File Systems Are the New Primitive for AI Agents

Most AI agent demos live inside a prompt window. Production agents need something more durable: memory, state, recovery, collaboration, and auditability. This talk argues that file systems are becoming a foundational primitive for agentic software. LLMs are already deeply trained on files, directories, command-line tools, codebases, Markdown, logs, and operating-system workflows. Instead of forcing agents through bespoke APIs for every task, developers can often build simpler, more reliable systems by exposing work through file-system-like interfaces the model already understands. We’ll walk through practical architecture patterns for file-backed agents: using files as long-term memory, organizing intermediate work, coordinating multi-step tasks, enabling human-in-the-loop review, synchronizing state, and preserving an auditable record of agent activity. The session includes a live demo of an agent operating over a cloud file system: reading task files, detecting human edits, updating related files, and showing an inspectable history of work. Attendees will leave with a concrete framework for deciding when to use files, when to use APIs or databases, and how to design agent workflows that are simpler, more inspectable, and easier to operate in production.