# Loop Engineering An emerging agentic engineering practice for designing recurring workflows that guide AI agents through multi-step tasks with limited human intervention. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Honoring #IconsOfQuality: Mark Hrynczak DevFeed: [Honoring #IconsOfQuality: Mark Hrynczak]() Original publisher: [Read original article]() Author: Rajrupa Roychowdhury Published: 2026-09-16T08:20:52Z Content type: opinion Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Loop Engineering](), [Artificial Intelligence](), [site-reliability-engineering](), [Amazon Web Services](), [atlassian]() Tags: [agentic](), [ai](), [atlassian](), [aws](), [icons-of-quality](), [quality](), [sre](), [testing](), [tooling]() ### AI overview BrowserStack profiles Mark Hrynczak, Canva's Head of Quality and QA Director, discussing how distributed quality ownership, agentic testing, and AI-driven decision support can help engineering teams move faster while maintaining reliability. ### Source excerpt To celebrate the relentless passion and invaluable contributions of leaders in software quality, BrowserStack is proud to honour Icons of Quality. ## How to operate shared platforms safely at agent scale DevFeed: [How to operate shared platforms safely at agent scale]() Original publisher: [Read original article]() Author: Candace Shamieh; T Zhang; Gabriele Baldoni Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Platform Engineering](), [Loop Engineering](), [AI Agent](), [sdlc](), [Latency](), [Low Latency](), [Concurrency](), [Software]() Tags: [agent](), [agent-observability](), [agentic-workflows](), [agents](), [ai](), [ai-agent](), [ci](), [concurrency](), [latency](), [low-latency](), [operational](), [performance](), [platform-engineering](), [platforms](), [queue](), [recovery](), [timeout]() ### AI overview This Datadog article explains how platform teams can operate shared platforms safely as AI agent workloads scale across teams. It discusses modeling demand across agent trajectories, planning capacity across dependencies such as CI queues and sandbox pools, handling contention and recovery behavior, and preserving control across system boundaries. ### Source excerpt Learn how Datadog models agent demand, allocates capacity under contention, and preserves control as AI agent workloads scale across shared platforms. ## Agentic Engineering in Python: From Vibes to Evidence DevFeed: [Agentic Engineering in Python: From Vibes to Evidence]() Original publisher: [Read original article]() Author: Ben Batman Published: 2026-09-14T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Loop Engineering](), [AI Agent](), [Python](), [Transformers](), [Code review](), [Test-driven development]() Tags: [agentic](), [agentic-engineering](), [ai-coding-agents](), [cheat-sheet](), [code-review](), [learning](), [python](), [tests]() ### AI overview This tutorial presents agentic engineering in Python as a disciplined workflow for delegating bounded tasks to AI coding agents while retaining human control over intent, constraints, review, and release decisions. It explains execution and acceptance loops, the Engineering Evidence Ladder, and the RECAP review method for evaluating agent-generated changes with tests, type hints, refactoring, and code review. ### Source excerpt Move from vibe coding to agentic engineering in Python, using tests, types, and code review to prove an AI agent's changes are safe to keep. ## Loop engineering: stop prompting, start looping DevFeed: [Loop engineering: stop prompting, start looping]() Original publisher: [Read original article]() Author: Anthony Viard Published: 2026-09-08T16:00:00Z Content type: article Language: en Sources: [Postman Blog]() Topics: [Loop Engineering](), [AI Agent](), [AI-assisted coding](), [API]() Tags: [2026](), [agent](), [agentic](), [agents](), [ai](), [ai-agents](), [ai-coding](), [api](), [api-testing](), [blog](), [code](), [coding-agents](), [developer](), [engineering](), [general](), [guide](), [idea](), [loops](), [model](), [post](), [postman-cli](), [tool](), [verify](), [work]() ### AI overview This article explains loop engineering as a way to improve AI coding agent reliability. Instead of relying on a single prompt, the system repeatedly generates code, runs it against a real API or other source of truth, verifies the result, and decides whether to stop or continue. The article distinguishes this outer verification loop from the inner ReAct tool-use cycle and presents a reproducible setup for allowing an agent to correct its own mistakes. ### Source excerpt Stop prompting your AI agent, start looping. A guide to loop engineering: wire a real API call in as the oracle so generated code stops guessing. The post Loop engineering: stop prompting, start looping appeared first on Postman Blog. ## How to Build Effective Product Loops in Claude Code, with Chief AI and Product Officer at JobNimbus, Tyler Folkman DevFeed: [How to Build Effective Product Loops in Claude Code, with Chief AI and Product Officer at JobNimbus, Tyler Folkman]() Original publisher: [Read original article]() Author: Aakash Gupta Published: 2026-09-04T23:01:00Z Content type: tutorial Language: en Sources: [Product Growth]() Topics: [Loop Engineering](), [Claude Code](), [Artificial Intelligence](), [Prompt Engineering]() Tags: [advice](), [agentic](), [ai](), [claude-code](), [loops]() ### AI overview A practical guide for PMs and product builders on creating product loops in Claude Code. It defines a loop as a process that fetches its own inputs, performs work, passes a gate, writes a named artifact, and repeats. The article also recommends writing a first loop by hand before using AI. ### Source excerpt People keep saying loops are the new prompts. Here's a practical walkthrough of what that actually means for PMs and product builders ## Making AI agents work in your team ๐Ÿค– -- with Dennis Pilarinos DevFeed: [Making AI agents work in your team ๐Ÿค– -- with Dennis Pilarinos]() Original publisher: [Read original article]() Author: Luca Rossi Published: 2026-09-04T07:01:38Z Content type: opinion Language: en Sources: [Refactoring]() Topics: [Artificial Intelligence](), [Loop Engineering]() Tags: [agents](), [ai](), [ai-agents]() ### AI overview A Refactoring Podcast episode about making AI agents work effectively within a team, featuring Dennis Pilarinos. ### Source excerpt Watch now | Refactoring Podcast - Episode 71 ## Granite 4.2 brings native reasoning to enterprise agents DevFeed: [Granite 4.2 brings native reasoning to enterprise agents]() Original publisher: [Read original article]() Author: Mike Murphy; Kim Martineau Published: 2026-08-25T15:00:00Z Content type: release Language: en Sources: [IBM Research]() Topics: [ibm](), [Large Language Model](), [Artificial Intelligence](), [Loop Engineering](), [Reinforcement learning](), [foundation-models](), [Software Engineering](), [Cloud]() Tags: [agentic](), [agents](), [ai](), [apache](), [cloud](), [fine-tuning](), [ibm](), [large-language-models](), [reasoning](), [reinforcement-learning](), [release](), [terminal]() ### AI overview IBM is releasing Granite 4.2 language models in 3B, 8B, and 30B sizes for enterprise agentic workflows. The models provide native reasoning, tool calling, instruction following, coding support, and deployment across cloud, on-premises, and edge environments. They are released under the Apache 2.0 license and trained with a multi-stage reinforcement learning process. ### Source excerpt IBM's new open Granite models are designed for agentic AI, combining reasoning, tool use, coding, instruction following, and speech capabilities. ## From Chrome DevTools to AI Engineering, with Addy Osmani DevFeed: [From Chrome DevTools to AI Engineering, with Addy Osmani]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-08-19T16:53:57Z Content type: article Language: en Sources: [The Pragmatic Engineer]() Topics: [AI Engineering](), [AI Agent](), [Developer experience](), [Google](), [developer tooling](), [engineering-culture](), [Loop Engineering](), [Chrome]() Tags: [agents](), [ai-agents](), [ai-engineering](), [developer](), [developer-experience](), [devtools](), [engineering](), [google](), [software-engineering](), [workflows]() ### AI overview Addy Osmani discusses his 14 years at Google, including work on Chrome, DevTools, Core Web Vitals, and AI developer experience. The conversation covers AI agents, loop engineering, cognitive surrender, engineering culture, and the broader skills engineers need. ### Source excerpt Addy Osmani shares lessons from 14 years at Google and how AI agents are reshaping software engineering, developer workflows, and the skills engineers need to succeed. ## Practical Loop Engineering DevFeed: [Practical Loop Engineering]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-08-14T14:30:53Z Content type: opinion Language: en Sources: [Elevate]() Topics: [Loop Engineering](), [AI Agent](), [Claude Code](), [codex](), [Code review](), [Scripting, bash]() Tags: [agent](), [agents](), [bash](), [claude-code](), [code-review](), [codex](), [engineering](), [loops](), [workflows]() ### AI overview An opinion article about practical loop engineering for AI-assisted software development. It describes running multiple agents in parallel, delegating bounded tasks with explicit stopping conditions, and using goal- and timer-based loops in Claude Code and Codex. It emphasizes careful supervision and well-defined goals and constraints, especially for complex or high-risk codebases. ### Source excerpt Goals, loops, and the discipline of not delegating your judgment ## Practical Loop Engineering DevFeed: [Practical Loop Engineering]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-08-14T00:00:00Z Content type: article Language: en Sources: [Addy Osmani]() Topics: [Loop Engineering](), [AI Agent](), [Claude Code](), [codex](), [Bash]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [bash](), [claude-code](), [code-review](), [codex](), [complexity](), [engineering](), [loops](), [workflows]() ### AI overview The article explains loop engineering as an autonomous, self-correcting feedback cycle in which an AI agent acts, tests results, and adjusts its approach toward a defined goal. It discusses Claude Code and Codex primitives, earlier Bash-based loops, and the need to define goals and constraints carefully before delegating work, especially in complex or high-risk codebases. ### Source excerpt Goals, loops, and the discipline of not delegating your judgment ## Agentic Engineering 101 DevFeed: [Agentic Engineering 101]() Original publisher: [Read original article]() Author: Daniel Moka Published: 2026-08-12T05:01:48Z Content type: tutorial Language: en Sources: [Craft Better Software]() Topics: [Loop Engineering](), [Prompt Engineering](), [context](), [Artificial Intelligence]() Tags: [agent](), [agentic](), [agentic-engineering](), [agents](), [ai](), [context-engineering](), [engineering](), [prompt](), [prompt-engineering]() ### AI overview This tutorial introduces agentic engineering as a set of layers around an AI model. It describes prompt engineering, context engineering, harness engineering, loop engineering, graph engineering, and memory engineering, with detailed guidance in the supplied text on prompt design and context curation. ### Source excerpt Prompt vs Context vs Harness vs Loop vs Graph Engineering ## Data Engineering Weekly #281 DevFeed: [Data Engineering Weekly #281]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-08-03T12:34:40Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [AI, ML & Data Engineering](), [Loop Engineering](), [Artificial Intelligence](), [Large Language Model](), [post-training]() Tags: [agent-harness](), [ai](), [data-engineering](), [feature-engineering](), [genai](), [netflix](), [newsletter](), [post-training](), [weekly]() ### AI overview Data Engineering Weekly #281 covers building data platforms, emerging approaches to AI workflow architecture, data modernization, Netflix's GenRec recommendation system, AI infrastructure modernization, and evaluation practices for generative AI at scale. ### Source excerpt The Weekly Data Engineering Newsletter ## ๐Ÿ”๐Ÿง  Software Factories: Harnessing Loops At Scale DevFeed: [๐Ÿ”๐Ÿง  Software Factories: Harnessing Loops At Scale]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-07-27T15:30:10Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [Loop Engineering](), [Orchestration](), [Software Engineering](), [Artificial Intelligence]() Tags: [agent](), [ai](), [architecture](), [automated](), [code](), [loops](), [parallel](), [review](), [sandbox](), [sandboxes](), [scale](), [software](), [software-engineering](), [test](), [verification]() ### AI overview The article explains how software factories use repeated agent loops, safety harnesses, parallel orchestration, work queues, and review gates to automate code production. It argues that verification, rather than code generation, is the main bottleneck, and warns that fully automated "dark factories" can accumulate comprehension debt when humans stop reading the code. ### Source excerpt PLUS: In-process load balancing ๐Ÿ’จ, On-disk ANN indexes ๐Ÿ’พ, Rust integ tests ๐Ÿงช ## Code review comments are the rules you forgot to write down DevFeed: [Code review comments are the rules you forgot to write down]() Original publisher: [Read original article]() Author: Marco Gomiero Published: 2026-07-27T00:00:00Z Content type: article Language: en Sources: [Posts on Marco Gomiero]() Topics: [Code review](), [Pull Request](), [Loop Engineering](), [context](), [Markdown]() Tags: [agent](), [ai-agents](), [also](), [code-review](), [maintenance](), [markdown](), [workflow]() ### AI overview The article describes a workflow for using AI agents to inspect recent pull-request review comments, identify recurring violations of project conventions, and propose updates to instruction files such as AGENTS.md. It presents code-review feedback as evidence of undocumented project rules and discusses using scripts to retrieve review comments. ### Source excerpt If you work with AI agents, you have an instruction file (and if you don't, you should!) usually called AGENTS.md (with CLAUDE.md symlinked to it, because standards are hard to follow). This Markdown file is automatically added to the AI context, and it should contain all the guidelines for the project and everything that the agent should be aware of. For example, how to build the project, styling, conventions, and anything else that should be known to achieve the best possible result. ## Software Factories, Light and Dark DevFeed: [Software Factories, Light and Dark]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-07-22T03:44:07Z Content type: opinion Language: en Sources: [Elevate]() Topics: [Loop Engineering](), [Software](), [Artificial Intelligence]() Tags: [agentic](), [agents](), [harness](), [loops](), [software]() ### AI overview The article revisits the idea of software factories through loops, harnesses, and factories. It distinguishes light factories, where humans remain involved, from dark factories, where agents scope, build, and ship code with limited human review. It argues that autonomy requires carefully designed checks, boundaries, tools, memory, and completion gates. ### Source excerpt A software factory is harnessing loops at scale. ## Software Factories, Light and Dark DevFeed: [Software Factories, Light and Dark]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-07-20T00:00:00Z Content type: article Language: en Sources: [Addy Osmani]() Topics: [Loop Engineering](), [Software](), [Artificial Intelligence]() Tags: [agentic](), [agents](), [ai](), [build](), [code](), [loops](), [software]() ### AI overview The article revisits the idea of software factories as repeatable, instrumentable production systems and describes light and dark factories based on how much human oversight they retain. It presents loop engineering as the design of agentic systems that repeatedly gather context, act, check results, and continue until a condition is met, with harnesses providing tools, memory, sandboxes, and completion gates. ### Source excerpt A software factory is harnessing loops at scale - the system that builds your software instead of you. You can run the loop with humans in it (light factory) or you can ignore the humans (dark factory) and let those agents scope, build and ship code, without anyone really reading the details. But if people stop reading, they'll stop understanding your software. Your hardest job now is knowing which checks to build and how much autonomy to delegate. ## A Feedback-Driven Agent Pattern for Self-Repairing AI Workflows DevFeed: [A Feedback-Driven Agent Pattern for Self-Repairing AI Workflows]() Original publisher: [Read original article]() Author: Wix Engineering Published: 2026-07-16T06:17:34Z Content type: tutorial Language: en Sources: [Wix Engineering]() Topics: [Loop Engineering](), [AI Agent](), [Code](), [context window](), [integrity]() Tags: [agentic-workflows](), [agents](), [ai-agent](), [ai-agents](), [code](), [context-window](), [validation]() ### AI overview This tutorial proposes a Feedback-Driven Agent pattern for building self-repairing AI agent workflows. It separates deterministic structural validation from goal-based assessment: agents produce artifacts, programmatic checks identify failures, and structured error reports route the agent toward repair. ### Source excerpt Every developer building with AI agents eventually hits the same wall. You write a masterpiece of a system prompt, loaded with rules, edge cases, and brand guidelines. It works perfectly... until you add one more rule. Suddenly, the agent gets "context fatigue," ignores half your instructions, and hallucinates a creative but entirely broken solution. We need to stop trying to make agents perfect on the first try. Instead, we suggest a new architectural approach: organizing agentic workflows... ## Building AX evals that actually work DevFeed: [Building AX evals that actually work]() Original publisher: [Read original article]() Author: Waldek Mastykarz Published: 2026-07-15T12:53:13Z Content type: tutorial Language: en Sources: [Developer Blogs]() Topics: [AI-assisted coding](), [Loop Engineering](), [Prompt Engineering]() Tags: [agent-experience](), [ai](), [ai-coding-agents](), [article](), [ax](), [coding-agents](), [evals](), [evaluation](), [how-to](), [microsoft](), [microsoft-for-developers](), [quality]() ### AI overview This eighth and final article in a series about Agent Experience explains how to build meaningful evaluations for AI coding agents. It identifies representative prompts, accurate and unambiguous criteria, and other structural decisions needed to produce useful signal rather than misleading scores. ### Source excerpt This is the eighth and final article in a series about Agent Experience (AX): the practice of making AI coding agents work correctly with your technology. The series covers what you can and can't control in the agent stack, how to measure whether your extensions are helping or hurting, and how to iterate toward better [...] The post Building AX evals that actually work appeared first on Microsoft for Developers. ## Why loop engineering needs a human at the boundary DevFeed: [Why loop engineering needs a human at the boundary]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-07-09T14:31:24Z Content type: opinion Language: en Sources: [Elevate]() Topics: [Loop Engineering](), [Software](), [systems]() Tags: [agentic-engineering](), [agents](), [engineering](), [observability](), [process](), [software](), [systems](), [trust](), [verification]() ### AI overview The article argues that engineers must own the outer loop of agentic software systems. It defines quality checks, production verdicts, and answerability as human responsibilities, while describing agents as models combined with tools, memory, permissions, sandboxes, tests, observability, and recovery. ### Source excerpt Why loop engineering needs a human at the boundary ## What Is Loop Engineering? A Practical Codex Example DevFeed: [What Is Loop Engineering? A Practical Codex Example]() Original publisher: [Read original article]() Published: 2026-07-08T10:30:00Z Content type: tutorial Language: en Sources: [Antonio Leiva]() Topics: [Loop Engineering](), [AI Agent](), [codex](), [Development]() Tags: [agent](), [agents](), [ai](), [architecture](), [codex](), [programming](), [pull-requests](), [software-engineering](), [verification](), [workflow]() ### AI overview This article explains Loop Engineering as the design of a complete software development loop around an AI agent. It describes state sources, isolated workspaces, verification, feedback mechanisms, human control points, manager and worker threads, and a pull request reviewer. ### Source excerpt Everything Android, Kotlin and other random topics ## Examples of Agent Goals, Loops, and Scheduled Workflows in Codex and Claude Code DevFeed: [Examples of Agent Goals, Loops, and Scheduled Workflows in Codex and Claude Code]() Original publisher: [Read original article]() Author: Owain Lewis Published: 2026-07-02T07:01:00Z Content type: article Language: en Sources: [The AI Engineer]() Topics: [codex](), [Loop Engineering](), [Automation](), [Claude Code](), [Pull Request](), [GitHub Issues](), [ci]() Tags: [agent](), [ai-agents](), [automation](), [ci](), [claude-code](), [codex](), [github-issues](), [logs](), [review](), [scheduled](), [tests](), [workflow]() ### AI overview The article explains how prompts, goals, recurring runs, and workflows differ, then presents examples of using agents for recurring engineering tasks. It emphasizes evidence, stop rules, tests, CI, logs, issues, pull requests, and human review as safeguards because agents can misunderstand context or make mistakes. ### Source excerpt Practical ways to combine prompts, goals, and scheduled runs in Codex and Claude Code. ## Why Agent Loops Fail in Production (and the Database Patterns That Fix Them) DevFeed: [Why Agent Loops Fail in Production (and the Database Patterns That Fix Them)]() Original publisher: [Read original article]() Author: Quentin Packard Published: 2026-07-01T00:00:00Z Content type: tutorial Language: en Sources: [Cockroach Labs]() Topics: [Loop Engineering](), [Databases](), [AI Agent](), [CockroachDB](), [PostgreSQL]() Tags: [agent](), [ai-agent](), [ai-agents](), [article](), [cockroachdb](), [database](), [loops](), [postgresql](), [production]() ### AI overview This article explains why AI agent loops can fail in production even when the underlying model reasons correctly. It focuses on database state, recovery, consistency, and auditability, with patterns and working code for PostgreSQL and CockroachDB. ### Source excerpt Agent loops fail in production for reasons that have little to do with the model, and everything to do with what happens to their state between iterations. ## A Framework for Running Long-Running AI Coding Agents at Scale DevFeed: [A Framework for Running Long-Running AI Coding Agents at Scale]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-06-29T15:31:27Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [Artificial Intelligence](), [Loop Engineering](), [Software Engineering](), [context window](), [Orchestration](), [cursor]() Tags: [agents](), [ai](), [ai-agents](), [claude](), [coding](), [context-window](), [cursor](), [engineering](), [implementation](), [memory](), [retry]() ### AI overview A DoorDash engineer describes a framework for using AI coding agents on multi-thousand-line features. It separates research, planning, implementation, and review, while using progress files, retryable loops, and deterministic orchestration to preserve continuity across context windows. ### Source excerpt PLUS: OpenAI's custom chip ๐Ÿค–, Netflix Cassandra ๐Ÿ“Š, CQRS deep dive โšก ## The AI-Assisted Engineering Workflow I Use Day to Day DevFeed: [The AI-Assisted Engineering Workflow I Use Day to Day]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2026-06-28T13:01:33Z Content type: tutorial Language: en Sources: [Neural Bits]() Topics: [AI-assisted coding](), [Loop Engineering](), [Test-driven development](), [Claude Code](), [codex](), [Production Engineering](), [coding]() Tags: [ai](), [claude](), [claude-code](), [codex](), [coding-agents](), [engineering](), [production](), [tdd](), [validation](), [workflow]() ### AI overview The article presents a controlled workflow for AI-assisted engineering with Claude, Codex, and other coding agents. It describes intake, task decomposition, test-driven development, bounded implementation, review gates, system validation, and sign-off while keeping the engineer involved throughout. ### Source excerpt How I structure intake, slicing, TDD, implementation, review gates, system validation, and signoff when working with Claude, Codex, and coding agents in production engineering work. [Next page]()