Context engineering: QC for agent memory
Treat the filesystem and docs as the factory’s memory substrate: formats, scoping, and evolution matter as much as prompts.
This article is AI-assisted and co-authored by Xesca Alabart, co-founder of EasySpecs.
Context engineering is how you govern what the agent reads and writes. Models do not carry your org’s truth in weights; they need durable, inspectable state.
Filesystem as memory
McCarthy and Schillace both emphasize the filesystem: directories, Markdown indexes, scratch files, and ripgrep to rehydrate context. Formats models handle well—Markdown, JSON, YAML—mirror training and tooling.
Scoped loading
Large repos punish naive “read everything.” Scoped context loading (per directory, per package, per task) reduces token waste and confusion—patterns like the three-repo split (library / setup / resource catalog) exist to keep layers composable.
Factory bottleneck
Factories stall when seeds and context are incomplete or stale. That is why upstream seed manufacturing (EasySpecs / Gluecharm) sits beside the coding factory—not as a cute metaphor but as a throughput problem.
Related
References
- Sam Schillace — Filesystem primacy, compounding teams
- Dev.to / Sarony11 — Three-repo agent infrastructure