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Developer engineers repo to fix AI coding agent errors

By PulseAugur Editorial · [1 sources] ·

A developer grew frustrated with their AI coding agent's persistent errors, such as editing incorrect files or ignoring established conventions. To address this, they shifted focus from prompt engineering to directly modifying the project's repository structure. This involved implementing features like an AGENTS.md file, drift checks, CI feedback loops, and dedicated memory stores for decisions and failures, all built with standard Python and designed to be tool-agnostic. AI

IMPACT This approach could offer a more robust method for improving AI agent reliability by focusing on structured feedback and memory.

RANK_REASON Developer describes a method for improving AI agent performance by engineering the repository structure, rather than solely relying on prompt adjustments.

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COVERAGE [1]

  1. Mastodon — mastodon.social TIER_1 English(EN) · baskduf ·

    I got tired of my AI coding agent repeating the same mistakes — editing the wrong files, re-trying approaches I'd already rejected, ignoring conventions. So ins

    I got tired of my AI coding agent repeating the same mistakes — editing the wrong files, re-trying approaches I'd already rejected, ignoring conventions. So instead of tweaking prompts, I started engineering the repo itself: an AGENTS.md, drift checks, CI feedback loops, and a do…

    LINKS github.com/…/harness-starter-kit

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