The biggest shift in AI coding right now isn’t better prompting—it’s building better loops. Boris Cherny, one of the creators of Claude Code at Anthropic, has been advocating a different approach: stop manually prompting AI for every task and start designing systems that can plan, execute, verify, and improve on their own. Instead of: Prompt → Response → Fix → Repeat The workflow becomes: Goal → Execute → Verify → Fix → Repeat You define the objective, success criteria, tools, and stopping conditions once. The AI handles the iteration. This is where the real leverage starts. Modern AI coding tools can now: • Run autonomous verification loops using tests, linters, and code reviews • Launch parallel sub-agents to tackle different parts of a problem • Monitor repositories, PRs, and builds on a schedule • Coordinate complex workflows across multiple environments • Continue refining output until quality thresholds are met The result? One well-designed workflow can review codebases, manage migrations, generate PRs, and resolve issues while you’re focused on higher-level decisions. A few lessons stand out: Clear goals matter more than clever prompts. Verification is the secret weapon—always give AI ways to check its own work. Parallelism is a force multiplier. Reusable workflows compound over time. The bottleneck is increasingly system design, not prompt design. We’re moving from prompt engineering to workflow engineering. The developers who learn how to design autonomous, self-correcting systems will have a significant advantage over those still treating AI as a chatbot. The future isn’t asking AI better questions. It’s building systems that don’t need to keep asking you what to do next. submitted by /u/Annual_Judge_7272
Originally posted by u/Annual_Judge_7272 on r/ArtificialInteligence