# AI Engineering An emerging engineering discipline that combines systems engineering, software engineering, computer science, and human-centered design to create scalable, robust, and secure AI systems. 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 Future of Data Engineering in the Age of AI | Erfan Hesami DevFeed: [The Future of Data Engineering in the Age of AI | Erfan Hesami]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-09-16T13:21:19Z Content type: article Language: en Sources: [Data Engineering Central]() Topics: [data-engineering](), [AI Engineering](), [agent observability](), [coding](), [Security]() Tags: [agents](), [ai](), [ai-agents](), [ai-engineering](), [coding](), [data-engineering](), [fundamentals](), [governance](), [security]() ### AI overview An interview with Erfan Hesami examines how AI and agents may change data engineering, including the evolving role of data engineers, the overlap with AI engineering, the continuing importance of fundamentals, and the need to manage governance, security, costs, technical debt, and human judgment. ### Source excerpt AI Agents, Coding & Fundamentals ## Priyanka Halder on responsible AI, software quality, and scaling technology strategy DevFeed: [Priyanka Halder on responsible AI, software quality, and scaling technology strategy]() Original publisher: [Read original article]() Author: Rajrupa Roychowdhury Published: 2026-09-16T08:22:04Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [AI Engineering](), [Responsibility & Safety](), [AI Strategy](), [legacy]() Tags: [ai-engineering](), [cloud-native](), [engineering](), [icons-of-quality](), [quality](), [responsible-ai](), [software](), [testing]() ### AI overview BrowserStack profiles Priyanka Halder, an engineering executive at Deloitte, discussing responsible AI at scale, software quality, buy-versus-build decisions, and the skills needed to create scalable solutions with measurable business value. ### Source excerpt To celebrate the relentless passion and invaluable contributions of leaders in software quality, BrowserStack is proud to Icons of Quality. ## Chip Huyen explains how to cut inference costs without new hardware DevFeed: [Chip Huyen explains how to cut inference costs without new hardware]() Original publisher: [Read original article]() Author: Tim Koopmans Published: 2026-09-13T15:00:00Z Content type: article Language: en Sources: [The New Stack]() Topics: [Inference Performance](), [Low-Latency Inference](), [model-deployment](), [Optimization](), [Frontier Model](), [AI Engineering](), [Latency](), [math]() Tags: [ai](), [ai-agents](), [ai-engineering](), [ai-infrastructure](), [frontier-model](), [inference](), [low-latency](), [metrics](), [optimization](), [performance](), [post-contributed](), [reasoning](), [scylladb](), [sponsor-scylladb](), [sponsored](), [sponsored-post-contributed](), [tokens]() ### AI overview Chip Huyen explains why inference costs can outweigh one-time frontier-model training costs and outlines ways to optimize inference without new hardware. The article emphasizes latency metrics such as time to first token, time per output token, end-to-end latency, and goodput, especially for reasoning models. ### Source excerpt Last October, the P99 conference -- the online gathering for developers focused on high-performance, low-latency applications -- featured a cracking The post Chip Huyen explains how to cut inference costs without new hardware appeared first on The New Stack. ## Learn Claude Code, evals, AI systems, and more: ByteByteGo Live is here DevFeed: [Learn Claude Code, evals, AI systems, and more: ByteByteGo Live is here]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-11T15:32:16Z Content type: release Language: en Sources: [ByteByteGo]() Topics: [Artificial Intelligence](), [Claude Code](), [AI Development](), [AI Engineering]() Tags: [ai](), [ai-development](), [ai-engineering](), [ai-evals](), [claude-code](), [development]() ### AI overview ByteByteGo announces ByteByteGo Live, a membership offering live courses on Claude Code, production AI systems, AI engineering, AI evaluations, cost optimization, and related topics. The announcement cites higher completion rates for live cohorts and says the membership covers courses offered over the next 12 months. ### Source excerpt Most online courses never get finished (~4% completion). Live cohorts get ~40%, roughly 10x higher. Live courses are the only courses people actually finish. So we're launching ByteByteGo Live. ## 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. ## Our LDX3 New York 2026 Picks - Justin Mancinelli DevFeed: [Our LDX3 New York 2026 Picks - Justin Mancinelli]() Original publisher: [Read original article]() Published: 2026-09-08T13:00:00Z Content type: opinion Language: en Sources: [Touchlab | Enterprise Mobile Innovation & Development]() Topics: [Software Engineering](), [AI Engineering](), [Mobile](), [Kotlin Multiplatform](), [decision-making]() Tags: [2026](), [ai](), [community](), [conference](), [kotlin](), [kotlin-multiplatform](), [leadership](), [mobile]() ### AI overview Touchlab's Justin Mancinelli previews the LDX3 New York 2026 talks the company is most interested in, highlighting software engineering leadership, AI's effects on engineering and management, innovation practices, and mobile development with Kotlin Multiplatform. ### Source excerpt LDX3 is back in NYC and Touchlab will be in attendence. Here are the talks we're most excited about. ## GPT 6 Astra's performance in a software-engineering workflow DevFeed: [GPT 6 Astra's performance in a software-engineering workflow]() Original publisher: [Read original article]() Author: Armin Ronacher Published: 2026-09-07T00:00:00Z Content type: opinion Language: en Sources: [Armin Ronacher]() Topics: [Artificial Intelligence](), [AI Engineering](), [Software Engineering](), [computer-use](), [ChatGPT](), [Python](), [Concurrency]() Tags: [agent](), [ai](), [ai-engineering](), [chatgpt](), [computer-use](), [python](), [software-engineering](), [thoughts]() ### AI overview The author argues that AI engineering can intensify effort without improving productivity and examines GPT 6 Astra's usefulness for software engineering. A self-managed software factory using Astra produced substantial code and prompts over 35 hours but, according to the author, delivered nothing of value and provided no clear lessons for improving the workflow. ### Source excerpt I'm more and more convinced that all of AI engineering is Neijuan (内卷, meaning curl inwards). In China it describes a system that demands ever more effort and competition without improving output. The way in which it sometimes shows up in the West is the 996 nonsense. The English term for Neijuan is "Involution" from the book Agricultural Involution. Agricultural involution describes the intensification of farming that raises productivity per square meter while leaving productivity per head unchanged. That's how I feel about AI right now. Which brings me to GPT 6 Astra. Astra is by all accounts an incredibly impressive model. There is really not much I can say against this. It's amazing at computer use, understands images and complex topics, and it's relentless in its pursuit of completion. It is absolutely impressive; these types of models are going to change the world in one form or another. But at least for the moment I don't know how to work with it for actual software engineering. Since that got quite a bit of attention on Twitter, I figured I might summarize my thoughts and just share what kind of code comes out of this thing. My Slop Factory "Armin, you should run a software factory!" I've heard that a few times now, so I figured I might celebrate the release of it by running a little software factory over the weekend. If everybody builds slop 3D games, then I should do something useful with it. My software factory was intentionally set up to let the model decide the how of the workflow entirely. It was free to manage its own context and could maintain its own records in an agent-notes folder. Then it spun off subagents to work on stuff. The goal? What if we had a Python with virtual threads and lexical scoping. And well, I burned a full reset's worth of ChatGPT tokens on this which appears to be around 4 billion tokens. 35 hours later, the factory has delivered absolutely nothing of value and also not taught me anything about how to operate a better one. But ## 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. ## Agentic Engineering 2.0 Explained: The Future of AI Engineering DevFeed: [Agentic Engineering 2.0 Explained: The Future of AI Engineering]() Original publisher: [Read original article]() Author: Zohar Einy Published: 2026-08-10T11:32:21Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [AI Engineering](), [Artificial Intelligence](), [Incident response](), [Graphs](), [Code review]() Tags: [agentic](), [agents](), [ai-agents](), [ai-engineering](), [autonomous](), [building](), [development](), [incident](), [incident-response](), [latency](), [scope](), [workflow]() ### AI overview The article distinguishes two generations of agentic engineering. Version 1.0 uses AI agents in deterministic workflows with predefined stages, prompts, responsibilities, and hand-offs. Version 2.0 gives an agent a goal and lets it determine the path, with the foundation built during the first generation enabling that transition. A self-healing incident workflow illustrates the contrast. ### Source excerpt Agentic engineering 1.0 wires the path; 2.0 hands agents a goal. See how the shift works and what foundation to build in 2026. ## How OpenAI Built a Reliable Data Agent with Context, Memory, and Evals DevFeed: [How OpenAI Built a Reliable Data Agent with Context, Memory, and Evals]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-08-02T13:14:27Z Content type: article Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [OpenAI](), [AI Engineering](), [AI Development](), [data](), [SQL](), [Slack]() Tags: [agents](), [ai](), [ai-agents](), [ai-engineering](), [data](), [evals](), [memory](), [openai](), [schema](), [sql](), [thread](), [verify]() ### AI overview The article examines how OpenAI built an internal data agent for more than 3,500 users working across 70,000 datasets and over 600 petabytes of data. It explains that trustworthy results require more than valid SQL: the agent needs relevant business context, safeguards for permissions, ways to detect subtle query errors, and transparency so users can inspect its work. ### Source excerpt How OpenAI built its data agent to work across 70,000 datasets. ## 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 ## Platform engineering makes a difference. Here's how to prove it DevFeed: [Platform engineering makes a difference. Here's how to prove it]() Original publisher: [Read original article]() Author: Liz Coolman Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [internal developer platform](), [AI Engineering](), [Developer Platform](), [configuration](), [Security](), [telemetry]() Tags: [ai](), [ai-engineering](), [ai-impact](), [cognitive-load](), [configuration](), [developer-platform](), [encryption](), [engineering](), [idp](), [infrastructure](), [least-privilege](), [metrics](), [performance](), [platform](), [platform-engineering](), [platforms](), [security](), [software](), [speed](), [tooling]() ### AI overview This article explains how platform engineering teams can prove the value of an internal developer platform (IDP) to executives. It describes how IDPs reduce developer friction and cognitive load through standardized golden paths, configuration, and tooling; provide guardrails for security, encryption, and least-privilege access; and help organizations manage the effects of AI on engineering work. It recommends measuring velocity indicators such as cycle time and lead time to change, along with AI adoption, impact, and other metrics, to quantify platform performance and guide improvements. ### Source excerpt Learn how to prove the value of platform engineering to executives. This article outlines the essential metrics--from velocity and AI impact to developer sentiment--needed to quantify the success of your Internal Developer Platform (IDP) and keep pace with evolving AI capabilities. ## From Android to AI Engineering DevFeed: [From Android to AI Engineering]() Original publisher: [Read original article]() Author: Lukas Lechner Published: 2026-07-22T15:14:21Z Content type: opinion Language: en Sources: [Lukas Lechner]() Topics: [AI Engineering](), [Large Language Model](), [Development]() Tags: [ai-engineering](), [allgemein](), [developer](), [llm]() ### AI overview The author describes transitioning from Android development to backend development and then to AI Engineering after releasing the AI Engineering Fundamentals course. The article explains that AI Engineering involves building applications on top of large language models and addresses production concerns such as model cost, context optimization, and latency. ### Source excerpt I released a new course "AI Engineering Fundamentals". 👉 https://www.udemy.com/course/ai-engineering-fundamentals-build-real-llm-apps-in-python/?referralCode=D1BA2721D48381A98F83 Below you can find the story about my transition 👇 Many Android Developers know me as the "Coroutines & Flow Guy", since I have the most popular udemy course on that topic. What few people know is that I haven't worked as an Android Developer Read More The post From Android to AI Engineering first appeared on Lukas Lechner. ## Adopting Agentic: Talks and Reflections from Our AI Engineering Event DevFeed: [Adopting Agentic: Talks and Reflections from Our AI Engineering Event]() Original publisher: [Read original article]() Author: Colin Eberhardt Published: 2026-07-15T00:00:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [Artificial Intelligence](), [AI Engineering](), [Software Engineering](), [Automation]() Tags: [agentic](), [ai](), [ai-engineering](), [automation](), [autonomous](), [engineering](), [event](), [reflections](), [sdlc](), [software-engineering](), [talks](), [tech]() ### AI overview This article summarizes talks and discussions from the Adopting Agentic event, focusing on how AI is affecting software engineering, individual skills and job roles, teams, organizational practices, and the software development life cycle. It also includes the author's reflections, key quotes, and session videos. ### Source excerpt A summary of the talks and discussions from our recent event, Adopting Agentic - Software Engineering for the AI Age, exploring the human and organisational impact of AI on our industry. Includes session videos, key quotes and my personal reflections on each presentation. ## How to Build Unified Agent Memory from Scratch DevFeed: [How to Build Unified Agent Memory from Scratch]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-07-14T05:01:54Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [AI Engineering](), [Claude Code](), [Graphs](), [LangChain](), [Large Language Model](), [Database](), [Model Context Protocol]() Tags: [agent](), [ai-engineering](), [claude-code](), [graph](), [graph-database](), [knowledge-graphs](), [langchain](), [llm](), [mcp]() ### AI overview A tutorial on building a unified agent memory layer from scratch using knowledge graphs, including ingestion, querying, and serving. It discusses trade-offs among vector databases, graph databases, temporality, versioning, MCP servers, CLIs, and skills, and explains why understanding the underlying memory layer matters. ### Source excerpt Ingest, query, and serve a unified memory from a single database. ## AI Reliability Engineering DevFeed: [AI Reliability Engineering]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-07-12T17:34:19Z Content type: opinion Language: en Sources: [Alex Ewerlof Notes]() Topics: [AI Engineering](), [site-reliability-engineering](), [Large Language Model](), [Generative AI](), [Software Engineering]() Tags: [ai](), [ai-engineering](), [generative-ai](), [llms](), [reliability-engineering](), [software-engineering](), [sre]() ### AI overview This article examines how site reliability engineering practices can be adapted for AI systems and AI-generated black boxes. It focuses on the need to run these systems predictably, securely, and at scale as large language models and their supporting harnesses become more capable. ### Source excerpt Why SRE is a key skill in the age of AI-generated black boxes and how to renovate the traditional toolbox for the new era ## Harness June 2026 Product Updates DevFeed: [Harness June 2026 Product Updates]() Original publisher: [Read original article]() Author: Chinmay Gaikwad Published: 2026-07-03T00:00:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [Automation](), [Artificial Intelligence](), [AI Engineering](), [Playwright](), [Test automation](), [Testing]() Tags: [2026](), [agents](), [ai](), [ai-agents](), [ai-engineering](), [automation](), [playwright](), [product-updates](), [release](), [test-automation]() ### AI overview Harness's June 2026 product updates included 62 features, including Autonomous Worker Agents, parallel DAG pipeline execution, AI Test Automation with Playwright, AI SAST, feature flag updates, and AI Engineering Insights. ### Source excerpt See everything Harness shipped in June 2026, including Autonomous Worker Agents, parallel DAG pipelines, AI Test Automation with Playwright, AI SAST, feature fl | Blog ## Debug and evaluate your AI app from your coding agent with Datadog Agent Observability DevFeed: [Debug and evaluate your AI app from your coding agent with Datadog Agent Observability]() Original publisher: [Read original article]() Author: Michael Bevilacqua-Linn; Till W; Tanguy Renaudie; Mehul Sonowal; Gabriele Lorenzo; Alex Barksdale Published: 2026-06-30T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [agent observability](), [AI-assisted coding](), [AI Engineering](), [observability](), [MCP](), [MCP Server](), [Command-line interface](), [LLM evaluation / benchmarking](), [datasets](), [Traces](), [debug](), [experiments]() Tags: [agent-observability](), [agent-skills](), [agents](), [ai](), [ai-engineering](), [ai-observability](), [analysis](), [api](), [claude](), [claude-code](), [cli](), [code](), [codex](), [coding](), [command-line](), [cursor](), [datasets](), [debug](), [eval](), [how-to](), [observability]() ### AI overview This article explains how to use Datadog Agent Observability from coding agents such as Claude Code, Cursor, and Codex CLI. It presents the Datadog MCP Server, Pup CLI, and Agent Skills as ways to access traces, evaluation results, experiment metrics, and other telemetry for classifying sessions, debugging production failures, creating evaluation datasets, and generating fixes. ### Source excerpt Learn how to give your coding agent access to Datadog Agent Observability data to classify failures, run RCA, bootstrap evaluators, and generate fixes. ## Loop engineering: building autonomous cycles for coding agents DevFeed: [Loop engineering: building autonomous cycles for coding agents]() Original publisher: [Read original article]() Author: Eric Roby Published: 2026-06-24T12:03:21Z Content type: article Language: en Sources: [Eric Roby]() Topics: [Loop Engineering](), [AI Engineering](), [Claude Code]() Tags: [agents](), [ai](), [ai-engineering](), [coding-agents](), [engineering](), [verify]() ### AI overview The article argues that working effectively with coding agents is shifting from writing individual prompts to designing autonomous loops that generate prompts, use tools, and run longer with less human intervention. It presents loop engineering as a third era after prompt engineering and context or harness engineering, while noting that token costs require caution. ### Source excerpt The shift from crafting individual instructions to building autonomous cycles that run, verify, and improve themselves. ## MCP Clearly Explained DevFeed: [MCP Clearly Explained]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-06-15T12:57:01Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Model Context Protocol (MCP)](), [AI Engineering](), [Protocol (disambiguation)](), [client](), [Server]() Tags: [ai-engineering](), [coding-assistant](), [integration](), [model-context-protocol-mcp](), [protocol](), [server](), [tool]() ### AI overview A beginner-friendly explanation of Model Context Protocol (MCP), a standard interface that lets AI applications communicate with external services. It describes MCP's purpose, core roles, capability discovery, data requests, action invocation, session establishment, and compatibility features. ### Source excerpt Plus: Big upgrades are coming to LUC. Vote on what you'd like to see first. ## Build, Configure, or Use As-Is: The Agentic Harness DevFeed: [Build, Configure, or Use As-Is: The Agentic Harness]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-06-09T05:00:28Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [Tool](), [AI Engineering](), [codex](), [Claude Code](), [Pydantic](), [TypeScript]() Tags: [agentic](), [agents](), [ai](), [ai-engineering](), [architecture](), [article](), [building](), [memory](), [permission](), [sandbox](), [sandboxes](), [tools]() ### AI overview This article explains the shared system design of agentic harnesses, covering tools, agent catalogs, subagents, skills, memory, sandboxes, and permissions. It argues that these harness components are increasingly standardized, while the main differentiation lies in the context and business layers built on top. ### Source excerpt A component-by-component teardown of an agentic harness, from tools and skills to memory, sandbox, and permissions. ## How AI Is Changing Coding Interviews in 2026 DevFeed: [How AI Is Changing Coding Interviews in 2026]() Original publisher: [Read original article]() Author: Brian Jenney Published: 2026-04-28T16:21:33Z Content type: opinion Language: en Sources: [Brian Jenney]() Topics: [coding](), [AI Engineering](), [Artificial Intelligence](), [HackerRank](), [LeetCode]() Tags: [2026](), [ai](), [career](), [coding](), [interviews](), [software-engineer](), [technical-interview]() ### AI overview This opinion article argues that coding interviews in 2026 remain disconnected from day-to-day software work, while AI has changed how developers write code. It describes traditional data-structures-and-algorithms assessments and other interview formats developers may encounter. ### Source excerpt Coding interviews have been notoriously tone deaf for years. ## What Happened When We Treated AI Like an Engineering Teammate DevFeed: [What Happened When We Treated AI Like an Engineering Teammate]() Original publisher: [Read original article]() Author: Global Technology Published: 2026-04-28T13:23:51Z Content type: opinion Language: en Sources: [McDonald's Technical Blog - Medium]() Topics: [Artificial Intelligence](), [AI Engineering](), [maintenance](), [Security](), [Cloud](), [test-coverage](), [Documentation](), [Framework]() Tags: [agent](), [ai](), [ai-agent](), [ai-engineering](), [autonomous](), [cloud](), [development](), [documentation](), [maintenance](), [platform-engineering](), [security](), [software-engineering](), [test-coverage](), [the-result](), [velocity]() ### AI overview The Restaurant Topology Management team used an AI-powered, agent-based engineering assistant within its development workflows to handle repetitive maintenance work, including configuration updates, security patches, framework modernization, test coverage, and documentation. The article reports 20% faster sprint velocity, security updates completed twice as fast, and modernization delivered up to 10 times faster. ### Source excerpt How an AI engineering assistant boosted sprint velocity by 20% and modernized services up to 10x faster. by: Sumedha Shenoy, Director, Engineering Tech Lead, and Kamal Jackson, Sr Manager, Engineering Tech Lead Quick Bytes: A growing backlog of essential but repetitive "glue work" was slowing the Restaurant Topology Management (RTM) team's ability to modernize and deliver new features By treating an AI engineering assistant as an autonomous teammate -- not a chatbot -- the team offloaded end-to-end maintenance tasks at scale The result was 20% faster sprint velocity, security updates completed twice as fast, and modernization work delivered up to 10x quicker without sacrificing quality In fast-moving software environments, critical but repetitive "glue work" like configuration updates, security patches, and documentation often compete with time that could be spent delivering new features. For the Restaurant Topology Management (RTM) team -- who build and maintain the cloud-based platform that enables markets to create and deploy consistent restaurant device configurations -- this maintenance workload had grown quietly over time and began to limit the team's ability to innovate. To regain momentum and address mounting operational debt, the team turned to an AI-powered, agent-based engineering assistant embedded into our standard development workflows, capable of handling complex, repetitive tasks. The challenge: The "un-fun" backlog Before piloting the AI assistant, the RTM team was managing maintenance tasks that had persisted across multiple sprints: Security updates: Enhancing automated scanning workflows to maintain a strong security posture Framework modernization: Updating services to align with the team's current application framework Test coverage improvements: Strengthening unit test consistency across services Documentation updates: Refreshing internal service documentation to support smoother onboarding and troubleshooting This maintenance work was important b ## Ralph Loops Use Fresh Context and External Verification to Improve AI Agent Workflows DevFeed: [Ralph Loops Use Fresh Context and External Verification to Improve AI Agent Workflows]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-04-23T11:02:54Z Content type: article Language: en Sources: [Decoding ML]() Topics: [Artificial Intelligence](), [AI Engineering]() Tags: [agent-orchestration](), [agents](), [ai](), [ai-agents](), [ai-engineering](), [loops]() ### AI overview The article argues that replacing complex multi-agent systems with a writer-and-reviewer loop can simplify AI workflows. Ralph loops reset the conversation on each iteration, reload the specification, use filesystem and git for memory, and rely on objective signals such as tests or linters for verification. ### Source excerpt How one simple loop beats multi-agent orchestration and context rot in production. [Next page]()