Open-Source Framework LEO Uses Rules and Roles to Fix AI Coding Agents' Memory Gap

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A developer building autonomous AI coding agents on real SaaS platforms repeatedly observed the same class of production bugs resurfacing weeks apart in different files, despite syntactically correct, test-passing code. The core issue was identified not as poor code generation but as architectural amnesia: fixes applied in one session never generalized into standing rules carried forward to future sessions or agents. To address this, the developer built a structured system comprising 41 codified laws, 22 specialist roles, and a file-based memory layer that logs every rule and its rationale persistently. This approach prevented recurring defects — such as non-atomic rate limiters, zombie heartbeat workers, and orphaned database locks — from quietly reappearing on a cyclical schedule. The entire framework is being released as an open-source project called LEO, framing AI agent reliability as an engineering-process problem rather than a prompting or model-quality problem.

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