This article argues that to get useful work from AI coding agents, teams must build a structured environment—context, tools, permissions, tests, and review loops—that guides stateless models and enforces deterministic feedback. It covers seven principles, from minimal and tested context to sandboxed credentials and self-validating work, so agents can onboard per task, prove their output, and operate safely at scale.

• Agents are stateless and need minimal, tested context (small rules/skills pointing to code) instead of a sprawling AGENTS.md
• Environments must be fully deterministic—locked dependencies, scripts, seed data, auth—so agents either work or fail clearly, no "I think this works" commits
• Agents should self-validate via tools like Playwright, Chrome DevTools MCP, and screenshot/accessibility checks, since burning extra tokens on closed-loop checks is cheaper than wasting human reviewer time
• Safety and access control need to be system design, not prompt hygiene—sandboxed credentials, scoped file/network access, approval gates, and audit trails so nontechnical users can't accidentally cause damage