# autonomous Published articles for autonomous. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Presentation: Teaching Engineers, Trusting AI: How Education Enabled Autonomous Code Review DevFeed: [Presentation: Teaching Engineers, Trusting AI: How Education Enabled Autonomous Code Review]() Original publisher: [Read original article]() Author: Sarah Deitke Published: 2026-09-16T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Artificial Intelligence](), [Code review](), [Pull Request](), [Tooling](), [engineering-culture](), [observability]() Tags: [adoption](), [agile](), [ai](), [ai-ml-data-engineering](), [architecture-design](), [artificial-intelligence](), [automated](), [automation](), [autonomous](), [case-study](), [code-review](), [code-reviews](), [culture](), [culture-methods](), [developer](), [developer-experience](), [development](), [duolingo-ai-literacy-code-review](), [infoq](), [metrics](), [ml-data-engineering](), [observability](), [pairing](), [presentation](), [qcon-london-2026](), [qcon-software-development-conference](), [transcripts]() ### AI overview Sarah Deitke presents Duolingo's approach to cultural AI adoption through internal AI literacy workshops, observability dashboards, and safe AI guardrails. The presentation includes a case study on redesigning code review with an automated PR risk-assessment bot and reports faster delivery without increased defect rates. ### Source excerpt Sarah Deitke discusses how Duolingo drives cultural AI adoption beyond tooling access. She explains their internal AI literacy workshops and observability dashboards, then shares a case study on redesigning code review using an automated PR risk-assessment bot. Deitke demonstrates how pairing targeted developer education with safe AI guardrails speeds up delivery without increasing defect rates. By Sarah Deitke ## 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. ## Autonomous LLM post-training with Tunix on TPUs DevFeed: [Autonomous LLM post-training with Tunix on TPUs]() Original publisher: [Read original article]() Author: Wei Wei Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [post-training](), [AI Agent](), [Fine-tuning](), [Large Language Model](), [grpo](), [lora](), [gemma](), [Google](), [Reinforcement learning](), [dataset](), [Command-line interface]() Tags: [autonomous](), [cli](), [fine-tuning](), [gemma](), [git](), [google](), [grpo](), [llm](), [lora](), [post-training](), [reinforcement-learning](), [training]() ### AI overview This article presents autofinetune, an autonomous research loop for LLM post-training. Using AI agents and Google's AI stack, including Tunix, Gemma, Cloud TPUs, Antigravity CLI, and Gemini Flash 3.7, it automates supervised fine-tuning and reinforcement learning with GRPO, exploring hyperparameters such as LoRA configurations, learning rates, batch sizes, and rollout settings. ### Source excerpt Imagine going to sleep after writing a single Markdown specification and waking up to find that an A... ## Cognition helps Devin test its own work with GPT-6 Astra DevFeed: [Cognition helps Devin test its own work with GPT-6 Astra]() Original publisher: [Read original article]() Published: 2026-09-11T16:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Code review](), [Software](), [Code](), [bug]() Tags: [agent](), [autonomous](), [bug](), [gpt](), [review](), [software](), [testing]() ### AI overview Cognition uses GPT-6 Astra with Devin to test software, provide evidence of results, and help engineers review code changes more efficiently. ### Source excerpt GPT-6 Astra improves Devin's ability to test software and show that it works, with the goal of helping engineers review less code and ship more. ## Latest Anthropic horror story chills with tales of kamikaze drone swarms and bioweapons research DevFeed: [Latest Anthropic horror story chills with tales of kamikaze drone swarms and bioweapons research]() Original publisher: [Read original article]() Author: Jessica Lyons Published: 2026-09-10T22:38:35Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [anthropic](), [Claude](), [Cloaked Ursa]() Tags: [ai](), [ai-and-ml](), [anthropic](), [attacks](), [autonomous](), [bioweapons](), [claude](), [phishing](), [scams](), [security]() ### AI overview Anthropic says malicious actors used Claude models in attempted or disrupted cyber, surveillance, fraud, biological, and weapons-related activity. The article highlights alleged AI-assisted automation of cyberattack workflows by a Russian espionage group. ### Source excerpt Everyone from ShinyHunters to Russian freelancers is in on the illicit model fun ## Redefining Digital Resilience in the AI Era DevFeed: [Redefining Digital Resilience in the AI Era]() Original publisher: [Read original article]() Author: Kamal Hathi Published: 2026-09-10T17:00:15Z Content type: news Language: en Sources: [Cisco Blogs]() Topics: [Resilience](), [Artificial Intelligence](), [data](), [Architecture & Design](), [Security](), [SOC](), [monitor]() Tags: [agentic-ai-security](), [agentic-ops](), [agentic-security](), [ai](), [ai-agents](), [autonomous](), [cost](), [digital-resilience](), [executive-platform](), [governance](), [monitor](), [observability](), [resilience](), [scale](), [security](), [soc](), [speed](), [workflows]() ### AI overview Splunk is evolving its data platform for the AI era, focusing on digital resilience, trusted autonomous agents, machine-speed security operations, and economically governed AI workflows. ### Source excerpt Splunk is evolving for the AI era - removing constraints to help customers achieve digital resilience and leverage AI with speed, scale and trust. ## Will AI kill us all within the next decade? DevFeed: [Will AI kill us all within the next decade?]() Original publisher: [Read original article]() Author: Pieter Arntz Published: 2026-09-10T12:18:51Z Content type: opinion Language: en Sources: [Malwarebytes]() Topics: [Responsibility & Safety](), [AI Strategy](), [AI Development](), [Vulnerabilities](), [anthropic]() Tags: [ai](), [ai-safety](), [anthropic](), [autonomous](), [fraud](), [models](), [news](), [superintelligence](), [vulnerabilities]() ### AI overview The article discusses warnings about possible future AI risks while noting that current models are described as low risk. It argues for safeguards such as independent testing, limits on high-risk autonomous uses, transparency, and accountability, alongside action against AI-enabled cybercrime. ### Source excerpt AI researchers are warning that the technology could kill us all within the next decade, although they say the risk from current models is low. ## Beyond Autonomous Teams in Software Product Development DevFeed: [Beyond Autonomous Teams in Software Product Development]() Original publisher: [Read original article]() Author: Ben Linders Published: 2026-09-10T10:29:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Self-organizing Team](), [Software Engineering]() Tags: [agile-conferences](), [autonomous](), [autonomous-software-teams](), [business-value](), [complex-systems](), [complexity](), [conference](), [culture-methods](), [delivering-value](), [management](), [networking](), [news](), [product-development](), [scrum](), [self-organizing-team](), [teamwork]() ### AI overview A conference talk argues for moving from product-focused autonomous teams to value centers, with organizational design shaped by value and its dependencies. It frames agency and coherence as a trade-off and uses the Viable Systems Model to suggest five questions for teams. ### Source excerpt Autonomous teams are an article of faith in modern software development. The shape of our value determines what we can do; it determines our trade-offs between agency and coherence. We all have a purpose, and we all have agency to do something, hence the suggestion is moving from product focus to value center thinking. By Ben Linders ## How much control should AI get? A CISO roundtable takes on SOC autonomy DevFeed: [How much control should AI get? A CISO roundtable takes on SOC autonomy]() Original publisher: [Read original article]() Author: Carly Page Published: 2026-09-09T15:38:58Z Content type: article Language: en Sources: [The New Stack]() Topics: [AI Bots]() Tags: [agents](), [ai](), [ai-agents](), [ai-operations](), [autonomous](), [security](), [soc](), [sponsored-webinar](), [webinar]() ### AI overview The article examines how security operations centers may use AI agents to investigate alerts and the limits organizations should place on autonomous response actions. It emphasizes human oversight, reversibility, and the changing role of SOC analysts. ### Source excerpt Security operations centers have struggled with alerts for years, and AI agents offer a new way to tackle it: Enable The post How much control should AI get? A CISO roundtable takes on SOC autonomy appeared first on The New Stack. ## 6 Benefits of Sandbox Environments (and How Docker Sandboxes Delivers Them) DevFeed: [6 Benefits of Sandbox Environments (and How Docker Sandboxes Delivers Them)]() Original publisher: [Read original article]() Author: Kevin Wittek Published: 2026-09-08T23:05:55Z Content type: article Language: en Sources: [Docker]() Topics: [Docker](), [Authorization]() Tags: [agentic-ai](), [ai-agents](), [ai-ml](), [autonomous](), [concepts](), [docker-ai-governance](), [docker-sandboxes](), [linux](), [policy](), [products](), [sandbox](), [secrets]() ### AI overview The article explains how Docker Sandboxes isolate untrusted code and autonomous AI agents from host machines and external systems. It highlights runtime policy controls, credential handling, disposability, and microVM-based isolation. ### Source excerpt Learn about the key benefits of sandbox environments with Docker including isolation, definable controls, secrets credential handling, and more. ## Scaling your money safely with AI DevFeed: [Scaling your money safely with AI]() Original publisher: [Read original article]() Published: 2026-09-08T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [AI-assisted coding](), [sdlc](), [Code]() Tags: [agentic-ai](), [ai](), [ai-coding](), [autonomous](), [loops](), [podcast](), [sdlc](), [se-stackoverflow](), [se-tech](), [security]() ### AI overview A podcast conversation about validating AI-generated code for security, building autonomous SDLC harnesses with feedback loops, and creating a headless checkout experience. ### Source excerpt Ryan chats with Srini Venkatesan, CTO at PayPal, about validating AI-generated deterministic code for security, developing autonomous SDLC harnesses with iterative feedback loops, and creating a seamless headless checkout experience. ## What Is the x402 Protocol? HTTP 402 Payments for AI Agents DevFeed: [What Is the x402 Protocol? HTTP 402 Payments for AI Agents]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-09-08T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [AI Agent](), [API](), [client](), [Model Context Protocol]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [ai-agents](), [api](), [autonomous](), [coinbase](), [guide](), [http](), [mcp](), [model-context-protocol](), [payments](), [tools]() ### AI overview A technical guide to x402, an open payment standard that uses HTTP 402 to let clients pay for individual API requests without accounts, API keys, sessions, or subscriptions. It explains the payment handshake, the roles of clients, resource servers, and facilitators, plus x402's use over HTTP, MCP, and A2A transports. ### Source excerpt A technical guide to the x402 protocol: how the HTTP 402 payment handshake works, the PAYMENT-REQUIRED and PAYMENT-SIGNATURE headers, facilitators, payment schemes, and where x402 fits next to card rails. ## Agentic security: Detection and response at machine speed DevFeed: [Agentic security: Detection and response at machine speed]() Original publisher: [Read original article]() Author: Gee Rittenhouse Published: 2026-09-02T18:36:37Z Content type: article Language: en Sources: [AWS Security Blog]() Topics: [Security & compliance, Cloud security](), [workload protection](), [AI Strategy]() Tags: [agentic](), [ai-agents](), [announcements](), [artificial-intelligence](), [autonomous](), [cloud](), [cloud-security](), [foundational-100](), [identity](), [security](), [security-blog](), [security-identity-compliance](), [zero-trust]() ### AI overview AWS discusses why autonomous AI agents require continuous security detection and response at machine speed. It outlines an enterprise framework that extends established practices such as identity governance, least privilege, defense in depth, and backup and recovery to agentic workloads. ### Source excerpt After talking with enterprise security leaders over the past year, one thing has become clear: the rise of autonomous AI agents is the most significant shift in security posture since the move to cloud. Organizations across every industry are adopting AI agents that authenticate on behalf of users, execute multistep workflows, and make decisions across [...] ## How River takes security work from a fix to merge DevFeed: [How River takes security work from a fix to merge]() Original publisher: [Read original article]() Author: Erin Son Published: 2026-09-02T16:30:45Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Vulnerabilities](), [AI Bots]() Tags: [agentic](), [ai-agent](), [autonomous](), [ci](), [security](), [shopify](), [slack](), [vulnerability](), [workflow]() ### AI overview Shopify describes River, an AI agent that manages vulnerability remediation from patch review through merge and verification against the repository head and dependency graph. The article reports a 70% reduction in open dependency issues during the workflow's first 11 days. ### Source excerpt River, Shopify's AI agent in Slack, enables autonomous vulnerability remediation, not just detection. ## An AI-Assisted Cyber Attack: Inside a Unit 42 Investigation DevFeed: [An AI-Assisted Cyber Attack: Inside a Unit 42 Investigation]() Original publisher: [Read original article]() Author: Renzon Cruz, Nicolas Bareil, Eric Semaan and Omar Jbari Published: 2026-09-02T10:00:46Z Content type: article Language: en Sources: [Unit 42]() Topics: [Security Attacks](), [Large Language Model](), [CI/CD]() Tags: [agentic](), [agentic-ai](), [ai](), [ai-agents](), [attacks](), [autonomous](), [breach](), [ci-cd](), [enterprise](), [frontier-ai](), [general](), [insights](), [llm](), [secrets](), [security](), [threat-research]() ### AI overview An investigation of a ransom attack in which a human attacker used AI agents and frontier models to automate intrusion, reconnaissance, credential theft, and CI/CD pipeline abuse against an enterprise network. ### Source excerpt Using autonomous AI agents, an attacker breached an enterprise network in a matter of hours. Understand how to address and defend against agentic attacks. The post An AI-Assisted Cyber Attack: Inside a Unit 42 Investigation appeared first on Unit 42. ## Fragments: September 1 DevFeed: [Fragments: September 1]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-09-01T19:50:00Z Content type: article Language: en Sources: [Martin Fowler]() Topics: [AI Bots](), [AI Chat]() Tags: [agents](), [ai](), [architecture](), [autonomous](), [autonomous-agents](), [benchmark](), [ci](), [claude](), [evaluation](), [gpu](), [kernel](), [llm](), [mcp](), [memory](), [nvidia](), [optimization](), [reasoning]() ### AI overview A fragment-style developer roundup covers concerns about detecting AI-generated prose, a long-horizon autonomous-agent architecture used for GPU kernel optimization and reasoning benchmarks, a brief MCP comparison, and the effect of AI agents on CI workflows. ### Source excerpt Like many readers, I'm wary of AI generated prose. Simon Wilison has written an LLM cliché highlighter - paste in some text, or a URL, and it will flag various patterns common to LLMs. It references a wikipedia page of signs of AI writing. That page points out that: Humans are notoriously bad at distinguishing human and LLM-generated text. While research on humans' abilities to detect AI-generated text is still limited, a 2025 study has shown that human ability to distinguish LLM text from human is no better than random chance. Another 2025 study on German theses has shown that humans managed a "recognition rate of 57% for AI texts and 64% for human-generated texts".[ Not just do I find myself repelled by prose with an LLM-voice, I also wonder how accurate my reaction is. I'm old enough to see all sorts of new tic-phrases appear, and in the past would just chalk it up to youngsters or airport business books. (Not to mention Americanisms, which I'll get used to momentarily.) ❄ ❄ ❄ ❄ ❄ NVIDIA's technical blog reports on an Architecture for Long-Horizon Autonomous Agents. Their research group used a combination of Claude Opus 5 and a harness called AVO, and used it first to do GPU kernel optimization and then a broader reasoning benchmark (ARC-AGI-3). Both of these were long-term tasks, for the kernel optimization the agent ran for seven days. AVO is designed to preserve progress beyond a single model context. Two mechanisms are particularly important: persistent memory and supervision. Persistent memory carries forward prior implementations, evaluation results, compiler and profiler outputs, and accumulated reasoning, allowing the agent to resume from the current state rather than repeatedly reconstructing the search. The supervisor monitors the broader trajectory for stagnation or repeated unproductive cycles and can redirect the main agent toward alternative strategies when needed. During the seven-day attention-kernel run, the main agent remained responsible for de ## What Is Bmad? Understanding the Bmad Loop and how to scale It DevFeed: [What Is Bmad? Understanding the Bmad Loop and how to scale It]() Original publisher: [Read original article]() Author: Yonatan Boguslavski Published: 2026-09-01T08:40:17Z Content type: tutorial Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Agile](), [AI Development](), [AI-assisted coding](), [Open Source](), [Software Engineering](), [agent observability]() Tags: [agentic](), [ai](), [ai-agents](), [ai-coding](), [autonomous](), [coding](), [coding-agents](), [development](), [open-source](), [review](), [testing]() ### AI overview This article explains the BMAD method, an open source framework for AI-driven software development, and its BMad Loop for unattended story-by-story implementation, review, testing, and commits. ### Source excerpt Learn how the BMAD method runs agentic software development, from AI-driven planning to autonomous coding, review, and commits. ## Scale AV Perception Across Vehicle Platforms with NVIDIA Omniverse NuRec DevFeed: [Scale AV Perception Across Vehicle Platforms with NVIDIA Omniverse NuRec]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-31T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Nvidia](), [synthetic-data](), [Simulation and Design](), [data](), [configuration]() Tags: [3d](), [autonomous](), [autonomous-vehicles](), [data](), [developer-tools-techniques](), [developers](), [development](), [model](), [models](), [nvidia](), [omniverse](), [performance](), [physical-ai](), [post](), [robotics](), [scale](), [simulation-modeling-design](), [synthetic-data](), [training](), [tutorial]() ### AI overview This tutorial explains how to adapt an autonomous-vehicle perception stack across carline and sensor-rig variants using existing real-world drives. It presents a four-step workflow with NVIDIA Omniverse NuRec: pair a reconstructed drive with a target rig, render target camera views, refine the frames with NVIDIA Harmonizer, and train a perception model on the output. ### Source excerpt A perception stack is shaped by the vehicle that carries it. Move the same software to a new carline--for example, from an SUV to a sedan or another vehicle... ## ClickGap: Autonomous QA for ClickHouse DevFeed: [ClickGap: Autonomous QA for ClickHouse]() Original publisher: [Read original article]() Author: Lareb Zafar Published: 2026-08-25T12:19:59Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [AI Bots](), [C++]() Tags: [agent](), [ai](), [ai-agent](), [autonomous](), [bug](), [c-plus-plus](), [ci](), [clickhouse](), [github](), [pull-request]() ### AI overview ClickGap is an autonomous QA agent for merged ClickHouse pull requests. It tests real builds, bisects regressions, and files tracker issues and fix pull requests while emphasizing mechanisms to prevent costly false positives. ### Source excerpt ClickGap reviews merged ClickHouse changes, executes reproducers, rejects false positives, and attributes regressions to specific commits. ## Why Your Website Should Never Stop Changing DevFeed: [Why Your Website Should Never Stop Changing]() Original publisher: [Read original article]() Author: hello@smashingmagazine.com (Pierre Burgy) Published: 2026-08-25T08:00:00Z Content type: opinion Language: en Sources: [Articles on Smashing Magazine -- For Web Designers And Developers]() Topics: [AI Bots](), [User interface design]() Tags: [accessibility](), [agents](), [ai](), [autonomous](), [code](), [design](), [platform](), [tools]() ### AI overview The article argues that websites should keep improving after launch through agent-assisted maintenance, while questioning full autonomy as the right goal. ### Source excerpt Every website peaks on launch day and slowly drifts from there, not because it breaks, but because nobody has time to keep it current. Autonomous websites, continuously optimized by agents after launch, aim to change that. Pierre Burgy shares what they learned building for full website autonomy and the deeper design problem they uncovered along the way. ## Golden Paths for AI agents: What changes when platform users aren't human? DevFeed: [Golden Paths for AI agents: What changes when platform users aren't human?]() Original publisher: [Read original article]() Author: Candace Shamieh; Shlomo Benyaminov; James Eastham Published: 2026-08-25T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Artificial Intelligence](), [Software Engineering](), [API](), [Low Latency](), [Provisioning](), [real-time]() Tags: [agent-observability](), [agentic](), [agents](), [ai](), [ai-agents](), [ai-security](), [api](), [autonomous](), [developers](), [engineering](), [how-to](), [latency](), [low-latency](), [platform](), [platform-engineering](), [real-time](), [sandbox](), [software-engineering](), [workflow](), [workflows]() ### AI overview Golden Paths for AI agents must evolve beyond human-oriented development workflows. The article explains how platform teams can design agent-facing paths around workload requirements, machine-consumable and enforceable platform capabilities, execution patterns, and controlled workflow dispatch. ### Source excerpt Golden Paths for AI agents require intentional execution patterns, machine-consumable contracts, and dispatch controls. Here's how to build them. ## What makes a good AI coworker? With OpenAI's Codex product lead DevFeed: [What makes a good AI coworker? With OpenAI's Codex product lead]() Original publisher: [Read original article]() Author: info@1password.com (Chris Fowler) Published: 2026-08-25T00:00:00Z Content type: article Language: en Sources: [Blog on 1Password Blog]() Topics: [Artificial Intelligence](), [codex](), [OpenAI](), [long-context](), [Filesystems](), [coding](), [cursor]() Tags: [agent](), [agentic](), [agents](), [ai](), [ai-coding](), [autonomous](), [codex](), [context-window](), [cursor](), [dev](), [developers](), [engineering](), [filesystems](), [memory](), [openai](), [podcast](), [podcasts]() ### AI overview An episode featuring OpenAI Codex product lead Rohan Varma explores what makes AI agents effective coworkers. It focuses on shared context, durable memory and state outside the context window, filesystem-based task continuity, and steerability so people can understand and guide agent output. ### Source excerpt Zero-Shot Learning is a podcast about how AI is built, secured, and deployed. Hosted by Nancy Wang, 1Password CTO, and Dev Tagare, Senior Director of Engineering at Google Gemini, it offers a builder's view of the architecture and complex decisions involved in shipping AI. In this episode, Rohan Varma, Product Lead for Codex at OpenAI, described what happens when teams move from using agents for one-off tasks to enabling autonomous coworkers. Having worked on AI coding products at Cursor and OpenAI, Ro understands what people need to work effectively with agents and what agents need to work effectively with people. What makes a good (agentic) coworker? With any coworker, collaboration works best when everyone is working from the same context, toward a shared goal. Human coworkers are accustomed to working toward shared goals. With proper context and resources, they can divide work without losing sight of how their contribution affects the team. The difference between teams of people and agents is that people don't need to be told how to remember things. Everything they do carries historical context. When a team works together, their shared knowledge expands exponentially. Agents work within context windows, a temporary working memory that fills as a task continues. When the window is full, the system has to summarize the work without losing decisions and constraints that could cause the agent to miss crucial directives. State is a fundamental building block for making an agent feel more like a coworker. Without memory, every time you prompt an agent, it's kind of like its first day on planet Earth." -Rohan Varma, Codex Product Lead, OpenAI To be a useful long-term collaborator, an agent's memory has to exist outside its context window. The system has to preserve the work durably to understand which files were changed, which decisions were made, which results were gathered, and which tasks are yet to be completed. That shared state lets one agent resume a task, anoth ## NVIDIA AVO Reaches 100% on ARC-AGI-3, Demonstrating a Frontier-Level General-Purpose Architecture for Long-Horizon Autonomous Agents DevFeed: [NVIDIA AVO Reaches 100% on ARC-AGI-3, Demonstrating a Frontier-Level General-Purpose Architecture for Long-Horizon Autonomous Agents]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-21T13:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Bots]() Tags: [agentic](), [agentic-ai-generative-ai](), [agents](), [ai-agent](), [architecture](), [autonomous](), [autonomous-agents](), [benchmark](), [coding](), [gpu](), [kernel](), [language-models](), [nvidia](), [nvidia-research](), [optimization](), [performance](), [software-engineering](), [top-stories](), [trustworthy-ai-cybersecurity]() ### AI overview NVIDIA introduces AVO, a general-purpose coding-agent architecture intended for sustained autonomous work on long, multistep tasks. The article describes its use in GPU-kernel optimization and its adaptation to the ARC-AGI-3 benchmark through different task-specific tools and evaluation. ### Source excerpt A frontier language model is only one component of an AI agent. The surrounding agent system--often called a harness--determines how the model receives... ## How AgentFlo built AI sales agents with Amazon Bedrock AgentCore - Part 2 DevFeed: [How AgentFlo built AI sales agents with Amazon Bedrock AgentCore - Part 2]() Original publisher: [Read original article]() Author: Muhammad Musab Iqbal Published: 2026-08-21T10:01:18Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [AI Bots](), [ai observability]() Tags: [advanced-300](), [agents](), [ai](), [ai-agents](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [architecture](), [autonomous](), [autonomous-agents](), [aws](), [aws-fargate](), [customer-solutions](), [observability](), [policy](), [sales](), [security](), [serverless]() ### AI overview AgentFlo's second architecture post explains how it uses Amazon Bedrock AgentCore and AWS serverless components to operate AI sales agents with layered safeguards. It emphasizes controls before requests, during tool execution, and after responses, while reporting an early 12% net-revenue uplift. ### Source excerpt Part 2: how AgentFlo built trusted, reliable AI sales agents on Amazon Bedrock AgentCore and AWS serverless architecture. Learn the three-layer guardrails, grounded data foundation, and end-to-end observability behind a +12% net revenue uplift, plus what's next for real-time voice and server-side tool execution. [Next page]()