# Agent Harness An agent harness is software runtime scaffolding that connects a language model to context and tools, coordinates tool calls and approvals, and maintains session state. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## Build Your Own AI Agent Harness in C#, the MafClaw Live Series DevFeed: [Build Your Own AI Agent Harness in C#, the MafClaw Live Series]() Original publisher: [Read original article]() Author: Bruno Capuano Published: 2026-09-16T21:00:00Z Content type: tutorial Language: en Sources: [.NET Blog]() Topics: [Agent Harness](), [Microsoft Agent Framework](), [AI Agent](), [C#](), [.NET]() Tags: [agent](), [agent-framework](), [agent-harness](), [ai](), [building](), [c-sharp](), [cloud](), [csharp](), [microsoft-agent-framework](), [microsoft-reactor](), [net](), [observability]() ### AI overview This article introduces a four-part live series that builds a complete C# AI agent using the Microsoft Agent Framework harness. It describes adding tools, planning, file access, approvals, memory, skills, shell commands, code execution, background agents, observability, governance, evaluations, and hosted deployment. ### Source excerpt I am building a complete C# agent live, from a single call around an IChatClient to a production-ready, observable, governed agent, using the Microsoft Agent Framework harness in a 4-part Microsoft Reactor series. The post Build Your Own AI Agent Harness in C#, the MafClaw Live Series appeared first on .NET Blog. ## 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 ## AI safety does not stop at the model DevFeed: [AI safety does not stop at the model]() Original publisher: [Read original article]() Author: Samar Abbas Published: 2026-09-15T00:00:00Z Content type: opinion Language: en Sources: [Temporal Blog]() Topics: [ai safety](), [Agent Harness](), [reliability](), [retry](), [Security]() Tags: [agent](), [agent-harness](), [ai](), [reliability](), [retry](), [safety](), [security](), [temporal-voices]() ### AI overview The article argues that AI safety extends beyond model behavior to the application layer, where companies must control agent authority and enforce approvals, policies, and credential limits. It emphasizes that these controls must remain reliable through crashes, timeouts, and retries, and presents Temporal Agent Harness as an execution-layer control point around an agent SDK. ### Source excerpt Auditing what an agent did is only half the job. Companies also have to control what agents may do, and make those limits hold when systems fail. ## What It Takes to Build a Production Agent Harness DevFeed: [What It Takes to Build a Production Agent Harness]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-14T19:50:37Z Content type: article Language: en Sources: [Daily Dose of Data Science]() Topics: [Agent Harness](), [LangChain](), [Artificial Intelligence](), [Persistence](), [Graphs]() Tags: [agent](), [ai](), [ai-engineer](), [engineering](), [evaluation](), [langchain](), [langgraph](), [memory](), [persistence](), [tools](), [tracing]() ### AI overview A hands-on series chapter explains how to build a production agent harness with LangChain and LangGraph. It covers model, message, prompt, and tool interactions; tool-call execution; state transitions; persistence; failure handling; tracing; evaluation; human approval; and resumable execution. ### Source excerpt A hands-on nanodegree for production agent engineering. ## Claude Managed Agents: How They Work and Where They Fit DevFeed: [Claude Managed Agents: How They Work and Where They Fit]() Original publisher: [Read original article]() Author: Matar Peles Published: 2026-09-14T11:29:41Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Agent Harness](), [Claude](), [Artificial Intelligence](), [sdlc](), [context window](), [Network](), [Processes]() Tags: [agent](), [agents](), [ai](), [claude](), [context-window](), [infrastructure](), [network](), [platform](), [sandbox](), [sdlc](), [software](), [tools]() ### AI overview This article explains Claude Managed Agents, a hosted runtime and managed agent harness operated by Anthropic. It describes how the harness coordinates tools and execution, the sandbox provides isolated command and file access, and the session preserves durable task history outside the model's context window. It also discusses the additional platform layer needed to connect multiple agents across a business process and the SDLC. ### Source excerpt What Claude Managed Agents are, how the runtime works, and how platform teams connect several agents across the SDLC. ## Enable on-demand expertise with Agent Skills in Genkit Go DevFeed: [Enable on-demand expertise with Agent Skills in Genkit Go]() Original publisher: [Read original article]() Author: Daniela Petruzalek Published: 2026-09-12T11:04:33.891311Z Content type: tutorial Language: en Sources: [Google Developers Blog]() Topics: [Agent Skills](), [Agent Harness](), [context window](), [Go](), [Script]() Tags: [agent](), [agent-skills](), [architecture](), [context-window](), [developers](), [go](), [skills](), [workflows]() ### AI overview This article explains how Agent Skills in Genkit Go provide on-demand specialized expertise through progressive disclosure. Skills package instructions, references, and scripts in modular SKILL.md bundles, exposing only metadata initially and loading full content when a task requires it. ### Source excerpt To prevent context window bloat and reduce token consumption, Genkit Go introduces Agent Skills based on a progressive disclosure architecture. Developers can package specialized instructions, scripts, and references into modular SKILL.md bundles where only the frontmatter metadata is initially exposed to the agent's system prompt. When a task matches the skill's description, Genkit's middleware dynamically loads the full instruction body and associated assets, ensuring the model accesses precise workflows exactly when needed. ## Agent Harness vs Platform Harness: Why Teams Need Both DevFeed: [Agent Harness vs Platform Harness: Why Teams Need Both]() Original publisher: [Read original article]() Author: Zohar Einy Published: 2026-09-10T04:47:42Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Agent Harness](), [AI Engineering](), [AI Agent](), [Orchestration](), [Software]() Tags: [agentic](), [ai-agent](), [ai-engineering](), [article](), [governance](), [memory](), [orchestration](), [platform](), [tools]() ### AI overview This article distinguishes between an agent harness, which wraps a model with prompts, tools, orchestration, memory, and guardrails, and a platform harness, which adapts an agent to an organization's systems, standards, skills, tools, and governance requirements. It explains why teams need both layers and why vendor-agent adopters should build the platform harness first. ### Source excerpt Agent harness vs platform harness: what each layer covers, who owns it, what breaks when you have only one, and why you need both. ## Your Agent Harness Needs Runtime Security DevFeed: [Your Agent Harness Needs Runtime Security]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-09T20:59:58Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [agent observability](), [Agent Harness](), [Security](), [Open Source](), [telemetry]() Tags: [agent](), [agent-harness](), [ai-agents](), [anthropic](), [claude](), [cybersecurity](), [github](), [guide](), [local](), [monitoring](), [open-source](), [security](), [telemetry]() ### AI overview This guide presents Agent Beacon, an open-source telemetry layer for AI agents. It records tool calls, shell commands, file changes, and approval decisions as structured runtime events across supported agent harnesses, providing a live record of agent behavior for security monitoring and investigation. ### Source excerpt A 100% local, open-source guide to recording what AI agents actually do at runtime. ## Cursor Cloud Agents can now run in Vercel Sandbox DevFeed: [Cursor Cloud Agents can now run in Vercel Sandbox]() Original publisher: [Read original article]() Author: Allen Zhou Published: 2026-09-03T15:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [cursor](), [Vercel](), [Agent Harness](), [Firecracker]() Tags: [agents](), [apis](), [architecture](), [cursor](), [enterprise](), [firecracker](), [guide](), [sandbox](), [scale](), [vercel](), [virtual-machines](), [workflows]() ### AI overview Cursor Cloud Agents can run in Vercel Sandbox, using isolated Firecracker microVMs as their execution environment. The architecture combines Cursor's agent harness and inference loop with Vercel Functions and Workflows for durable request handling, worker provisioning, monitoring, retries, cleanup, and short-lived user-scoped credentials. ### Source excerpt Cursor Cloud Agents can now run in Vercel Sandbox instead of Cursor's hosted machines. Cursor manages the agent harness and inference loop. Its Self-Hosted Machines APIs let you supply the execution environment where agents clone repositories, edit files, and run commands and tests. Self-Hosted Machines requires a Cursor Enterprise plan. Vercel Sandbox provides that execution environment as an isolated Firecracker microVM for each agent request. Vercel Functions and Vercel Workflows form a durable control plane that claims queued agent requests, provisions workers, monitors sessions, and cleans up automatically. With this architecture, you get: A scale-to-zero worker pool without long-lived virtual machines A dedicated, isolated Sandbox for every agent request Durable retries when a worker or session fails Short-lived, user-scoped credentials inside each Sandbox Follow the step-by-step guide to deploy the reference implementation to your own Vercel account, or learn more in the Vercel Sandbox documentation. Read more ## Why Short AI Coding Prompts Can Cost You More Time DevFeed: [Why Short AI Coding Prompts Can Cost You More Time]() Original publisher: [Read original article]() Author: Dean Hume Published: 2026-09-03T09:17:56Z Content type: opinion Language: en Sources: [Dean Hume]() Topics: [Prompt Engineering](), [ai-coding](), [GitHub Copilot](), [Artificial Intelligence](), [Agent Harness](), [GitHub Copilot CLI](), [Testing]() Tags: [agent-harness](), [ai](), [ai-coding](), [coding](), [copilot](), [github-copilot](), [prompt](), [testing]() ### AI overview This opinion article connects GitHub Copilot team's findings about compressing tool output with everyday AI coding prompts. It argues that optimizing a visible step, such as shortening a response or prompt, can increase total time and cost when missing details force follow-up questions, reruns, or recovery work. It recommends giving coding assistants the full problem and relevant source material, and treating instruction files carefully because rewriting guidance can change agent behavior. ### Source excerpt Ever tried to save time by keeping a prompt short - only to spend the next ten minutes answering follow-up questions because the AI didn't have what it needed? Yeah, me too. I recently came across a really interesting post from the GitHub Copilot team about making ## AI-assisted customization may reshape how software is built DevFeed: [AI-assisted customization may reshape how software is built]() Original publisher: [Read original article]() Author: Mike Yerou Published: 2026-09-01T12:42:38Z Content type: opinion Language: en Sources: [Rock and Null]() Topics: [Software](), [Vibe coding](), [AI-assisted coding](), [Agent Harness](), [Cloudflare]() Tags: [agent-harness](), [ai](), [ai-coding-agents](), [cloudflare](), [software](), [software-engineering](), [thoughts](), [vibe-coding]() ### AI overview The article argues that AI coding agents and vibe coding are lowering the cost of custom software, but suggests the future may involve customizing capable software platforms rather than building and maintaining applications entirely from scratch. ### Source excerpt AI is changing the economics of custom software. The future may not be everyone building from scratch, but rather customizing a strong software base to fit their specific needs. ## Implementing the Anthropic AI-Native SDLC Playbook: How to get it right DevFeed: [Implementing the Anthropic AI-Native SDLC Playbook: How to get it right]() Original publisher: [Read original article]() Author: Yonatan Boguslavski Published: 2026-08-28T16:15:46Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [anthropic](), [sdlc](), [Platform Engineering](), [Agent Harness](), [Artificial Intelligence](), [AI-assisted coding]() Tags: [agentic](), [ai](), [anthropic](), [monitor](), [platform](), [platform-engineering](), [sdlc](), [tooling]() ### AI overview The article explains how to implement Anthropic's AI-native SDLC playbook at organizational scale. It describes agents contributing across the software lifecycle, producing artifacts from intent and specifications through plans, pull requests, and production, with governance, human judgment, orchestration, and monitoring built into a platform foundation. ### Source excerpt Implement Anthropic's AI-native SDLC playbook. Learn the key requirements and foundation needed to run agentic SDLC at scale. ## Private Preview: DigitalOcean Managed Agents Runtime Services DevFeed: [Private Preview: DigitalOcean Managed Agents Runtime Services]() Original publisher: [Read original article]() Author: Salman Paracha Published: 2026-08-25T19:01:05Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Agent Harness](), [Artificial Intelligence](), [Cloud](), [Claude Code](), [codex](), [Langgraph](), [Command-line interface]() Tags: [agents](), [ai-agents](), [apis](), [claude-code](), [cli](), [cloud](), [codex](), [coding-agents](), [digitalocean](), [gateway](), [harness](), [langgraph](), [preview](), [product-updates](), [services](), [workflows]() ### AI overview DigitalOcean announces Managed Agents Runtime Services (M.A.R.S.) in private preview, combining Harness Runtime and Action Gateway. The managed service provides cloud infrastructure for persistent, scalable agent sessions and governed access to tools, APIs, and SaaS systems. ### Source excerpt AI agents are helping developers, teams, and businesses do more: writing and executing code, conducting research, and running dynamic workflows across systems. But that ability is often bounded by where they run. Close the laptop, and the work stops there. You can't pick it up on another device, hand off to a teammate, or scale it across users. Moving agents to cloud VMs solves part of this problem; developers and companies building agent harness frameworks still have to build a high-fidelity experience that can match a local session, including agent friendly execution environments, session persistence, secure tool access, human-in-the-loop approvals, and observability. Now available in Private Preview, DigitalOcean Managed Agents Runtime Services (M.A.R.S.) provides that infrastructure as a fully managed service. It gives developers, teams, and ISVs a powerful yet lightweight environment for operating coding agents and long-running, multi-tool agentic workflows without building and managing the underlying infrastructure themselves. M.A.R.S. brings together two products: Harness Runtime provides the managed execution environment in which agents run, persist, and scale. Action Gateway gives those same agents governed access to the tools, APIs, and SaaS systems they need to complete real-world work. Rather than requiring you to rebuild your agent around a proprietary framework, M.A.R.S. lets you define an environment template that packages your preferred harness, dependencies, tools, and configuration. Designed to work with Claude Code, Codex CLI, and OpenCode, as well as agents built with LangGraph or CrewAI, giving teams the freedom to choose the agent experience that best fits their needs without being locked into a single harness or framework. Agent sessions that outlive your laptop Harness Runtime helps agent sessions run independently of any local machine. They can start in under a second and resume from a pause in as little as 200 milliseconds while preserving ## Temporal Agent Harness: An early look at durable agent infrastructure DevFeed: [Temporal Agent Harness: An early look at durable agent infrastructure]() Original publisher: [Read original article]() Author: Cornelia Davis Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Agent Harness](), [AI Agent](), [reliability](), [interfaces]() Tags: [agent-harness](), [ai](), [ai-agents](), [announcements](), [execution](), [interfaces](), [production](), [tool]() ### AI overview Temporal presents an early look at Agent Harness, an infrastructure layer for production AI agents. It is designed to add durable execution, tool-call approvals, typed interfaces, enforceable policies, reliable execution, and a durable history of agent activity while allowing teams to keep using existing agent harnesses and tools. ### Source excerpt Temporal's new Agent Harness brings durable execution, tool-call approvals, and typed interfaces to production AI agents. An early look. ## Evaluating AI Agent Skill Performance with NVIDIA SkillEvaluator DevFeed: [Evaluating AI Agent Skill Performance with NVIDIA SkillEvaluator]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-19T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Agent Skills](), [AI Agent](), [Benchmark](), [Nvidia](), [Open Source](), [Agent Harness](), [Security]() Tags: [agent](), [agent-skill](), [agentic-ai-generative-ai](), [ai](), [ai-agent](), [benchmark](), [build-ai-agents](), [codex](), [cursor](), [developer-tools-techniques](), [featured](), [nvidia](), [open-source](), [prompt-injection](), [sandbox](), [security](), [simulation-modeling-design](), [trustworthy-ai]() ### AI overview NVIDIA SkillEvaluator is an open-source evaluation layer for measuring how packaged skills affect AI-agent performance. It compares agent runs with and without a skill, using static validation, embedding-based distinctiveness checks, and live task evaluations in isolated sandboxes. The article reports benchmark results for more than 300 verified skills across over 30 NVIDIA products and describes integrations with Claude Code, Codex, Cursor, Skills.sh, ClawHub, and Hermes Hub. ### Source excerpt AI agents are only as effective as the context they receive. Even with capable models and well-documented NVIDIA libraries, agents can spend extra steps finding... ## Using the GitHub Copilot SDK for Java DevFeed: [Using the GitHub Copilot SDK for Java]() Original publisher: [Read original article]() Author: Edward Burns Published: 2026-08-10T19:30:00Z Content type: tutorial Language: en Sources: [GitHub]() Topics: [GitHub Copilot](), [Java](), [SDK](), [Jakarta EE](), [Agent Harness](), [Artificial Intelligence](), [Command-line interface](), [Spring AI]() Tags: [agent](), [agent-harness](), [ai](), [ai-ml](), [cli](), [client-library](), [engineering](), [framework](), [github-copilot](), [github-copilot-sdk](), [jakarta-ee](), [java](), [sdk]() ### AI overview This tutorial introduces the GitHub Copilot SDK for Java, a framework-agnostic client library for creating Copilot agent sessions, registering tools, sending prompts, and receiving structured responses. It demonstrates the SDK in a Jakarta EE 11 real-estate lead-management application and describes prerequisites including JDK, Maven, a GitHub Copilot subscription, and the Copilot CLI. ### Source excerpt Enterprise Java developers have a new superpower--drive GitHub Copilot from idiomatic Java code with annotations, virtual threads, and more. The post Using the GitHub Copilot SDK for Java appeared first on The GitHub Blog. ## What 21 five-minute demos looked like at WorkOS Demo Night DevFeed: [What 21 five-minute demos looked like at WorkOS Demo Night]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-07T00:00:00Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Demo](), [Agent Harness](), [Artificial Intelligence]() Tags: [agent](), [agent-harness](), [demo](), [mac](), [mac-os](), [recap]() ### AI overview A recap of WorkOS Demo Night in San Francisco, featuring 21 live five-minute demos. The article highlights Carbon Code, an agent harness running on emulated Mac OS 9 that edited its own source and added an effort selector during the event. ### Source excerpt A recap of the August 5 Demo Night at our San Francisco office: 21 live five-minute demos, no slides, no pitches, and an agent harness running on Mac OS 9. ## Build your own coding agent DevFeed: [Build your own coding agent]() Original publisher: [Read original article]() Author: Owain Lewis Published: 2026-08-05T20:06:19Z Content type: tutorial Language: en Sources: [The AI Engineer]() Topics: [Agent Harness](), [coding](), [AI-assisted coding](), [AI Development](), [Code](), [Go Language](), [Shell](), [Language models](), [Open Source](), [codex](), [Claude Code]() Tags: [agent](), [agent-harness](), [agents](), [ai](), [ai-development](), [claude](), [claude-code](), [code](), [codex](), [coding](), [commands](), [development](), [go](), [open-source]() ### AI overview This tutorial explains the basic architecture of coding agents and implements a minimal Go-based agent called Micro Neo. The agent connects to an existing AI model and supports reading files, editing files, and running shell commands. It also describes the harness loop that sends tasks and tool results between the user, model, and local machine. ### Source excerpt Why I built my own coding agent and lessons learned ## Testing an Agent Harness Without Ever Calling the Model DevFeed: [Testing an Agent Harness Without Ever Calling the Model]() Original publisher: [Read original article]() Author: Philipp Lehmann (philipp.lehmann@gruppe.ai) Published: 2026-07-31T07:00:00Z Content type: tutorial Language: en Sources: [Philipp Lehmann]() Topics: [Testing](), [Agent Harness](), [Pytest](), [Unit testing](), [coding]() Tags: [agent](), [agent-harness](), [agents](), [api](), [network](), [permission](), [pytest](), [python](), [recording](), [testing](), [unit-testing]() ### AI overview This tutorial explains how to unit test a coding agent's tool-call permission layer without calling a model. It recommends separating the decision function from model responses, using recorded real-request fixtures, and keeping the policy module independent of any LLM SDK. ### Source excerpt Unit testing a coding agent's tool-call permission layer with pytest and recorded fixtures: allow, deny or ask decisions with no model or API key. ## Stronger with every update: How we're making Chrome and the web safer in the AI Era DevFeed: [Stronger with every update: How we're making Chrome and the web safer in the AI Era]() Original publisher: [Read original article]() Author: Chrome Security Team Published: 2026-07-30T17:00:00Z Content type: article Language: en Sources: [Security]() Topics: [Chrome](), [Security](), [Vulnerabilities](), [Artificial Intelligence](), [Large Language Model](), [Fuzzing/Fuzz testing](), [Agent Harness](), [V8](), [JavaScript](), [interoperability](), [Resilience](), [Software]() Tags: [agent](), [ai](), [ai-models](), [bug](), [bugs](), [chrome](), [chrome-security](), [exploits](), [fuzzing](), [interoperability](), [javascript](), [none](), [resilience](), [security](), [vulnerabilities]() ### AI overview This article describes how Chrome's security teams use large language models, fuzzing, specialized research tools, and AI vulnerability-discovery agents to find and remediate security bugs more quickly. It highlights Big Sleep, an agent harness using Gemini, model interoperability, and a Chrome knowledge base built from CVEs and Git history. ### Source excerpt Video of Chrome logo turning into a shield ## LoopSmith: Closed-Loop AI Engineering for Self-Correcting Pipelines on Antigravity 2.0 DevFeed: [LoopSmith: Closed-Loop AI Engineering for Self-Correcting Pipelines on Antigravity 2.0]() Original publisher: [Read original article]() Author: Esther Irawati Setiawan Published: 2026-07-29T09:40:30Z Content type: tutorial Language: en Sources: [Google Developer Experts - Medium]() Topics: [AI Engineering](), [google-antigravity](), [Agent Harness](), [SDKs](), [Command-line interface](), [Code]() Tags: [agent-harness](), [agentic-ai](), [ai](), [ai-engineering](), [antigravity](), [cli](), [google-antigravity](), [pipelines](), [sdk](), [verification]() ### AI overview A guide to using Antigravity 2.0 to build closed-loop AI engineering workflows. It presents a state-machine pattern in which an agent writes, runs, and fixes code against a defined objective until the output is verified, while noting that the SDK is pre-v1.0 and its documented interfaces may change. ### Source excerpt LoopSmith: Closed-Loop AI Engineering -- Autonomous /goal Execution for Self-Correcting Pipelines on Antigravity 2.0Stop prompting the agent turn by turn. Hand it an objective, a bar to clear, and let the state machine write, run, and fix its own code until the output is verified.This guide targets Antigravity 2.0 -- the four-surface release (desktop app, agy CLI, google-antigravity SDK, and enterprise cloud) that shares one agent harness. The SDK is pre-v1.0; symbol names and CLI flags below reflect the documented API as of mid-2026. Treat the patterns as stable and re-check exact signatures against the current docs before you ship.Table of contents The problem with the on-demand agent The architectural shift: closed-loop engineering as a state machine Step 1 -- Initialize the project and the state machine (agy) Step 2 -- Define the objective and trigger /goal execution mode (SDK) Step 3 -- Implement the self-correcting loop Step 4 -- Verification and state finalization Conclusion 1. The problem with the on-demand agent Most "AI engineering" today is still conversational. You prompt; the model answers. You notice the answer is wrong; you prompt again. You paste a traceback; it apologizes and tries once more. The intelligence is real -- but you are the control loop. You are the thing that runs the code, reads the error, decides whether the output is good enough, and feeds the next instruction back in. Take the human out of that seat and the whole system stops. That's fine for a chat window. It falls apart the moment you want an agent to produce a deliverable -- a cleaned dataset, a reconciled financial report, a migration that actually compiles. Real analytical work is iterative and self-referential: you write a script, it crashes on a currency string, you fix the parse, it runs but the totals don't reconcile, you fix the aggregation, and only then is the output trustworthy. Every one of those arrows is a decision. An on-demand agent makes you supply all of them. There are ## The Bare-Bones Coding Agent Loop DevFeed: [The Bare-Bones Coding Agent Loop]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-07-28T13:54:35Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [Agent Harness](), [AI-assisted coding](), [Python](), [Shell](), [Text-based user interface]() Tags: [agent](), [agent-harness](), [coding](), [coding-agents](), [interfaces](), [python]() ### AI overview A lesson in an open-source course that explains how to build a bare-bones coding agent loop in Python. It covers decomposing goals into plans, executing them with tools, building a terminal user interface, and avoiding message-handling problems in the loop. ### Source excerpt One agent loop, 9 tools, and a terminal you can steer. ## Introducing AURA: Building an Open Agentic harness for production AI DevFeed: [Introducing AURA: Building an Open Agentic harness for production AI]() Original publisher: [Read original article]() Author: Henry Andrews Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Agent Harness](), [Platform Engineering](), [Open Source](), [Orchestration](), [Artificial Intelligence](), [telemetry](), [Rust](), [Model Context Protocol](), [Retrieval Augmented Generation (RAG)]() Tags: [agentic](), [agents](), [ai](), [ai-agents](), [building](), [mcp](), [observability](), [open](), [open-source](), [orchestration](), [platform-engineering](), [platforms](), [rag](), [rust](), [source]() ### AI overview AURA is Mezmo's open-source, Rust-based agent harness for running production AI workflows. It provides declarative composition, multi-agent orchestration, MCP tool integration, RAG pipelines, and observability-oriented context for operational investigations. ### Source excerpt AI agents need production infrastructure. AURA is Mezmo's open-source agentic harness, offering declarative composition, multi-agent orchestration, and deep observability [Next page]()