Agentic FileSystem gives agents one reusable place to read and write durable context.

Agent memory is fragmented

Each harness stores instructions, traces, and context in its own format, so useful knowledge is trapped in one tool.

Context is hard to move

Teams need a plain, portable structure that lets agents share traces, context, sources, and decisions without a platform migration.

Knowledge needs provenance

External docs, links, copied references, source snapshots, and generated summaries need an obvious home with source history.

Filesystem first

AFS stays useful through normal files, folders, Markdown, grep, pull requests, and backups.

Portable across harnesses

Codex, Claude Code, Cursor, Notion Agents, OpenClaw, Hermes, and local tools can share the same durable context.

Trace history is separate from truth

Timestamped logs, lessons, fixes, plans, results, and reflections preserve work-in-time evidence; live specs, runbooks, and knowledge hold current truth.

Human intent stays at the root

Root files such as AGENTS.md, USER.md, VISION.md, LOOPS.md, TASTE.md, and GAPS.md keep direction and missing understanding readable before agents act.

Decides how the AFS should be managed.

Defines the north star, current direction, non-goals, strategic constraints, and what agents should protect over time.

Defines recurring agent loops, triggers, cadence, review gates, stop conditions, and improvement cycles.

Captures high-quality and personalized judgment: what good looks like, preferred examples, anti-patterns, and subjective standards.

Tracks gaps in human or agent understanding so unresolved questions stay visible until agents can resolve them into durable knowledge.

Defines how memory should be captured, promoted, and maintained.

Defines decision frameworks and repeatable operating patterns.

Defines general operating rules for AI agents working in the codebase, including conventions, workflows, architecture boundaries, testing expectations, and human collaboration.

Defines how planning should be done, including structure, milestones, execution order, priority rules, progress tracking, and plan updates.

Defines how specs should be written, including required sections, acceptance criteria, edge cases, constraints, implementation notes, and validation rules.

Gives personality, attitude, and behavioral style to AI agents, including tone, decision posture, collaboration principles, and risk tolerance.

Stores general context about the user, including preferences, background, goals, working constraints, communication expectations, and long-term priorities.

Defines frontend design principles, component rules, layout patterns, typography, color usage, spacing, interaction states, and UX standards.

Defines what the product is, who it serves, core value proposition, use cases, principles, feature boundaries, and strategic direction.

Defines company-level context, including mission, positioning, operating principles, team structure, culture, business model, and priorities.

Defines the values on which the company, user, product, and codebase should be based.

Defines target segments, buyer personas, pains, desired outcomes, buying triggers, objections, qualification criteria, and non-fit customers.

Defines communication style, tone, vocabulary, messaging patterns, banned phrases, examples, and how to sound consistent across channels.

Documents user, customer, developer, or operational friction points, repeated complaints, blockers, inefficiencies, adoption barriers, and pain to reduce.

Captures expected future risks, opportunities, user behaviors, market shifts, technical bottlenecks, and product bets.

Tracks known failures, recurring bugs, fragile assumptions, broken flows, previous fixes, test gaps, and regression traps.

Defines recurring operational status, active priorities, blockers, recent progress, next actions, health checks, and reporting cadence.

Create the full shell directly at the folder root, including root Markdown files, knowledge/INDEX.md, raw tracking files, sources/INDEX.md, facts/, models/, and templates/.

Use a GitHub repository named agents-fs by default for personal, company, or cross-project AFS workspaces.

Install AFS under docs/ so agent memory, source provenance, and knowledge stay separated from app code.

Create a nested agents-fs/ folder when the current directory is already populated but is not an app repo or existing AFS root.

Agents should treat a request to implement the AFS URL as an install request and choose the safest placement automatically.

Notion Agents

Paste the Notion template into Custom Agent instructions and point it at the pages or databases that mirror AFS root files and folders.

Keep durable outputs in Notion pages named after AFS files, then periodically export or sync them into the repository.

Claude Code

Use CLAUDE.md to import AGENTS.md, install the use-afs skill, and add the hook example when you want validation around file writes.

Keep scoped Claude rules thin and let the root AFS files define persistent policy.

Codex

Use AGENTS.md plus the repo-scoped skills/use-afs skill so Codex can route context, sources, and validation consistently.

Add optional Codex hooks for validation before major write operations.

Cursor

Use AGENTS.md for shared rules and the Cursor .mdc template for editor-local routing to AFS root files and folders.

Keep Cursor-specific UI or codebase rules scoped, while durable facts remain in AFS.

OpenClaw

Use the OpenClaw template as a bridge skill that points the agent to AFS root files, sources/, logs/, and validation scripts.

Treat the bridge as adapter guidance unless the OpenClaw project adds a native AFS integration.

Hermes Agent

Use the generic AFS operating contract: read root intent first, write trace history into memory folders, and keep external material in sources/.

Attach the AGENTS.md and BRAIN.md templates to Hermes until a native adapter is available.

Gbrain by Garry Tan

AFS can expose GBRAIN.md and BRAIN.md as adapter surfaces so Gbrain-style knowledge can point back to portable files and sources.

QMD by Toby at Shopify

AFS can expose QMD.md as a query and decision bridge that maps QMD-style notes back to specs, plans, knowledge, and source provenance.

Plain agent harnesses

Any harness that can read files and write Markdown can use AFS without a proprietary database or hosted service.