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Learn to achieve a 95% task success rate with AI coding agents through a systematic approach that transforms chaotic AI assistance into productive automation. Discover how to evolve from struggling with "naked" prompts and repository chaos to building a streamlined workflow where tasks become markdown files that AI agents can execute effectively. Explore the progression from 50% to 95% success rates by adding strategic context files, and see a live demonstration of the Backlog.md CLI tool that converts simple sentences into parallelizable development tasks. Compare the strengths and weaknesses of Claude, Codex, and Jules AI agents, and learn when to deploy each for planning, implementation, or review phases. Master a phone-only workflow with live merge capabilities that enables feature development from anywhere. Gain practical takeaways including a drop-in folder structure for safe LLM code contributions, a repeatable automation loop for feature development, and a decision-making checklist for optimal agent selection that adapts as AI tools continue evolving.
