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The AI Coding Team Working Agreement

Every team I've worked with has unwritten rules — who to ask before touching auth, which decisions are settled, what "in progress" actually means. They used to travel by osmosis. Once everyone on the team is coding with an agent, osmosis stops working, because half the conversation now happens in someone's private session. This is the one-page agreement we ended up writing down, and why each clause is in it. When every developer on a team codes with an AI agent, one of the first things to break down can be the unwritten rules. Small, shared assumptions that once traveled through everyday conversation ("ask before you touch auth," "we decided on v2 last week") may not reach a teammate's private session — let alone the agent working in it. The established fix for that is a working agreement : a short, explicit set of norms a team writes for itself. This one is designed for AI-assisted teams — a page you can copy, adapt, and keep somewhere every teammate can access and every agent is configured to read. What a working agreement is — and isn't A working agreement is a team norm, written by the team, kept short, and revised as you learn. It is not a tool, and not a policy handed down from above. It doesn't enforce anything — it aligns behavior. That's precisely why it survives across whatever mix of editors and agents your team actually uses: it lives at the human layer, above any one tool. If you've run agile ceremonies, you've seen these before. What's new is that agents now perform part of the work, so a few assumptions that people may have absorbed implicitly need to be written down. Accountability still stays with people. The template Copy this, cut what doesn't fit, and fill in the blanks. Keep it to a page. # Our AI Coding Working Agreement (v1) ## 1. Shared decisions - Decisions that affect others live in: ______ (a shared memory, a decisions doc). - Before a decision affects someone else's work, we record who decided it, what changed, and why. ## 2. Declaring wo

2026-08-11 原文 →
AI 资讯

Podcast: Culture & Methods Trends 2026: The Human Side of AI Engineering

This is the Engineering Culture Trends Report for 2026. Featuring a panel of QCon speakers and InfoQ contributors, they discussed AI adoption maturity and risk, the transformation of engineering team structures and roles, and the human dimensions of software development that must not be lost in 2026. By Ben Linders, Rafiq Gemmail, Craig Smith, Vanessa Formicola, Shawna Martell, Phillip Mortimer, Yinka Omole

2026-08-07 原文 →