How to Persist AI Agent Files Across Sessions
Keep AI agent files across sessions with durable storage, a clear accepted version, and a repeatable handoff. Includes a practical research-workspace example.
The Folderbase field guide
Make agent work
easier to continue.
Practical guides to persistent files, reviewable changes, and choosing storage for AI agents. Start with a recurring job and the files it needs.
ORDINARY FILES / AUTHORITATIVE
Wproduct-brief.docxdocument
Parchitecture.pdfsource
Xforecast.xlsxdataset
Ihero-final.pngasset
ENGINE-MANAGED LAYER.folderbase/
IDENTITYVERSIONSPOLICYPROVENANCEQUERIESRELATIONSHIPS
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How to Review AI Agent File Changes with Change Sets
Review AI agent file changes using a base version, staged proposal, text diffs, and binary checks. Handle stale work and verify the accepted result.
How to Choose Storage for AI Agents
Compare folders, Git, object storage, persistent volumes, AgentFS, checkpoints, and memory. Choose agent storage around the files your next run needs.
What Is a Folder Database?
A folder database keeps ordinary files authoritative while adding durable identity, relationships, versions, permissions, queries, and provenance.
What AI Agents Need From a File System
AI agents need more than read and write access: durable identity, scoped permissions, versions, provenance, conflict handling, and reviewable changes.
Folders vs. Vector Databases for AI Agent Memory
Folders preserve authoritative work; vector databases improve semantic recall. Learn why agent systems need both without confusing one for the other.
Live Folders: Sharing Work With Humans and AI Agents
A Live Folder combines local-first files, explicit recipient grants, immutable versions, and human-agent permission parity.
Why AI Agent Storage Should Be Local-First
Local-first agent storage keeps ordinary files fast and directly usable while adding cloud sync, sharing, recovery, and remote execution explicitly.