What question did this study set out to answer?

The study aims to enhance error learning in AI agents by establishing a reliable memory framework.

April 22, 2026Open Access

GEAR: Git-backed Error Analysis & Reflection

Puntos clave

The study aims to enhance error learning in AI agents by establishing a reliable memory framework.
Introduced a Production-Audit-Consumption model to streamline error management roles.
Implemented a decision factor for routing rules based on confidence and risk.
Developed YAML metadata schema for structured retrieval and version control.
The protocol ensures error corrections persist across sessions via git-backed storage.
Demonstrated effective role separation and review processes improve rule accuracy.
Reference implementation shows compatibility and ease of integration in existing systems.

Resumen

GEAR (Git-backed Error Analysis a Δ decision factor that routes each rule to automatic, semi-automatic, or mandatory human review based on confidence, risk level, and empirical evidence; and git-backed storage that makes reflection rules persistent, versioned, and atomically readable across sessions. The protocol defines five roles (Orchestrator, Resource Creator, Checker, Learner, Searcher), a five-state rule lifecycle, a structured YAML metadata schema with intent-driven retrieval, and nine conformance requirements. It is implementation-agnostic and requires no model fine-tuning. A reference implementation, Aristotle, demonstrates the protocol in a Claude Code / OpenCode environment. Source: https://github.com/alexwwang/aristotle

Leer artículo completoexternamente

Me gusta

Guardar

Ver artículo completo

Cite This Study

Alex Wang (Mon,) studied this question.

synapsesocial.com/papers/69e866416e0dea528ddea9ba https://doi.org/https://doi.org/10.5281/zenodo.19660779

Me gusta

Guardar

Ver artículo completo