From One Failure to Project Memory: Make the Pipeline Stronger Over Time

Short summary

AI projects fail repeatedly because lessons remain in chat rather than project structure. Classify each failure and route it appropriately—tests for code behavior, scripts for execution, issue templates for task entry, AGENTS.md for project rules, skills for workflows, or central knowledge for stable patterns. This transforms individual experiences into durable pipeline improvements.

• •Classify AI failures by root cause: unclear requirements, wrong context, missing tests, excessive permissions, unsafe tool calls, etc.
• •Route each lesson to the appropriate structural layer—tests, scripts, templates, rules files, skills, or central knowledge base
• •Keep project-specific details local; elevate only stable cross-project patterns to shared knowledge

Generated with AI, which can make mistakes.