# Agentic development Published articles for Agentic development. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## The AI Hurricane Is Here DevFeed: [The AI Hurricane Is Here]() Original publisher: [Read original article]() Author: Manoj Nair Published: 2026-09-15T04:00:00Z Content type: opinion Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [AI Development](), [Security](), [Vulnerabilities](), [Architecture & Design](), [audit trail]() Tags: [agentic-development](), [agents](), [ai](), [application-security](), [audit-trail](), [awareness](), [blog](), [code-security](), [developer](), [executive](), [security](), [security-labs](), [snyk-platform](), [snyk-security-intel](), [supply-chain-security](), [tech](), [validation](), [vulnerabilities](), [vulnerability-insights]() ### AI overview The article argues that AI is accelerating software creation and cyberattacks, widening the gap between machine-speed development and slower validation. It calls for securing agentic development, enforcing runtime controls, maintaining inventories and audit trails for production AI applications, and using independent validation. ### Source excerpt AI is accelerating software creation and cyberattacks alike. Leaders must secure agents and code at inception, enforce controls at runtime, and validate defenses independently. ## How agentic development affects team collaboration DevFeed: [How agentic development affects team collaboration]() Original publisher: [Read original article]() Author: Irina Nazarova (inazarova@evilmartians.com) Published: 2026-09-09T00:00:00Z Content type: opinion Language: en Sources: [Evil Martians]() Topics: [Development](), [Artificial Intelligence](), [Code](), [pull-requests]() Tags: [agentic-development](), [ai](), [code](), [collaboration](), [developer-community](), [development](), [dx](), [teamwork]() ### AI overview This opinion article examines how agentic development can increase individual speed while making collaboration harder. It distinguishes learning work, such as testing hypotheses and building experiments, from other engineering work even when both produce code, and presents practical ideas for working together again. ### Source excerpt Agentic speed turned all of us into super-ICs and broke our ability to collaborate. Two types of work that both produce code, and three recipes for working together again: a safe perimeter for experiments, shared rules everybody pays for, and an intent log your agents write themselves. ## Nuxt co-creator Alexandre Chopin joins Encore DevFeed: [Nuxt co-creator Alexandre Chopin joins Encore]() Original publisher: [Read original article]() Author: Marcus Kohlberg Published: 2026-09-07T00:00:00Z Content type: news Language: en Sources: [Encore Updates]() Topics: [Provisioning](), [Cloud](), [Vue.js](), [Command-line interface](), [GitHub](), [Open Source](), [SDKs](), [AI-assisted coding]() Tags: [agentic-development](), [ai-agents](), [cli](), [developer](), [developer-relations](), [github](), [infrastructure](), [open-source](), [provisioning](), [sdk](), [teams](), [vue]() ### AI overview Encore announces that Alexandre Chopin, co-creator of Nuxt, has joined the company as Head of Developer Relations. He will work with developers and engineering teams and help shape Encore's roadmap around automated infrastructure. ### Source excerpt Alexandre joins as Head of Developer Relations to help bring automated infrastructure to more developers and teams. ## Checks for safer review of rapidly generated TypeScript and React frontend code DevFeed: [Checks for safer review of rapidly generated TypeScript and React frontend code]() Original publisher: [Read original article]() Author: Travis Turner (richardturner@evilmartians.com) Published: 2026-09-01T00:00:00Z Content type: tutorial Language: en Sources: [Evil Martians]() Topics: [Front end](), [React](), [TypeScript](), [Artificial Intelligence](), [OpenAPI Specification](), [API](), [mutation-testing](), [Testing](), [Code]() Tags: [agent-experience](), [agentic-development](), [agents](), [ai](), [api](), [code](), [continuous-integration](), [dx](), [frontend](), [llms](), [mutation-testing](), [openapi](), [react](), [testing](), [typescript]() ### AI overview This article presents ten checks for making rapidly generated TypeScript and React frontend code safer to trust and cheaper to review. It covers API contracts, generated types, boundary validation, mutation testing, dead-code detection, and mandatory checks. ### Source excerpt Ten checks that catch what AI-written frontend code hides: contract codegen, boundary linting, mutation testing, and dead-code detectors. ## The Context Tax of Agentic Development DevFeed: [The Context Tax of Agentic Development]() Original publisher: [Read original article]() Author: A Talhan Published: 2026-08-25T11:01:02Z Content type: opinion Language: en Sources: [Expedia]() Topics: [Agentic development](), [Development](), [networking](), [data]() Tags: [agentic](), [agentic-ai](), [agentic-development](), [agents](), [context](), [context-engineering](), [development](), [engineering-management](), [networking](), [software-engineering](), [teamwork]() ### AI overview An Expedia Group engineering team describes how hidden context can cause agentic development to accelerate technically plausible work aimed at the wrong system. Using AI Workbench's multi-environment model handling as an example, the article argues that context must be explicit and structured so humans and agents share the same understanding. ### Source excerpt Expedia Group Technology -- EngineeringMissing context used to slow a team down, with agents in the loop it speeds up the wrong work insteadPhoto by Harley-Davidson on Unsplash When we started using agents more seriously inside the team, the first bottleneck was not code generation -- It was context coordination. That sounds like a documentation problem, but it did not feel like one. It felt like a delivery problem. A human engineer who is missing context usually slows down. They ask someone, search through old notes, or wait for the next sync. An agent does not always slow down. It can keep moving and produce work that is technically plausible, well formatted, and aimed at the wrong reality. That is the agentic velocity trap: unclear context does not just delay work; it can accelerate the wrong work. Rick Fast recently wrote about the broader Expedia Group™ platform shift toward agent-friendly interfaces and operating surfaces. This is the ground-level version from one team: what did we have to change in our own planning loop so humans and agents could work from the same understanding? The problem was hidden context One of the clearest examples came from multi-environment handling in AI Workbench (our web console for machine learning (ML) artifacts and workload management). From the outside, an AI Workbench URL appeared to represent one backend environment. Under the hood, it was backed by another. A team saw their model in the UI, reasonably assumed it existed in the environment implied by the URL, and then hit "model not found" when downstream jobs queried that expected backend. Internally, the concrete case was a machine learning scientist team onboarding a model. It appeared in the .prodA AI Workbench URL, but that deployment was serving model registry prodB data rather than model registry prodA data. The platform services were not simply broken. The hidden semantic mapping was because of networking and access nuances. That distinction matters. A human team lost ## \[July 2026\] AI Community -- Activity Highlights and Achievements DevFeed: [\[July 2026\] AI Community -- Activity Highlights and Achievements]() Original publisher: [Read original article]() Author: Nari Yoon Published: 2026-08-24T02:23:20Z Content type: article Language: en Sources: [Google Developer Experts - Medium]() Topics: [google-antigravity](), [AI Agent](), [Agentic development](), [Artificial Intelligence](), [SDK](), [Orchestration](), [Computer vision](), [Microsoft Agent Framework]() Tags: [2026](), [agentic-development](), [ai](), [ai-agents](), [computer-vision](), [google](), [google-ai](), [google-antigravity](), [orchestration](), [sdk]() ### AI overview A July 2026 roundup highlights Google AI community projects built with the Antigravity SDK and related tools. The featured work covers asynchronous triggers, autonomous and self-correcting agents, approval-gated workflows, computer vision operations, and parallel multi-agent orchestration. ### Source excerpt We love sharing the accomplishments of the Google AI communities over the month. We appreciate all the hard work and dedication of our community members. Without further ado, here are the key highlights by products! Agentic DevelopmentAntigravity Antigravity has no task queue. Meet @trigger, its real async primitive by AI GDE Omotayo Aina (UK) explores the design philosophy behind Antigravity SDK, detailing how it leverages asyncio and triggers instead of a traditional task queue. It demonstrates how to construct asynchronous patterns like bounded task queues and cron-like scheduling using this minimalist primitive. https://medium.com/media/0311867ab42ff4749c6db6e2653e2716/href Inside the /goal Loop: How to Build Autonomous AI Agents (repository) by GDE Alexander Amin (Germany) explores the architecture of a custom autonomous agent built with Antigravity SDK that coordinates a multi-agent squad to retrieve data and edit documents. It demonstrates how to implement human gate policies and maintain secure, production-ready agentic loops. Anatomy of a Self-Correcting Agent -- How /goal Closes the Loop in Antigravity by AI GDE Krupa Galiya (India) is a framework with a live dashboard to analyze an AI agent's self-correction process. It examines how agents respond to intentional failures through a loop of verification, diagnosis, replanning, and retrying. image source VisionOps Crew: A Multi-Agent Architecture for Computer Vision Operations Using Google ADK and the Antigravity SDK (repository) by AI GDE Henry Ruiz (US) introduces a multi-agent assistant designed to address fragmentation in computer vision engineering using ADK and Antigravity SDK. Henry leverages specialized agents and external tool integrations to coordinate model discovery, data inspection, and workflow execution. EscrowGuard: Building Approval-Gated AI Agents with the Google Antigravity SDK (repository) by AI GDE Aye Hninn Khine (Thailand) leverages Antigravity SDK to build a multi-agent architecture wi ## Agent Night demo recap: How Mastra turned its issue backlog into a software factory DevFeed: [Agent Night demo recap: How Mastra turned its issue backlog into a software factory]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-17T19:04:08Z Content type: news Language: en Sources: [WorkOS Blog]() Topics: [Development](), [Open Source](), [coding](), [Software](), [Command-line interface]() Tags: [agent](), [agentic](), [agentic-development](), [agents](), [coding](), [demo](), [development](), [harness](), [mastra](), [memory](), [open-source](), [open-source-software](), [recap](), [software](), [terminal]() ### AI overview The article recaps Abhi Aiyer's Agent Night demonstration of Mastra's open source software factory. It describes observational memory, Mastra Code, and the AgentController, which supports building interactive agent applications with modes, models, storage, workspaces, approvals, subagents, and channels. ### Source excerpt Abhi Aiyer, co-founder and CTO of Mastra, demoed the company's open source software factory at Agent Night: memory, harness, a rules engine around work. ## Engineering Practices for Building and Operating AI Systems DevFeed: [Engineering Practices for Building and Operating AI Systems]() Original publisher: [Read original article]() Author: Nicole Morgan Published: 2026-08-05T00:00:00Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [Agentic development](), [Testing](), [Spec Driven Development](), [context](), [Requirements]() Tags: [agentic-development](), [requirements](), [spec-driven-development](), [testing]() ### AI overview Harness argues that AI-assisted development needs stronger specifications, testing, operational controls, and failure-mode analysis to maintain delivery quality and manage risk. ### Source excerpt Learn agentic development best practices with spec-driven development, AI system testing, and operational readiness for secure AI delivery. | Blog ## +14% activated users for AppSignal: designing a new homepage in code DevFeed: [+14% activated users for AppSignal: designing a new homepage in code]() Original publisher: [Read original article]() Author: Travis Turner (richardturner@evilmartians.com) Published: 2026-08-04T00:00:00Z Content type: article Language: en Sources: [Evil Martians]() Topics: [Application Performance Management (APM)](), [error tracking](), [Tooling](), [Code](), [Figma](), [Artificial Intelligence]() Tags: [agentic-development](), [ai](), [apm](), [case-study](), [code](), [design](), [design-for-devtools](), [developer-marketing](), [developer-products](), [engineering](), [error-tracking](), [figma](), [product-development](), [tooling]() ### AI overview Evil Martians designed and shipped a new AppSignal homepage primarily in code, combining code, Figma, and AI during exploration and prototyping. The selected direction was validated through an A/B test that showed a 14% increase in activated users over the previous site. ### Source excerpt We designed and shipped a fresh AppSignal homepage in code, then validated it in an A/B test with a 14% lift in activated users. ## GLM-5.2: Considerations for enterprise teams starting out with open-weight models DevFeed: [GLM-5.2: Considerations for enterprise teams starting out with open-weight models]() Original publisher: [Read original article]() Author: Robat Williams Published: 2026-07-08T10:00:00Z Content type: article Language: en Sources: [Scott Logic]() Topics: [Agentic development](), [developer setup](), [AI Development](), [experiments](), [Benchmark](), [Open Source](), [Claude]() Tags: [agent](), [agentic](), [agentic-development](), [artificial-intelligence](), [benchmark](), [claude](), [developer-setup](), [experiments](), [open-source]() ### AI overview The article describes how a small team prepared an open-weight agentic development setup for AI productivity experiments. It explains the team's model-selection criteria, distinguishes open-weight models from locally run models, and reports choosing GLM-5.2 after comparing DeepSeek V4, Kimi K-2.6, and other models using benchmarks and practical trials. ### Source excerpt Setting yourself up to try out open-weight models for agentic development isn't difficult, but it isn't as straightforward as downloading a coding agent from one of the handful of well-known AI vendors. In preparation for the latest round of our AI productivity experiments, we've recently been through this process. Read on for the choices we made, the considerations at play, and what made our situation unusual. ## Using Storybook Workbench to audit AI-generated UIs for dead components and accessibility bugs DevFeed: [Using Storybook Workbench to audit AI-generated UIs for dead components and accessibility bugs]() Original publisher: [Read original article]() Author: Travis Turner (richardturner@evilmartians.com) Published: 2026-07-08T00:00:00Z Content type: tutorial Language: en Sources: [Evil Martians]() Topics: [Storybook](), [Agent Skills](), [agentic-coding](), [AI-assisted coding](), [Accessibility](), [Design system]() Tags: [accessibility](), [agent-experience](), [agent-skills](), [agentic-coding](), [agentic-development](), [ai](), [bugs](), [design](), [design-engineering](), [design-for-devtools](), [design-system](), [dx](), [storybook]() ### AI overview The article presents Storybook Workbench, a bundle of Agent Skills that audits AI-generated web applications by rendering components as Storybook stories. It describes finding dead components, coexisting design systems, conditional component states, and accessibility bugs, based on an internal audit of an agent-coded app. ### Source excerpt Storybook Workbench is a bundle of Agent Skills that turn Storybook into an audit layer for AI-generated UIs: find dead components, design-system drift, and hidden accessibility bugs. ## Teaching the Agent Our Craft: Structured Agentic Development on a Real Codebase DevFeed: [Teaching the Agent Our Craft: Structured Agentic Development on a Real Codebase]() Original publisher: [Read original article]() Author: Alex Haldeman Published: 2026-07-06T16:00:00Z Content type: article Language: en Sources: [8th Light](), [8th Light Insights]() Topics: [Agentic development](), [Claude Code](), [Development](), [AI-assisted coding](), [Test-driven development]() Tags: [agentic](), [agentic-development](), [ai-and-emerging-tech](), [claude-code](), [clean-architecture](), [structured](), [test]() ### AI overview 8th Light describes a structured agentic development workflow built around Claude Code. The approach adapts Research-Plan-Implement by separating research, planning, and implementation, adding review cycles and involving product managers and designers at each transition. It also applies test-driven development, clean architecture, and explicit project conventions to address common agent failure modes. ### Source excerpt The Mission We recently partnered with a startup that had developed a clinically proven approach to alleviating neuroplastic chronic pain. Their program worked: a coach-led model that helped patients ease chronic pain at a lower cost than conventional treatment. The problem was reach. The in-person model could not scale to the demand they were seeing, and there were not enough coaches to close the gap. We were brought in to build a digital platform that could deliver the program to every patient who needed it. Our Development Philosophy and Inspiration At 8th Light, we approach agentic development the same way we approach any software engagement: with discipline around test-driven development, clean architecture, and code that is built to embrace change. But agentic development comes with its own failure modes. An agent produces code that covers the happy path and misses critical behaviors. A context window fills with stale reasoning from earlier attempts, and the agent starts working against itself. Without explicit conventions, the output works but looks like nobody on the team wrote it. Tyler Burleigh's Research-Plan-Implement gave us a useful frame for thinking about this. His core observation: the bottleneck is not code generation, it is ensuring the model understands what to build before it starts building. RPI addresses that by separating research, planning, and implementation into distinct phases, each with a review cycle before the next begins. We adapted that structure into our Claude Code workflow, with the additional goal of keeping product managers and designers genuinely in the loop at each transition, not just developers. What follows is a description of the harness we built from a Claude Code-specific perspective. The HarnessStructure Before walking through the pieces, it helps to see how they fit together. Everything that teaches the agent our craft lives in a handful of files at the project root and inside a single .claude/ directory. None of it is ## Agentic Development Loops Shift the Unit of Work and Increase the Need for Verification DevFeed: [Agentic Development Loops Shift the Unit of Work and Increase the Need for Verification]() Original publisher: [Read original article]() Author: Arjun Iyer Published: 2026-06-13T16:00:00Z Content type: opinion Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack]() Topics: [ai-coding](), [Development](), [coding](), [Kubernetes](), [Cloud]() Tags: [agentic-development](), [agents](), [ai](), [ai-coding](), [ai-engineering](), [cloud-native](), [coding](), [development](), [engineering](), [kubernetes](), [loops](), [platform-engineering](), [post-contributed](), [signadot](), [sponsor-signadot](), [sponsored-post-contributed](), [verification]() ### AI overview The article argues that agentic development is moving from prompt-driven and specification-driven workflows toward loops that generate, evaluate, and retry work. For cloud-native teams, this shift increases the importance of verification and the surrounding infrastructure needed to help agent-driven work converge on correct results. ### Source excerpt Something shifted in the AI coding discourse this month. The argument is no longer about whether agents can write production The post Loops are replacing prompts. Verification is about to be your biggest problem. appeared first on The New Stack. ## Flutter's multiplatform value for agentic development DevFeed: [Flutter's multiplatform value for agentic development]() Original publisher: [Read original article]() Author: Michael Thomsen Published: 2026-05-18T18:42:10Z Content type: article Language: en Sources: [Flutter - Medium]() Topics: [Flutter](), [multiplatform](), [Artificial Intelligence](), [Dart](), [Development](), [Large Language Model]() Tags: [agentic](), [agentic-ai](), [agentic-development](), [ai](), [code](), [consistency](), [dart](), [flutter](), [flutter-app-development](), [llms](), [mobile-app-development](), [multiplatform](), [reuse]() ### AI overview The article explains how Flutter's single shared codebase can support multiplatform app development and agent-driven workflows. It argues that writing features once in Dart gives AI assistants unified context, reduces token usage and hallucination risk, and helps maintain consistency across platforms. It also describes reported benefits including code reuse, native compilation, and strongly typed Dart code. ### Source excerpt The fundamental value of multiplatform development with Flutter lies in building apps that support multiple platforms with just a single, shared source codebase, allowing developer teams to work in unison across all platforms. This is crucial in an AI-driven world where enhanced consistency, reduced token usage, and fast market reach become vital. By maintaining a single codebase, builders can focus their AI assistants on one unified context, drastically reduce token overhead, and minimize AI hallucinations. Instead of asking AI to translate features across fragmented, platform-specific languages, builders can leverage AI to write it once in Dart and instantly deploy it everywhere. Dash secretly hanging out in an alley doing agentive thingsThe existing value proposition Multiplatform development relies on enabling a single, shared source codebase. In our first-party Flutter apps, between 95% and 99% of the source code is shared. This massive code reuse unlocks several benefits: Faster time to market across multiple platforms because a team only needs to maintain one codebase. Guaranteed consistency across platforms, giving companies a single, consistent feature set to support across all their customers, regardless of their platform of choice. Native performance and stability because Flutter code is compiled to each platform's native machine code. Semantic guardrails increase security because the Dart language is strongly typed. The agentic value proposition While LLMs are good at translating requirements into code, using them to build separate native apps for each platform scales poorly. Replicating features across different languages using LLMs multiplies generation time and token usage, and can quickly lead to implementations drifting apart. Flutter's single-source solution eliminates these problems. But beyond just code sharing, Flutter's specific architecture makes it the ideal framework for agent-driven development. This emerging value proposition is driven by ## Why Reading Research Papers Can Accelerate Learning in Agentic Development DevFeed: [Why Reading Research Papers Can Accelerate Learning in Agentic Development]() Original publisher: [Read original article]() Author: hi@swizec.com (Swizec Teller) Published: 2026-04-20T00:00:00Z Content type: opinion Language: en Sources: [Swizec Teller]() Topics: [Agentic development](), [Development]() Tags: [agentic-development](), [agents](), [paper](), [reading](), [research]() ### AI overview The author argues that research papers are an efficient source of distilled knowledge for learning technical subjects. They describe using papers to build a mental framework for agentic development, while noting that expert conversations and books are better suited to very recent information or broad historical context. ### Source excerpt Read more papers. You can learn the latest and greatest in your field in one chill afternoon. ## Branches are GA: data infrastructure for agents DevFeed: [Branches are GA: data infrastructure for agents]() Original publisher: [Read original article]() Author: Jorge Sancha Published: 2026-02-24T12:00:00Z Content type: release Language: en Sources: [Tinybird]() Topics: [Data Infrastructure](), [Kafka](), [Amazon S3](), [ci](), [Development]() Tags: [agentic-development](), [agents](), [ci](), [connectors](), [data](), [data-infrastructure](), [development](), [kafka](), [product-updates](), [s3](), [workflows]() ### AI overview Tinybird announces general availability for Branches, adding Kafka, S3, and GCS connectors, CI preview deployments, and agentic development workflows. ### Source excerpt Branches now support Kafka, S3, and GCS connectors, preview deployments from CI, and agentic development workflows. Branches are how agents develop with Tinybird. ## Start building with Gemini 3 DevFeed: [Start building with Gemini 3]() Original publisher: [Read original article]() Author: Logan Kilpatrick Published: 2025-11-18T17:49:13Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [agentic-coding](), [Development](), [Command-line interface](), [ide](), [Benchmark]() Tags: [agentic-coding](), [agentic-development](), [antigravity](), [benchmark](), [cli](), [gemini](), [google](), [google-antigravity](), [jetbrains](), [linux](), [macos](), [none](), [preview](), [windows]() ### AI overview Google introduces Gemini 3, highlighting Gemini 3 Pro's reasoning, coding, agentic workflow, and benchmark performance. The model is available in preview through the Gemini API, Google AI Studio, and Vertex AI, with integration across developer tools. ### Source excerpt Gemini 3 is introducing advanced agentic coding capabilities, plus Google Antigravity, a new agentic development platform. ## Founder Mode: How Windsurf builds product, from 0 to 1M users DevFeed: [Founder Mode: How Windsurf builds product, from 0 to 1M users]() Original publisher: [Read original article]() Author: Tiffany Chen Published: 2024-12-03T00:00:00Z Content type: opinion Language: en Sources: [Mintlify Blog]() Topics: [Development](), [dogfooding](), [Agentic development](), [developer tooling](), [GitHub Copilot](), [Visual Studio Code]() Tags: [agentic](), [agentic-development](), [article](), [discord](), [dogfooding](), [for-founders](), [github](), [github-copilot](), [vscode]() ### AI overview Kevin Hou describes how Windsurf, formerly Codeium, grew from an initial VS Code extension into a product used by more than one million users. The article attributes this growth to a free-first strategy, user feedback, dogfooding, vertical integration, and a focus on agentic development tools. ### Source excerpt [Note: This was published under Codeium, but the company has since rebranded to Windsurf. Edits have been made accordingly.]