Lifestyles of the AI-Native — Nick Nisi & Zack Proser, WorkOS

This post contributes to agent memory and harness engineering by detailing a practical workflow for evaluating and shipping AI agents that work reliably in production. It emphasizes defining completion with checks the system must satisfy, moving beyond simple task completion to verifiable outcomes. The workshop demonstrates voice coding with Handy and Claude Code, session tracking with Fleet, and the use of Git worktrees for parallel changes. It introduces concepts like goals with measurable stopping conditions, loops for repeating tasks within boundaries, and verification gates with hooks (linting, type checks, tests). A key contribution is the adversarial review process using a second model before a pull request reaches a human. The post also covers turning working sessions into scheduled tasks for recurring reports, dependency updates, and meeting preparation, and shows how to generate weekly summaries of engineering work and token usage to preserve a record of automated tasks.