# Agentic development A software development approach in which AI agents autonomously perform multi-step coding tasks while developers set goals and review results. 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 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 ## Designing Libraries and SDKs for Human Developers and Coding Agents DevFeed: [Designing Libraries and SDKs for Human Developers and Coding Agents]() Original publisher: [Read original article]() Author: CommonsWare Published: 2026-08-22T17:08:47Z Content type: opinion Language: en Sources: [The CommonsBlog]() Topics: [SDKs](), [AI-assisted coding](), [Agentic development](), [Documentation](), [test](), [Specifications]() Tags: [agentic](), [blog](), [coding-agents](), [documentation](), [open-source](), [sdk](), [sdks](), [specifications](), [tests]() ### AI overview This opinion article argues that library and SDK authors should support both human developers and coding agents. It recommends clear source-code comments, generated API documentation, robust and runnable test suites, coverage analysis, and language-neutral specifications with conformance tests to clarify intended behavior and support faithful ports. ### Source excerpt Let's explore how we can succeed with libraries and SDKs at a time when coding agents will be consuming what we create, as will ordinary developers ## 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 ## 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. ## 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 ## 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. ## My Agentic Development and Productivity Setup in 2026 DevFeed: [My Agentic Development and Productivity Setup in 2026]() Original publisher: [Read original article]() Published: 2026-02-23T14:00:00Z Content type: opinion Language: en Sources: [Antonio Leiva]() Topics: [Agentic development](), [codex](), [Claude Code](), [Agent Skills](), [AI Development](), [coding]() Tags: [2026](), [agent-skills](), [agentic](), [ai](), [ai-agents](), [automation](), [claude](), [claude-code](), [codex](), [coding](), [development](), [productivity](), [software-development](), [workflow]() ### AI overview The author describes a personal 2026 workflow centered on AI agents, with Codex App used across active projects and parallel tasks. Claude Code complements it for UI refinement and a Telegram chatbot, while agent skills are described as Markdown files that guide agents. ### Source excerpt Everything Android, Kotlin and other random topics ## 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.]