# OpenAI Authors - Building an AI-Native Engineering Team (Highlights)  ## Metadata **Review**:: [readwise.io](https://readwise.io/bookreview/56707946) **Source**:: #from/readwise #from/reader **Zettel**:: #zettel/fleeting **Status**:: #x **Authors**:: [[OpenAI Authors]] **Full Title**:: Building an AI-Native Engineering Team **Category**:: #articles #readwise/articles **Category Icon**:: 📰 **URL**:: [readwise-assets.s3.amazonaws.com](https://readwise-assets.s3.amazonaws.com/media/wisereads/articles/building-an-ai-native-engineer/1040.pdf) **Host**:: [[readwise-assets.s3.amazonaws.com]] **Highlighted**:: [[2025-12-07]] **Created**:: [[2025-12-13]] ## Highlights - But with coding agents like Codex, engineers can now spend more time on complex and novel challenges, focusing on design, architecture, and system-level reasoning rather than debugging or rote implementation. ([View Highlight](https://read.readwise.io/read/01kbwee7a54ebt8h8k0byprmb5)) ^964423789 - For example, teams may build workflows that connect coding agents to their issue-tracking systems to read a feature specification, cross-reference it against the codebase, and then flag ambiguities, break the work into subcomponents, or estimate difficulty. ([View Highlight](https://read.readwise.io/read/01kbweh54rwe7pnc4y7ancyd2b)) ^964423941 - Coding agents can also instantly trace code paths to show which services are involved in a feature — work that previously required hours or days of manual digging through a large codebase. ([View Highlight](https://read.readwise.io/read/01kbwehfht8569jtbwsqwbp169)) ^964423949 - AI agents can take the first pass at feasibility and architectural analysis. They read a specification, map it to the codebase, identify dependencies, and surface ambiguities or edge cases that need clarification. ([View Highlight](https://read.readwise.io/read/01kbwejknh5amwn6brv6937555)) ^964423986 - This makes it possible to iterate on multiple prototypes in hours instead of days, and to prototype in high fidelity early, giving teams a clearer basis for decision-making and enabling customer testing far sooner in the process. ([View Highlight](https://read.readwise.io/read/01kbwep607ave1057tannbke81)) ^964424196 - Select a product that has a model specifically trained on code review. We've found that generalized models often nitpick and provide a low signal to noise ratio. ([View Highlight](https://read.readwise.io/read/01kbwfjt0a22st350x4jb7779p)) ^964426154 - With AI coding tools, you can provide access to your logging tools via MCP servers in addition to the context of your codebase. ([View Highlight](https://read.readwise.io/read/01kbwfsndvzfktx5ktsaa6nsxr)) ^964426563