Giving AI Coding Agents a Memory That Argues Back
I'm writing a series called "Becoming a Super Engineer": a software engineer since 2013, learning to hold software, data and AI under the same belt, and writing it down as it happens. Chapter 1 ended with agents that only stopped repeating mistakes once a check failed on them. This chapter is about the other half of that problem: what agents remember. It's about an open-source project of mine, a…
A knowledge base, called knowledge-database, helps AI coding agents remember and avoid repetitive mistakes. The project teaches agents to read a knowledge base before making changes and write back to it after completing a task. This approach helped an agent fix a money-rounding bug consistently, while other agents came up with multiple correct solutions.
The knowledge base uses markdown entries with evidence citing real lines of code. It enforces rules, deletes outdated or poorly written entries, and applies checks using a Python CLI, pre-commit hook, CI, and a post-session hook. These measures aim to maintain consistency and avoid drift in the codebase.
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