Turso MCP server. ### Source excerpt Agents delete things. Now you can undelete them. Restore databases for up to 5 days after deletion, on any paid plan, at no additional cost. ## Highlights from the 2026 State of Knowledge Report DevFeed: [Highlights from the 2026 State of Knowledge Report]() Original publisher: [Read original article]() Author: Lauren Volpi Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Mintlify Blog]() Topics: [Documentation](), [Artificial Intelligence](), [HTML](), [Website](), [MCP]() Tags: [2026](), [ai](), [ai-agents](), [ai-trends](), [documentation](), [llms-txt](), [markdown](), [mcp](), [report]() ### AI overview Mintlify's 2026 State of Knowledge Report examines how AI agents are changing documentation readership and access. It reports that agent requests exceeded human page loads on Mintlify-powered sites in August, with many agents using Markdown, llms.txt, agent skills, and MCP tool calls. Controlled tests across 20 documentation sites found that linking Markdown pages to llms.txt improved agent navigation and reduced token consumption. ### Source excerpt AI agents overtook humans as the primary readers across Mintlify-powered sites. In August alone, agents made 257 million web requests compared to 131 million human page loads, which means agent traffic now outnumbers human traffic by nearly 2:1. ## OpenAI's Greg Brockman discusses computer use as an alternative to purpose-built AI agent integrations DevFeed: [OpenAI's Greg Brockman discusses computer use as an alternative to purpose-built AI agent integrations]() Original publisher: [Read original article]() Author: Meredith Shubel Published: 2026-09-15T23:41:22Z Content type: opinion Language: en Sources: [The New Stack]() Topics: [computer-use](), [OpenAI](), [Model Context Protocol](), [Reinforcement learning]() Tags: [ai-agents](), [computer-use](), [developer-tools](), [developers](), [mcp](), [model-context-protocol](), [openai](), [reinforcement-learning]() ### AI overview OpenAI president and co-founder Greg Brockman discusses whether AI agents could use computers through screens, keyboards, and mice instead of relying on purpose-built MCP servers, CLIs, APIs, and other integrations. ### Source excerpt This week on the a16z show, Greg Brockman, president and co-founder of OpenAI, made the point that developers have been The post OpenAI president: "The computer should be there to empower you." So stop retooling software for AI agents appeared first on The New Stack. ## Meta lets Claude and Codex configure WhatsApp Business via MCP DevFeed: [Meta lets Claude and Codex configure WhatsApp Business via MCP]() Original publisher: [Read original article]() Author: Paul Sawers Published: 2026-09-15T22:08:56Z Content type: news Language: en Sources: [The New Stack]() Topics: [Meta](), [Model Context Protocol](), [AI-assisted coding](), [Claude](), [codex](), [API]() Tags: [ai](), [ai-agents](), [ai-coding-agents](), [api](), [claude](), [codex](), [developer-tools](), [mcp](), [mcp-server](), [meta](), [model-context-protocol](), [product](), [updates]() ### AI overview Meta announced a WhatsApp Business Tools MCP server that lets developers connect Claude or Codex to selected businesses they administer. The agents can help configure WhatsApp Business accounts, verify phone numbers, manage templates, and perform related API tasks, with access scoped to the businesses the developer selects. ### Source excerpt Any business worth its salt in 2026 needs to be embracing the right tools to reach its customers, and few The post Meta lets Claude and Codex configure WhatsApp Business via MCP appeared first on The New Stack. ## Google and OpenAI take different approaches to reducing voice-agent latency DevFeed: [Google and OpenAI take different approaches to reducing voice-agent latency]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-15T21:50:15Z Content type: comparison Language: en Sources: [The New Stack]() Topics: [Latency](), [Google AI](), [OpenAI](), [API](), [Orchestration](), [real-time](), [Artificial Intelligence]() Tags: [ai](), [ai-agents](), [ai-engineering](), [api](), [comparison](), [google-ai](), [large-language-models](), [latency](), [openai](), [orchestration](), [real-time]() ### AI overview The article compares Google's Gemini 3.8 Live Extended Thinking with OpenAI's GPT-Live-1 for reducing latency in voice agents. Google keeps speech, reasoning, and asynchronous tool execution in one stateful session, while OpenAI uses a real-time conversation model alongside a backend reasoning model, shifting more orchestration to the application. ### Source excerpt Voice agents have a latency problem that shows up as soon as they have to do real work. Within five The post OpenAI's voice model doesn't think. That's the point. appeared first on The New Stack. ## AWS agents will suggest your new flights. Code decides what gets booked. DevFeed: [AWS agents will suggest your new flights. Code decides what gets booked.]() Original publisher: [Read original article]() Author: Meredith Shubel Published: 2026-09-15T21:27:21Z Content type: news Language: en Sources: [The New Stack]() Topics: [AWS Step Functions](), [Amazon Bedrock AgentCore](), [Orchestration](), [Generative AI](), [Amazon Web Services]() Tags: [ai-agents](), [ai-engineering](), [airline](), [amazon-bedrock-agentcore](), [aws](), [aws-step-functions](), [cloud-services](), [generative-ai](), [orchestration]() ### AI overview AWS published a Step Functions pattern for airline rebooking in which Amazon Bedrock AgentCore agents propose itineraries and compensation messages, while deterministic workflow steps validate proposals before reservations change or payments are issued. ### Source excerpt AWS published a new Step Functions pattern this week that gives AI agents a role in airline rebooking while keeping The post AWS agents will suggest your new flights. Code decides what gets booked. appeared first on The New Stack. ## The 3 Layers of Testing AI Agents DevFeed: [The 3 Layers of Testing AI Agents]() Original publisher: [Read original article]() Author: The T-Shaped Dev Published: 2026-09-15T20:59:04Z Content type: tutorial Language: en Sources: [The T-Shaped Dev]() Topics: [Testing](), [Artificial Intelligence]() Tags: [agents](), [ai](), [ai-agents](), [how-to](), [testing]() ### AI overview The article presents a three-layer approach to testing AI agents: deterministic tests for surrounding code such as tools, schemas, routing, state, and failure policies; judged scenarios for model behavior; and continuous production evaluations. ### Source excerpt Draw the line between the model and the machine, and 80% of agent testing is testing you already know how to do. (7 min) ## Your AI agents' reports and questions have a new inbox, courtesy of AWS DevFeed: [Your AI agents' reports and questions have a new inbox, courtesy of AWS]() Original publisher: [Read original article]() Author: Brandon Vigliarolo Published: 2026-09-15T20:25:44Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [Artificial Intelligence](), [Bot]() Tags: [agentic-ai](), [agents](), [ai](), [ai-agents](), [ai-and-ml](), [artificial-intelligence](), [aws](), [open-source]() ### AI overview AWS is associated with a new inbox for AI agents' reports and questions. The article mentions Pizza Bot, a local application that provides an email-like interface for interacting with AI agents. ### Source excerpt Pizza Bot runs locally and lets users interact with AI agents in an email-like interface. Perfect for blowing them off just like your human colleagues! ## Agents operate, humans govern: Scale your operations and reduce toil with Azure SRE Agent DevFeed: [Agents operate, humans govern: Scale your operations and reduce toil with Azure SRE Agent]() Original publisher: [Read original article]() Author: TNS Staff Published: 2026-09-15T16:21:45Z Content type: article Language: en Sources: [The New Stack]() Topics: [SRE](), [incident](), [Azure](), [observability](), [DevOps](), [Redis]() Tags: [agent](), [ai-agents](), [ai-operations](), [azure](), [code-review](), [deployment](), [devops](), [incident](), [microsoft-azure](), [monitoring](), [observability](), [post](), [redis](), [sponsor-microsoft-azure](), [sponsored](), [sponsored-post](), [sre](), [telemetry]() ### AI overview The article describes Azure SRE Agent as a system that analyzes telemetry, correlates deployment and monitoring data, investigates incidents, identifies root causes, recommends or prepares fixes, and supports mitigation and other operational tasks under human approval. It cites examples involving Microsoft service teams and InEight, including a recommendation to scale Redis. ### Source excerpt What if engineers could spend their time building and optimizing systems rather than maintaining them? It's 3 a.m., and the The post Agents operate, humans govern: Scale your operations and reduce toil with Azure SRE Agent appeared first on The New Stack. ## From Atlas experiment to Airlock: extracting agent governance into a product DevFeed: [From Atlas experiment to Airlock: extracting agent governance into a product]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-15T15:35:36Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Authorization](), [Security](), [Artificial Intelligence](), [App](), [Linear](), [Slack]() Tags: [agent](), [ai](), [ai-agents](), [authorization](), [email](), [integrations](), [linear](), [permission](), [policy](), [product](), [security](), [slack]() ### AI overview The article explains how WorkOS's Airlock grew from the Atlas experiment into a standalone authorization product for governing AI-agent actions in company tools. A demonstration shows policies blocking emails containing financial information and requiring approval for unfamiliar distribution lists. ### Source excerpt Airlock grew out of Atlas to give IT and security teams a shared way to govern agent actions. Aaron Tainter's Agent Night demo shows how it works. [Next page]()