A practical look at building an agent workflow that makes you faster — without turning your codebase into AI slop.

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A developer shares a structured workflow for using AI coding agents without losing code ownership or quality. The approach uses agency-agents (a registry of specialized AI agent personalities) with defined roles like Frontend Developer, Code Reviewer, and Accessibility Auditor. Key practices include tight prompt scoping, a 7-step delivery loop from idea to execution, mandatory governance gates (lint, test, type-check before every commit), and reading every diff. The author identifies what agents handle well — boilerplate generation, consistency checks, test failure diagnosis — and what stays human-owned: API design decisions, accessibility judgment, architecture, and documentation voice.

How I Use Agents Without Losing Ownership of My Code

My setup — agency-agents as the baseline

The project — a real codebase, not a tutorial

Using agents without losing your code is like having a helper who actually listens — rare but powerful 😄🤖
Me: “Write this module”
Agent: does it
Me: still holds the commit rights 🙌😂