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This article outlines a method for creating high-accuracy agentic systems by focusing on the job to be done (JTBD). It emphasizes designing task-oriented tools, ensuring verifiable outcomes, and using feedback for continuous improvement. The process aims to transform execution attempts into reliable, learning systems.
This article discusses the challenges and methods of verifying code generated by AI systems. It highlights the importance of precision in automated code reviews, the need for repo-wide tools, and how real-world deployment has shown positive outcomes in catching bugs and improving code quality.