# AI Agent Published articles for AI Agent. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## TensorRT Edge-LLM Completes the MLPerf Edge Agentic Benchmark 6.4x Faster on Jetson AGX Thor DevFeed: [TensorRT Edge-LLM Completes the MLPerf Edge Agentic Benchmark 6.4x Faster on Jetson AGX Thor]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-16T20:37:07Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Jetson AGX Thor Developer Kit](), [Inference Performance](), [benchmarking](), [TensorRT](), [Large Language Model](), [quantization](), [Artificial Intelligence]() Tags: [agentic-ai-generative-ai](), [ai-agent](), [ai-inference](), [evaluation](), [jetson](), [jetson-agx-thor-developer-kit](), [llm](), [llm-benchmarking](), [mlperf](), [nvidia](), [performance](), [quantization](), [tensorrt](), [tensorrt-llm](), [thor]() ### AI overview This article reports that NVIDIA TensorRT Edge-LLM ran Qwen3.6-27B on a single NVIDIA Jetson AGX Thor Developer Kit for the MLPerf Inference v6.1 Edge Agentic benchmark. Using NVFP4 quantization, tree-based multi-token prediction, and KV cache reuse, it achieved 52.33 tokens per second and completed 1,007 turns in 24 minutes and 36 seconds, 6.4 times faster than the llama.cpp reference submission. ### Source excerpt AI agents are moving from cloud data centers to vehicles, robots, and other edge devices. Unlike a chatbot that answers a single prompt, an agent works through... ## AI Agent Governance: Why It Belongs in Your Platform DevFeed: [AI Agent Governance: Why It Belongs in Your Platform]() Original publisher: [Read original article]() Author: Prateek Mittal Published: 2026-09-16T20:28:57.610955Z Content type: article Language: en Sources: [Harness Blog]() Topics: [AI Agent](), [Artificial Intelligence](), [observability](), [audit](), [test](), [Deployment](), [Monitoring](), [pull-requests]() Tags: [ai](), [ai-agent](), [audit](), [deployment](), [governance](), [monitoring](), [observability](), [pull-requests](), [test]() ### AI overview Engineering leaders from Yelp, Platformable, and Harness argue that AI agent governance must be automated, continuously enforced, and built into the platform rather than left to policy documents. The article discusses audit trails, agent-to-agent access controls, experiment tracking, testing, latency monitoring, rollback paths, and observability for agent-driven changes. ### Source excerpt Engineering leaders from Yelp, Platformable, and Harness explain why AI agent governance has to be built into the platform, not a policy doc. | Blog ## Translating CUDA Tile Operations from Python to Rust Using Agentic AI DevFeed: [Translating CUDA Tile Operations from Python to Rust Using Agentic AI]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-16T16:28:59Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [CUDA Tile](), [Rust](), [Agent Skill](), [Artificial Intelligence](), [AI Agent](), [GPU](), [benchmarking](), [Compiler]() Tags: [agent-skill](), [agentic-ai](), [agentic-ai-generative-ai](), [ai-agent](), [benchmarking](), [cuda](), [cuda-tile](), [cutile](), [developer-tools-techniques](), [gpu](), [mixture-of-experts-moe](), [python](), [rust]() ### AI overview This NVIDIA developer article explains a multi-agent workflow for translating cuTile Python and Triton-TileIR GPU kernels into cuTile Rust. The team ported 24 public TileGym operators, covering about 40 kernels, and achieved 99.5% of cuTile Python performance on average, with correctness and performance checks at each stage. ### Source excerpt cuTile Rust (cutile-rs) is a tile-based system for safe, idiomatic GPU kernel authoring in the Rust programming language. Extending the Rust ownership model to... ## Muse review: The personal AI agent that gets consumer UX right DevFeed: [Muse review: The personal AI agent that gets consumer UX right]() Original publisher: [Read original article]() Author: Claire Vo Published: 2026-09-16T12:02:14Z Content type: opinion Language: en Sources: [Lenny's Newsletter]() Topics: [AI Agent](), [User experience (UX)](), [Meta](), [browser](), [pdf](), [codex]() Tags: [agent](), [ai](), [ai-agent](), [browser](), [codex](), [comparison](), [meta](), [muse](), [pdf](), [product-design](), [ux]() ### AI overview This review tests Meta's Muse personal AI agent through onboarding, calendar management, goal setting, document generation, and browser-based shopping. It finds strong consumer UX, permission handling, activity-feed design, and avatar design, while browser shopping performance is mixed. ### Source excerpt Watch now (38 mins) | 🎙 I gave Meta's Muse my calendar, my email, and my kids' chaotic schedule, and it produced a one-shot family PDF more beautiful than anything I've ever made with Claude or Codex ## Build a WhatsApp AI agent with Appwrite Functions and TablesDB DevFeed: [Build a WhatsApp AI agent with Appwrite Functions and TablesDB]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [AI Agent](), [Messaging](), [Serverless](), [API](), [Database]() Tags: [ai](), [ai-agent](), [api](), [app](), [build](), [data](), [database](), [messages](), [messaging](), [meta](), [serverless](), [support](), [tutorial](), [tutorials](), [whatsapp]() ### AI overview This tutorial shows how to build a WhatsApp AI support agent with Appwrite Functions and TablesDB. One function receives and stores incoming messages, while a second function retrieves conversation history, uses an order-lookup tool, sends replies through Meta's WhatsApp Cloud API, and stores the responses. Conversation summaries help keep long threads within the model's token budget. ### Source excerpt Turn a WhatsApp number into an AI support agent. Two Appwrite Functions receive messages and reply, TablesDB keeps the conversation history, and a compaction step keeps the context small. ## How to test an AI agent policy before enforcing it DevFeed: [How to test an AI agent policy before enforcing it]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-15T22:03:24Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [AI Agent](), [Authorization](), [Testing](), [Access Control](), [Linear]() Tags: [access-control](), [ai-agent](), [authorization](), [how-to](), [testing]() ### AI overview This guide explains how to test authorization policies for AI agents before enforcing them. It presents reusable tests for role-based access control, task intent, organizational rules, and approval requirements, with checks against the connected service. ### Source excerpt Design and test AI agent policies that block unsafe actions and keep useful work moving. Learn to test intent, RBAC, and approvals, or try WorkOS Airlock. ## Who gets to define an AI agent's intent? DevFeed: [Who gets to define an AI agent's intent?]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-15T20:25:55Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [AI Agent](), [Artificial Intelligence]() Tags: [ai](), [ai-agent](), [identity](), [policy](), [scopes](), [tool](), [workflow]() ### AI overview The article explains how to define an AI agent's intent while preserving identity, resource-access controls, and company policy. It argues that the requester should specify the agent's purpose and intended effects, while the organization determines which actions are permitted. Using Airlock examples, it distinguishes task intent from policy decisions such as approval or denial of specific operations. ### Source excerpt An agent's task describes the work to do. Learn how to define useful intent while keeping identity, resource access, and company policy in force. ## Beyond the model: Engineering AI infra with scientific judgement DevFeed: [Beyond the model: Engineering AI infra with scientific judgement]() Original publisher: [Read original article]() Author: AirbnbEng Published: 2026-09-15T17:06:18Z Content type: article Language: en Sources: [The Airbnb Tech Blog - Medium]() Topics: [Agent Harness](), [AI Agent](), [Data Science](), [data](), [datasets](), [Large Language Model]() Tags: [agent-harness](), [ai](), [ai-agent](), [data](), [data-science](), [datasets](), [engineering](), [llms](), [quality](), [science](), [trust]() ### AI overview Airbnb describes an agent harness for data science that embeds scientific methodology around an AI model. The system guides agents through framing questions, selecting evidence, and recording decisions so unstructured-data investigations can be reproduced, audited, challenged, and extended across languages, geographies, and LLM-based products. ### Source excerpt How Airbnb's agent harness transforms unstructured data exploration by encoding scientific methodology into scalable, reproducible, and audit-ready infrastructure. By: Wren Dougherty Ask a coding agent to analyze 100,000 customer support conversations and within minutes you'll have a polished taxonomy, precise prevalence numbers, and an executive-ready summary. What you can't see is the investigation that produced them: the methods it chose, the evidence it weighed, how much to trust it, or whether a second request would agree. All that reaches you is the polish. The model is undeniably intelligent, but intelligence without methodology is not science. LLMs certainly make for confident scientists, but we need them to be responsible ones. Smarter models help, but intelligence has never been the whole of science, in people or in machines. The method is as much the product as the answer. That is the idea behind the agent harness we built for data science: the methodology itself, built as infrastructure around the model. It governs how an AI agent operates, from framing a question to selecting evidence to recording decisions, so results can be reproduced, audited, and challenged, and the method shared, inspected, and built on. The challenge of unstructured data exploration In 2025, Airbnb was preparing to launch an AI customer service assistant. Before it could ship, we needed to understand exactly what kinds of situations it would face in the real world. That included rare events that could be risky for AI to interact with, and involved examining their taxonomy and prevalence to create the datasets that would help us build a more responsible product. The investigative work to do this was rigorous, but the process was deeply artisanal. Months of high-touch iteration went into each investigation, from finding the right data, reviewing samples with experts, and generating representative datasets, and the method was manually curated across notebooks, tables, docs, and indiv ## Announcing Meta Business Messaging MCP: Set up and manage WhatsApp Business from your AI agent DevFeed: [Announcing Meta Business Messaging MCP: Set up and manage WhatsApp Business from your AI agent]() Original publisher: [Read original article]() Published: 2026-09-15T14:00:49Z Content type: release Language: en Sources: [Facebook Developers]() Topics: [Model Context Protocol](), [MCP Server](), [AI Agent](), [Messaging](), [Meta](), [API]() Tags: [agent](), [ai-agent](), [api](), [mcp](), [mcp-server](), [messaging](), [meta](), [testing](), [whatsapp](), [workflow]() ### AI overview Meta announces Business Messaging MCP, an MCP server that connects AI coding agents to the WhatsApp Business Platform. It supports account and phone-number setup, message templates, webhooks, and test messages within the developer workflow. ### Source excerpt Business Messaging MCP connects your AI coding agent to the WhatsApp Business Platform, so you can create accounts, add numbers, build templates, and send test messages without leaving your workflow. ## How to Run Codex in an Ubuntu Virtual Machine on Proxmox DevFeed: [How to Run Codex in an Ubuntu Virtual Machine on Proxmox]() Original publisher: [Read original article]() Author: Travis Henderson Published: 2026-09-15T12:00:11Z Content type: tutorial Language: en Sources: [Atomic Object]() Topics: [AI-assisted coding](), [codex](), [Linux](), [Ubuntu](), [Proxmox](), [OpenSSH](), [Authorization]() Tags: [ai-agent](), [ai-for-developers](), [codex](), [coding-agents](), [development](), [linux](), [openssh](), [permissions](), [proxmox](), [sandbox](), [ubuntu]() ### AI overview This guide explains how to isolate Codex in a headless Ubuntu virtual machine running on Proxmox, using a separate Linux account and OpenSSH. The setup limits the files, dependencies, and network access available to the agent while keeping administrator tasks separate, but it does not eliminate risks from credentials, network access, or mistakes pushed to GitHub. ### Source excerpt The more useful coding agents become, the more access they tend to need. Installing dependencies, running commands, changing files, and generally more autonomy are part of the appeal. They're also the point where I start thinking about what else the coding agent can reach, especially while these tools are changing so quickly. My answer is [...] The post Your Coding Agent Should be in Jail. Here's Why and How. appeared first on Atomic Spin. ## Grab's LLM-Kit Framework Standardizes More Than 500 Internal Agent Services DevFeed: [Grab's LLM-Kit Framework Standardizes More Than 500 Internal Agent Services]() Original publisher: [Read original article]() Author: Hien Luu Published: 2026-09-15T09:00:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Framework](), [AI Agent](), [Deployment](), [Model Context Protocol](), [agent observability](), [FastAPI](), [OpenTelemetry](), [GitLab]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [ai-ml-data-engineering](), [deployment](), [fastapi](), [framework](), [gitlab](), [grab-agent-platform](), [ml-data-engineering](), [model-context-protocol](), [news](), [opentelemetry](), [vault]() ### AI overview Grab's internal LLM-Kit framework standardizes more than 500 agent services by providing shared scaffolding for evaluation, tracing, secret handling, service discovery, and tool-server connections. The article reports that deploying a new agent service now takes about one hour instead of two weeks or more. ### Source excerpt Grab has implemented LLM-Kit, a framework that standardizes over 500 internal agent services. This system enhances service integration, evaluation, and secret handling, reducing the time to deploy new AI agents from two weeks to one hour. It centralizes infrastructure management, allowing runtime tool discovery and flexible model integration, while maintaining operational control. By Hien Luu ## How to operate shared platforms safely at agent scale DevFeed: [How to operate shared platforms safely at agent scale]() Original publisher: [Read original article]() Author: Candace Shamieh; T Zhang; Gabriele Baldoni Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Platform Engineering](), [Loop Engineering](), [AI Agent](), [sdlc](), [Latency](), [Low Latency](), [Concurrency](), [Software]() Tags: [agent](), [agent-observability](), [agentic-workflows](), [agents](), [ai](), [ai-agent](), [ci](), [concurrency](), [latency](), [low-latency](), [operational](), [performance](), [platform-engineering](), [platforms](), [queue](), [recovery](), [timeout]() ### AI overview This Datadog article explains how platform teams can operate shared platforms safely as AI agent workloads scale across teams. It discusses modeling demand across agent trajectories, planning capacity across dependencies such as CI queues and sandbox pools, handling contention and recovery behavior, and preserving control across system boundaries. ### Source excerpt Learn how Datadog models agent demand, allocates capacity under contention, and preserves control as AI agent workloads scale across shared platforms. ## Inside the LLM Call: GenAI Observability with OpenTelemetry DevFeed: [Inside the LLM Call: GenAI Observability with OpenTelemetry]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-09-14T16:56:42Z Content type: tutorial Language: en Sources: [Blog on OpenTelemetry]() Topics: [OpenTelemetry](), [agent observability](), [telemetry](), [AI Agent](), [Large Language Model]() Tags: [agent](), [ai-agent](), [claude-code](), [codex](), [observability](), [opentelemetry](), [token](), [tool](), [visibility]() ### AI overview This tutorial explains how OpenTelemetry Semantic Conventions for Generative AI record LLM calls, tool invocations, token counts, and related events. It demonstrates exporting telemetry from an LLM-powered application, viewing it with Aspire Dashboard, and considering sensitive-data implications of optional content capture. ### Source excerpt Your AI agent just took 45 seconds to answer a simple question. Was it the model? A slow tool call? A retry loop? Every time an application calls an LLM, a chain of model calls, tool invocations, and token exchanges happens behind the scenes -- and without observability, you are guessing. The OpenTelemetry Semantic Conventions for Generative AI give you that visibility. They standardize how GenAI operations are recorded -- the model being called, input and output token counts, and when opted in, the full content of prompts, completions, tool calls, and tool results. ## How to Connect Your Twilio Agent to External APIs in Node.js DevFeed: [How to Connect Your Twilio Agent to External APIs in Node.js]() Original publisher: [Read original article]() Author: Dhruv Patel, Amanda Lange Published: 2026-09-14T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [AI Agent](), [Node.js](), [voice ai](), [REST API](), [OpenAI](), [Express]() Tags: [agent](), [ai](), [ai-agent](), [developer-insights](), [environment-variables](), [node-js](), [openai](), [rest-apis](), [visual-studio-code](), [voice]() ### AI overview This tutorial explains how to build a voice-enabled AI agent in Node.js with Twilio Conversation Relay and OpenAI. The agent uses LLM-driven tool calling to retrieve live data and take actions through an external REST API. ### Source excerpt Build a voice-enabled AI agent in Node.js with Twilio Conversation Relay and OpenAI to dynamically fetch live data from external REST APIs. ## It passed CI. It passed your evals. The customer still got the wrong answer. DevFeed: [It passed CI. It passed your evals. The customer still got the wrong answer.]() Original publisher: [Read original article]() Author: Sean O'Dell Published: 2026-09-13T14:00:00Z Content type: article Language: en Sources: [The New Stack]() Topics: [AI Agent](), [agent observability](), [observability](), [tracing](), [dynatrace](), [ci](), [coding]() Tags: [ai](), [ai-agent](), [ai-agents](), [ai-engineering](), [ci](), [coding](), [dynatrace](), [observability](), [post-contributed](), [sponsor-dynatrace](), [sponsored](), [sponsored-post-contributed](), [traces]() ### AI overview The article explains how AI-agent failures can pass CI and evaluations while still producing slow or incorrect customer-facing results. It presents distributed traces and agent trajectories--model calls, tool calls, arguments, and results--as evidence for debugging retrieval behavior, release context, and feature-flag state. ### Source excerpt A diff is not evidence. It's a statement of intent. The tests passed. The review's done. The change is live. The post It passed CI. It passed your evals. The customer still got the wrong answer. appeared first on The New Stack. ## Deploy, Discover, Inspect, Observe: A Summer Spent Making a Public Vespa MCP Server DevFeed: [Deploy, Discover, Inspect, Observe: A Summer Spent Making a Public Vespa MCP Server]() Original publisher: [Read original article]() Author: eivinbingen oystein viktor mfstort Published: 2026-09-13T00:00:00Z Content type: article Language: en Sources: [Vespa Blog]() Topics: [Model Context Protocol (MCP)](), [MCP Server](), [Cloud](), [AI Agent](), [Large Language Model](), [Command-line interface]() Tags: [ai-agent](), [ai-assistants](), [claude](), [cli](), [cloud](), [codex](), [internships](), [language-models](), [mcp](), [mcp-server]() ### AI overview This article describes the construction of a standalone, publicly hosted Vespa Cloud MCP server. It explains how MCP connects AI assistants and language models to external systems through resources, tools, and prompts, and discusses evaluating MCP usage against terminal access and Vespa CLI access. ### Source excerpt We built a standalone Vespa Cloud MCP server as a summer interns project ## AI Coding Tip 036 - Grant AI the Least Privilege Possible DevFeed: [AI Coding Tip 036 - Grant AI the Least Privilege Possible]() Original publisher: [Read original article]() Author: Maxi Contieri Published: 2026-09-12T15:51:59Z Content type: tutorial Language: en Sources: [Maximiliano Contieri - Software Design]() Topics: [Artificial Intelligence](), [AI Agent](), [Authorization](), [API](), [systems]() Tags: [ai](), [ai-agent](), [api](), [authorization](), [least-privilege](), [systems]() ### AI overview This developer tip explains why AI agents should receive only the access required for their tasks. It describes how overly broad API permissions, shared credentials, compromised context, and external models connected through MCP can turn routine automation into unauthorized actions. ### Source excerpt Deny by default, or watch your API become someone else's playground. TL;DR: Give every AI agent only the access its task needs, and watch for the intrusion nobody granted. Common Mistake ❌ You wire ## Why MCP security is about permissions overhaul DevFeed: [Why MCP security is about permissions overhaul]() Original publisher: [Read original article]() Author: Mohit Bansal Published: 2026-09-12T15:00:00Z Content type: article Language: en Sources: [The New Stack]() Topics: [Model Context Protocol](), [Authorization](), [GitHub](), [anthropic]() Tags: [ai-agent](), [ai-agents](), [github](), [mcp](), [model-context-protocol-mcp](), [post-contributed](), [security](), [sponsor-webflow](), [sponsored-post-contributed](), [webflow]() ### AI overview The article argues that MCP security problems are fundamentally permission problems, not merely infrastructure flaws. It calls for compartmentalized, task-scoped access to reduce risks such as prompt injection, tool poisoning, and excessive inherited trust. ### Source excerpt Anthropic's Model Context Protocol (MCP) went into production in late 2024. It spread rapidly after that. Since then, thousands of The post Why MCP security is about permissions overhaul appeared first on The New Stack. ## Company's Building AI Agent Sandbox SDKs & APIs DevFeed: [Company's Building AI Agent Sandbox SDKs & APIs]() Original publisher: [Read original article]() Author: iHateReading Published: 2026-09-12T13:15:15Z Content type: opinion Language: en Sources: [iHateReading]() Topics: [AI Agent](), [SDKs](), [Artificial Intelligence](), [Development]() Tags: [ai](), [ai-agent](), [ai-sandboxes](), [ai-sandboxes-vercel-aws-google-daytona](), [apis](), [aws](), [backend](), [blockchain](), [coding](), [company-s-building-ai-agent-sandbox-sdks-apis](), [daytona](), [developer](), [development](), [frontend](), [google](), [ihatereading](), [javascript](), [node-js](), [product](), [programming](), [react](), [sdks](), [typescript](), [vercel](), [web-development]() ### AI overview An opinion article discusses companies building AI agent sandboxes, SDKs, and APIs, in the context of an ongoing competition around AI sandboxes. ### Source excerpt Company's Building AI Agent Sandbox SDKs & APIs I wrote about the fight for AI sandboxes back in March -- the short version was that every AI agent that wri... ## Designing Reliable AI Agent Memory for Stale Facts and Policy Changes DevFeed: [Designing Reliable AI Agent Memory for Stale Facts and Policy Changes]() Original publisher: [Read original article]() Author: Raul Junco Published: 2026-09-12T12:10:58Z Content type: tutorial Language: en Sources: [System Design Classroom]() Topics: [AI Agent](), [context window](), [context]() Tags: [ai-agent](), [context](), [context-window](), [memory]() ### AI overview This article explains that AI agent memory should be treated as evidence rather than truth, especially when policies or other facts change. It recommends attaching version, scope, source, and authoritative-data checks to memory, while keeping the context window limited to the information needed for a task. ### Source excerpt Learn how to design reliable AI agent memory that handles stale facts, policy changes, scoped retrieval, conflict resolution, and safe deletion. ## Better context, smarter testing: How to give your AI coding agent direct access to k6 docs DevFeed: [Better context, smarter testing: How to give your AI coding agent direct access to k6 docs]() Original publisher: [Read original article]() Author: İnanç Gümüş Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [k6](), [Agent Skill](), [AI Agent](), [Command-line interface](), [coding]() Tags: [agent-skill](), [ai](), [ai-agent](), [ai-coding](), [analytics](), [cli](), [grafana](), [k6](), [logs](), [metrics](), [monitoring](), [observability](), [performance-testing](), [testing](), [traces]() ### AI overview The article introduces k6 x docs, an official k6 2.0 command that provides k6 documentation directly in the terminal. It supports API references, guides, best practices, and examples; works offline after the first lookup; matches documentation to the installed k6 version; and includes an agent skill for AI coding assistants. ### Source excerpt As testing workflows become more AI-assisted, fast access to accurate documentation matters more than ever. Whether you're writing a new load test, troubleshooting an issue, or having an AI agent generate a script for you, you need reliable guidance that keeps pace with the way you work. But most documentation still lives in a browser. Every time you or your agent needs to verify an API or look up a best practice, you're forced to leave your terminal or editor and interrupt your workflow. That's why, in k6 2.0, we introduced k6 x docs, an official k6 command that puts the entire k6 documentation library, including API references, guides, best practices, and examples, directly in your terminal. It works offline after first use, matches the docs to your exact k6 version, and includes a built-in agent skill, so AI coding assistants can look up k6 docs faster and most cost-effectively. The problem: documentation lives in the wrong place Most developers know the feeling. You're writing a k6 script, you need to check the signature for http.post or remember how thresholds work, and suddenly you're in a browser tab, searching, scrolling, clicking through navigation, and losing the context you had in your editor. For AI agents, the problem is worse. When they need to reference k6 APIs, best practices, or examples, they either rely on stale training data, hallucinate a function signature, or burn expensive tokens on a web search that may not return the right version of the docs. k6 x docs solves both problems by making documentation a first-class part of the k6 CLI. It gives agents CLI access to k6 docs that automatically detect the k6 version in use and deliver accurate content without leaving the session or performing web searches. How k6 x docs works The use is simple: type k6 x docs, optionally followed by the topic you want to look up. k6 x docs # See all available topics k6 x docs http # Learn about the k6/http module k6 x docs http get # Look up a specific function k6 ## Agents of Chaos: A New $100K Agentic Security Challenge DevFeed: [Agents of Chaos: A New $100K Agentic Security Challenge]() Original publisher: [Read original article]() Author: Vanessa Villa - John Gamble Published: 2026-09-12T11:17:51.295154Z Content type: article Language: en Sources: [Blog]() Topics: [Security](), [prompt injection](), [AI Agent]() Tags: [agentic](), [agentic-security](), [ai](), [ai-agent](), [ai-agents](), [attacks](), [competition](), [prompt-injection](), [securing-ai](), [security](), [techniques]() ### AI overview CrowdStrike is launching Agents of Chaos, an online game and AI red-teaming competition with a $100,000 prize pool. Players interact with real AI agents in an adversarial world, attempting to manipulate them and exploit gaps between their intended behavior and their actual behavior across three increasingly sophisticated acts. ### Source excerpt Agents of Chaos, CrowdStrike's new AI red teaming competition, tests players' defensive skills against adversarial AI techniques. ## CrowdStrike Announces Agentic Identity Provider DevFeed: [CrowdStrike Announces Agentic Identity Provider]() Original publisher: [Read original article]() Author: Ryan Terry Published: 2026-09-12T11:17:51.295154Z Content type: release Language: en Sources: [Blog]() Topics: [AI Agent](), [Next-Gen Identity Security](), [Authorization](), [Security](), [Authentication]() Tags: [2026](), [agentic](), [ai](), [ai-agent](), [api-keys](), [applications](), [authentication](), [authorization](), [cloud-infrastructure](), [code](), [enterprise](), [identity](), [identity-control](), [next-gen-identity-security](), [saas](), [security]() ### AI overview CrowdStrike announces Agentic Identity Provider, a capability in CrowdStrike Falcon Next-Gen Identity Security that gives AI agents trusted identities and continuously controls their access according to real-time security and business context. The announcement also covers expanded privileged access across SaaS applications, endpoints, code repositories, and cloud infrastructure. ### Source excerpt CrowdStrike gives every AI agent a trusted identity and controls their access based on real-time context, and expands modern privileged access. ## CrowdStrike Falcon Guardian Defines the Next Generation of AI Security DevFeed: [CrowdStrike Falcon Guardian Defines the Next Generation of AI Security]() Original publisher: [Read original article]() Author: Michael Devins Published: 2026-09-12T11:17:51.295154Z Content type: release Language: en Sources: [Blog]() Topics: [AI Bots]() Tags: [agentic](), [agents](), [ai](), [ai-agent](), [ai-agents](), [ai-gateway](), [ai-security](), [architecture](), [autonomous](), [platform](), [securing-ai](), [security](), [telemetry](), [threat-detection]() ### AI overview CrowdStrike announces Falcon Guardian, an AI detection and response solution for discovering, monitoring, investigating, and securing AI agents at runtime. It adds an AI gateway and connects agent activity with endpoint telemetry to support threat response. ### Source excerpt A new flagship AI detection and response solution delivers runtime protection for AI agents, introduces a new AI gateway, and extends expert-led defense. [Next page]()