Most AI agents today are stateless. They spin up, do some orchestration, maybe call a few APIs, then disappear. If you ask them to recall something they created yesterday, they usually can’t because nothing was ever persisted. That’s fine for toy demos, but if you want agents to actually be useful in day-to-day workflows, they need what every real operating system has: storage. Somewhere to write outputs, read inputs, update state, and remove what’s no longer needed. That’s what I mean by agentic storage. It’s a native file system for agents, designed with persistence, context, and automation in mind.
> How it works At its core, agentic storage exposes the four basic file operations: Create, Read, Update, Delete but reimagined for agents: * Create: Whenever an agent generates an artifact, a spreadsheet, report, dataset, chart - it gets written into storage as a file with metadata. This isn’t just raw bytes; we log who/what created it, and in what task context. * Read: Retrieval isn’t path-based. Instead of remembering /projects/2025/job_hunt/nyc_jobs.xlsx, you can say “the job hunting file” and the agent does a contextual lookup. Because the system parses files directly, agents can also “read” inside spreadsheets, images, or PDFs and act on the content. * Update: Agents can persistently modify files. Append rows, highlight values, merge in new content, or schedule recurring updates. All updates are versioned with provenance so you can track what changed and why. * Delete: Straightforward but important. Deletions are scoped to the agent’s storage (not your OS), require confirmation, and leave a trace. This makes experimentation safe.
> Beyond CRUD Because every artifact is tied back to the task that created or modified it, storage evolves into a memory graph. Files aren’t isolated blobs; they’re connected to the prompts, runs, and workflows that produced them. On top of this, there’s a built-in scheduler. You can say, “every Monday for the next 2 months, fetch new job listings and add them to my spreadsheet.” The system remembers, and it executes without requiring external orchestration tools.
> Why this matters Giving agents persistent storage changes the game. Instead of ephemeral runs that vanish after execution, you now have a foundation that compounds over time. It’s the same leap we made in computing decades ago: from temporary scripts that print to stdout, to real programs that write to disk, maintain state, and schedule tasks. Agentic storage is simply that leap for AI agents. It gives them a reliable substrate, a hard drive and an operating system, so they can build, recall, and extend their work like any other piece of software.
Feel free to try it out here -> Sellagen.com <- It’s free to use :)