The best AI coding workflow for solo developers is a short loop from product outcome to verified release: define one user result, inspect a plan, implement in isolation, run checks, review the diff, and deploy with rollback. It preserves speed without asking one person to hold every detail in memory.
Keep a small prioritized backlog and write acceptance criteria before coding. Ask the agent to inspect the repository and identify affected contracts, risks, and unknowns. Resolve decisions that would change architecture, then let it work in a branch or worktree. Require tests, types, linting, builds, and a completion report. Review behavior before style, with extra attention to authentication, money, data loss, public interfaces, and migrations. Merge small units and keep unfinished work behind a flag. Record deployment steps and an abort condition.
Use parallel agents only when tasks do not compete for the same files or decisions. One agent can implement while another investigates or tests, but shared interfaces need one owner. Measure accepted result time and repair effort, not generated output.
Solo does not mean unsupervised. Automate mechanical checks and reserve your attention for product and risk decisions. A repeatable loop lets you move quickly today and understand the system tomorrow. Keep it simple enough that you can pause, recover, and ship without relying on hidden chat history.
