# context-engineering Published articles for context-engineering. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Using Six Scoped Subagents to Manage Context Windows DevFeed: [Using Six Scoped Subagents to Manage Context Windows]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-09-01T05:00:29Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [context window](), [long-context](), [Artificial Intelligence](), [Code](), [coding](), [Claude](), [Claude Code](), [codex](), [LangChain](), [Python](), [Cloud]() Tags: [agent](), [agents](), [ai](), [claude](), [claude-code](), [cloud](), [codex](), [coding](), [coding-agents](), [context-engineering](), [context-window](), [langchain](), [python]() ### AI overview This tutorial explains how splitting research work among six parallel subagents can reduce context-window noise and limit the state returned to an orchestrator agent. It also covers designing subagent protocols, maintaining an agent registry, and running parallel agent workflows. ### Source excerpt The harness determines what your orchestrator agent never sees, not the model. ## 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 ## Context Engineering for Coding Agents DevFeed: [Context Engineering for Coding Agents]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-08-25T05:01:37Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [context window](), [AI-assisted coding](), [coding](), [Claude](), [codex]() Tags: [agents](), [claude](), [codex](), [coding-agents](), [context](), [context-engineering](), [context-window](), [harness]() ### AI overview This tutorial explains context engineering for coding agents, focusing on memory, skills, LSP servers, compaction, and feedback loops. It presents these techniques as ways to keep an agent's context high-signal and improve coding-agent performance. ### Source excerpt The 4 harness components that keep your context window high-signal. ## 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 ## 11 Context-Engineering Tips for Using Coding Agents Efficiently DevFeed: [11 Context-Engineering Tips for Using Coding Agents Efficiently]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-08-04T11:04:10Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [AI-assisted coding](), [Large Language Model](), [Documentation](), [anthropic](), [codex](), [Obsidian]() Tags: [2026](), [anthropic](), [codex](), [coding-agents](), [context-engineering](), [docs](), [tokens](), [weekly]() ### AI overview The author shares 11 context- and harness-engineering techniques for using coding agents while controlling token use. The advice includes progressive disclosure, detailed planning with cheaper models handling execution, LLM wikis for project context, and subagents that return summaries. ### Source excerpt Context engineering techniques to avoid wasting tokens on what doesn't matter ## Context engineering with Dex Horthy DevFeed: [Context engineering with Dex Horthy]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-07-15T16:08:59Z Content type: article Language: en Sources: [The Pragmatic Engineer]() Topics: [AI-assisted coding](), [Large Language Model](), [AI Agent](), [Code quality]() Tags: [ai](), [ai-agent](), [code-quality](), [context-engineering](), [langchain](), [llm]() ### AI overview Dex Horthy discusses context engineering for working around LLM context limitations in AI-assisted software development. The conversation covers context engineering, harness engineering, loop engineering, software factories, and automating more of the software development lifecycle while maintaining code quality. ### Source excerpt Dex Horthy explains why context engineering is key to building more effective AI-assisted software without sacrificing code quality. ## New Open-Source Context Specs Released DevFeed: [New Open-Source Context Specs Released]() Original publisher: [Read original article]() Author: Capital One Tech Published: 2026-06-24T16:58:13Z Content type: article Language: en Sources: [Capital One Tech]() Topics: [Spec Driven Development](), [AI-assisted coding](), [context window](), [Open Source](), [Development]() Tags: [ai-assisted-coding](), [context-engineering](), [context-window](), [development](), [open-source](), [software-engineering](), [spec-driven-development]() ### AI overview Capital One has open-sourced Context Specs, a spec-driven development framework that uses context engineering for AI-assisted coding. It captures domain knowledge in reusable experts and uses automated feedback signals to verify an agent's work, addressing context-window constraints such as context decay, pollution, and compaction loss. ### Source excerpt Capital One open-sources Context Specs, an SDD framework using context engineering to bridge business ideas and code. Every developer has had the same experience: The AI writes code confidently, only for you to realize that it has missed the mark. The fix isn't a better model, it's better context. Right context, right time. That's the whole game. To address this challenge, Capital One is open-sourcing Context Specs, a framework for spec-driven development (SDD) that treats context engineering-the practice of building systems that dynamically decide what your agent sees and when it sees it-as the primary lever for AI-assisted coding. The framework achieves this by capturing a team's domain-specific knowledge into reusable "experts" that you create once and compose across the entire workflow, and then using automated feedback "signals" to verify the agent's work. The real problem: Your agent's context window is finite Every AI coding agent operates within a context window, a fixed amount of information it can see at any given moment. This constraint creates three mechanical failure modes that plague every developer using AI tools today: Context decay: Older messages in a conversation get ignored, summarized or dropped entirely. That careful instruction you gave 20 messages ago? Gone. Context pollution: When an agent searches your codebase autonomously, it pulls in irrelevant files. Every irrelevant token displaces a useful one. Compaction loss: When the window fills up, the system summarizes history to make room. You don't control what gets dropped. These aren't edge cases; they're the default experience. Most frameworks for AI-assisted development weren't designed with these constraints in mind. Some frameworks generate thousands of lines of specification markdown before a single line of code is written, burning millions of tokens on ceremony. Other frameworks are so lightweight they leave everything to interpretation, forcing the agent to search blindly and pollute ## Why Agentic Coding Tools Require Software Engineers to Focus on Building Products DevFeed: [Why Agentic Coding Tools Require Software Engineers to Focus on Building Products]() Original publisher: [Read original article]() Author: Michael Werle Published: 2026-03-06T15:07:10Z Content type: opinion Language: en Sources: [Indeed]() Topics: [ai-coding](), [AI Development](), [Software Engineering](), [Model Context Protocol](), [ide]() Tags: [agentic-coding](), [ai](), [ai-coding](), [context-engineering](), [engineering](), [engineering-culture](), [how-to](), [mcp](), [sandboxing](), [software-engineering](), [unsorted]() ### AI overview This opinion article argues that software engineers should adapt to agentic coding tools by focusing less on manual coding and more on building products that solve customer problems. It describes the author's changing views on AI-assisted programming and highlights context engineering, MCP, skills, sandboxing, subagents, and related tools, while acknowledging significant security and automation risks. ### Source excerpt Why agentic coding tools demand a new identity for software engineers After more than two decades of professional software engineering, I have arrived at a set of conclusions that I find very uncomfortable. The era of mostly manual coding has ended. IDEs, in their current form, are no longer necessary. Traditional software development languages are [...] ## What 300+ Engineers from Netflix, Amazon, and Instacart Asked About AI Engineering DevFeed: [What 300+ Engineers from Netflix, Amazon, and Instacart Asked About AI Engineering]() Original publisher: [Read original article]() Author: Hugo Bowne-Anderson Published: 2026-03-06T07:03:44Z Content type: article Language: en Sources: [MarvelousMLOps]() Topics: [AI Engineering](), [AI Development](), [Large Language Model](), [Prompt Engineering](), [Testing]() Tags: [ai-engineering](), [context-engineering](), [engineering](), [evaluation](), [llms](), [testing]() ### AI overview This article presents the top 10 questions and answers gathered from four cohorts of a Building AI Applications course attended by more than 300 builders from companies including Netflix, Amazon, and Instacart. The supplied excerpt details how to improve reliability and consistency in LLM applications through prompt and context engineering, structured outputs, validation, evaluation, and testing. ### Source excerpt The Top 10 questions (and answers) from 4 cohorts of Building AI Applications ## A Seven-Level AI Fluency Framework for Knowledge Workers DevFeed: [A Seven-Level AI Fluency Framework for Knowledge Workers]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-01-30T18:14:37Z Content type: tutorial Language: en Sources: [Alex Ewerlof Notes]() Topics: [AI Development](), [Prompt Engineering](), [Retrieval Augmented Generation (RAG)](), [context](), [Development]() Tags: [ai](), [ai-development](), [context-engineering](), [guide](), [prompt-engineering](), [rag](), [technical]() ### AI overview This guide presents a seven-level, directional framework for assessing and developing AI fluency among knowledge workers. It describes progression from prompt engineering toward RAG, model ROI, context engineering, and deterministic AI system architecture, with applications for learners, leaders, product teams, consumers, and hiring managers. ### Source excerpt Transition from prompt "engineering" to AI system architecture with this 7-step AI fluency framework. Designed for software engineers and SREs, this guide provides a roadmap for mastering RAG, context engineering, and the critical shift from probabilistic to deterministic AI development. ## How AI Performs at React Coding and How Developers Can Improve Its Results DevFeed: [How AI Performs at React Coding and How Developers Can Improve Its Results]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2025-12-29T15:31:06Z Content type: tutorial Language: en Sources: [Elevate]() Topics: [React](), [Artificial Intelligence](), [ai-coding](), [coding](), [Development]() Tags: [ai](), [ai-assistants](), [ai-coding](), [benchmarks](), [coding](), [context-engineering](), [guide](), [react]() ### AI overview This article examines how AI coding tools perform for React developers. It reports stronger results on isolated tasks such as scaffolding components and implementing explicit requirements, but weaker results on multi-step integrations involving complex state management and design judgment. It argues that context engineering, specific prompts, structured workflows, guardrails, and deep React knowledge can improve outcomes. ### Source excerpt A data-driven look at what AI can and can't do for React developers - and what you can do about it ## How Wix Used Context Engineering for an AI Scheduling Assistant DevFeed: [How Wix Used Context Engineering for an AI Scheduling Assistant]() Original publisher: [Read original article]() Author: Wix Engineering Published: 2025-12-09T08:38:23Z Content type: article Language: en Sources: [Wix Engineering]() Topics: [context](), [Artificial Intelligence](), [AI Agent](), [Large Language Model](), [Development]() Tags: [agent](), [ai](), [context-engineering](), [data](), [llm](), [prompt-engineering]() ### AI overview Wix describes using context engineering to prepare, structure, and optimize data before sending it to an LLM for an AI scheduling assistant. The article explains how this preprocessing approach supports ranked candidate matching and enables use of the faster, lower-cost Gemini Flash 2.0 model. ### Source excerpt Intro: The Limits of Prompt Engineering In modern AI agent development, many teams, including ours at Wix , start with an intense focus on prompt engineering , carefully crafting instructions for the LLM. We iterated endlessly on system messages and instruction sets to solve a core problem: our AI scheduling assistant was struggling with vague requests, such as "Schedule a meeting with Jake." This simple sentence triggers a surprisingly complex challenge. The system must identify which... ## Agent Skills: How is it different from command and other tools? DevFeed: [Agent Skills: How is it different from command and other tools?]() Original publisher: [Read original article]() Author: Kaushik Gopal Published: 2025-10-20T11:45:11Z Content type: opinion Language: en Sources: [Kaushik Gopal's Site]() Topics: [Agent Skills](), [context window](), [Artificial Intelligence](), [Claude](), [Markdown](), [YAML]() Tags: [agent](), [agent-skills](), [ai](), [claude](), [context](), [context-engineering](), [context-window](), [markdown](), [tokens](), [yaml]() ### AI overview This commentary examines Anthropic's Claude Skills and argues that their important innovation is progressive disclosure: Claude scans lightweight YAML metadata, builds a capability index, and loads full skill content only when relevant. It compares this approach with AGENTS.md, nested instructions, slash commands, and MCPs, emphasizing improved context and token efficiency. ### Source excerpt Anthropic announced Claude Skills and my first reaction was: "So what?" We already have AGENTS.md, slash commands, nested instructions, or even MCPs. What's new here? But if Simon W thinks this is a big deal, then pelicans be damned; I must be missing something. So I dissected every word of Anthropic's eng. blog post to find what I missed. I don't think the innovation is what Skills does or achieves, but rather how it does it that's super interesting. This continues their push on context engineering as the next frontier. Addendum Skills has been out for sometime and Anthropic has even made it an open standard. Make sure to read the addedum at the end of this post, where I share some newer realizations I've had since first writing it. Skills and how it works # Skills are simple markdown files with YAML frontmatter. But what makes them different is the idea of progressive disclosure: Progressive disclosure is the core design principle that makes Agent Skills flexible and scalable. Like a well-organized manual that starts with a table of contents, then specific chapters, and finally a detailed appendix, skills let Claude load information only as needed: So here's how it works: Scan at startup: Claude scans available Skills and reads only their YAML descriptions (name, summary, when to use) Build lightweight index: This creates a catalog of capabilities (with minimal token cost); so think dozens of tokens per skill Load on demand: The full content of a Skill only gets injected into context when Claude's reasoning determines it's relevant to the current task This dynamic context loading mechanism is very token efficient; that's the interesting development here. In this token-starved AI economy, that's 🤑. Other solutions aren't as good in this specific way. Why the alternatives aren't as good # AGENTS.md (monolithic) ## ✓ Auto-discovered and loaded ✗ Static: all context loaded upfront (bloats context window at scale) Why not throw everything into AGENTS.md? You could add ## Temporal and the next frontier: Scaling AI reliably DevFeed: [Temporal and the next frontier: Scaling AI reliably]() Original publisher: [Read original article]() Author: Samar Abbas Published: 2025-09-03T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [systems](), [AI Infrastructure](), [agentic workflows](), [SDKs](), [OpenAI](), [MongoDB](), [Pydantic](), [Retrieval Augmented Generation (RAG)]() Tags: [agentic-workflows](), [ai](), [ai-infrastructure](), [announcements](), [context-engineering](), [developer-community](), [mongodb](), [openai](), [rag](), [sdk]() ### AI overview Temporal describes how its durable workflow infrastructure is being applied to production AI systems, including agentic workflows, multiple AI models, stateful processes, and integrations with OpenAI, MongoDB, and Pydantic. ### Source excerpt At Temporal, we've always focused on one thing: making it simpler to build systems that work and keep working -- reliably at scale and in the messy reality of prod. That mission matters more today than ever. ## Explainer: K2 & Math Olympiad Golds DevFeed: [Explainer: K2 & Math Olympiad Golds]() Original publisher: [Read original article]() Published: 2025-07-19T00:00:00Z Content type: article Language: en Sources: [Tim Kellogg]() Topics: [Artificial Intelligence](), [multi-agents](), [Chain-of-thought](), [Claude](), [deepseek](), [Model Context Protocol](), [OpenAI](), [computer-use]() Tags: [agentic](), [agents](), [ai](), [benchmarks](), [chain-of-thought](), [context-engineering](), [cost](), [explainer](), [multi-agent](), [multi-agents](), [openai](), [tokens](), [tools]() ### AI overview This explainer reviews major AI-agent developments from January through July 2025, focusing on K2 and the International Math Olympiad gold result. It argues that K2's strong agentic performance without a long chain-of-thought trace raises questions about whether extended thinking is necessary for effective agents, while noting that shorter reasoning can reduce token costs. ### Source excerpt the best way to emphasize the importance of this week's developments is to go all the way back to January and see how we got here. ## Launch Week II Day 5: Chat Analytics DevFeed: [Launch Week II Day 5: Chat Analytics]() Original publisher: [Read original article]() Author: Joshua Kim Published: 2024-01-11T00:00:00Z Content type: release Language: en Sources: [Mintlify Blog]() Topics: [dashboards](), [Chat Bot](), [Documentation](), [ChatGPT]() Tags: [announcements](), [article](), [context-engineering](), [engineering](), [launch-week](), [releases]() ### AI overview Mintlify introduced Chat Analytics to its dashboard during Launch Week II. The feature lets teams segment chat history by date, manage AI Chat usage, examine recurring user queries, and review chatbot responses to identify documentation gaps and improve structure. ### Source excerpt Our final day of Launch Week 2 brings Chat Analytics to the Mintlify dashboard 🤖.