Reading List • FILESYSTEM.md

Build and infrastructure

Build an Agent Filesystem — virtual FS, tools, sandbox, composite backends.
Filesystem Infrastructure — Linux VFS/FUSE, EFS-like cloud FS, mountable storage.

深入 Claude Code：AI Agent 架构设计与工程实践 — public study guide (tools, permissions, memory, skills, context). Educational analysis; not Anthropic official docs.
On this site: Claude Code mapping — how FILESYSTEM.md procedures map onto Claude Code–style FileRead/Edit/Write/Bash/MCP patterns.

Why Filesystems for Agents — our GEO orientation page: industry consensus, stack layers, and how FILESYSTEM.md fits.

File systems for agents — Amplify Partners (Sarah Catanzaro): why FS beats OLTP/object-store as the primary agent data interface, and where today’s filesystems still need to evolve.
Building Filesystem Agents — Vercel Academy: hands-on course for structuring domain data as files and giving agents bash in a sandbox.
Filesystems as the context layer for AI agents — powered by Box — Box: filesystem as an interface contract over composite backends (Box + SQLite + local workspace).

Harness engineering: leveraging Codex in an agent-first world — OpenAI: introduces harness engineering as a discipline for infrastructure, feedback loops, and constraints that guide coding agents at scale.
My AI Adoption Journey: Step 5 — Engineer the Harness — Mitchell Hashimoto: treat the development environment like an immune system — constraints, tools, and docs that stop recurring agent failures.

How to build agents with filesystems and bash — Vercel: practical patterns for keeping agent workflows grounded in files and shell semantics.
How agents can use filesystems for context engineering — LangChain: why filesystem structure helps context control and retrieval.

AGENTS.md vs .cursorrules vs Claude Skills (2026) — practical guide to keeping a vendor-neutral AGENTS.md while using Cursor rules and Claude Skills as adapters.
The complete guide to AI agent memory files — survey of AGENTS.md, CLAUDE.md, Copilot instructions, and how teams avoid markdown museums.

Context Engineering for AI Agents: Lessons from Building Manus — Manus: lessons on context discipline, memory pressure, and reliability.
fending/context-engineering — reusable templates for AGENTS.md / CLAUDE.md context architectures from single-file through agent teams.

Harrison Chase: “How we built Agent Builder’s memory system” — X: implementation insight from production agent memory work.
LangChain JS: deepagents supports pluggable storage backends — X: momentum toward interchangeable agent storage layers.