Deployment and lifecycle management for AI agents

AIThis description was written by a model from public signals. The facts behind it are listed under Evidence.

Problem

Building and deploying AI agents that work with filesystems requires stitching together multiple pieces: packaging, sandboxing, messaging, state persistence, and file management. Teams lack a unified platform to handle the full lifecycle of agents that read and write files.

Solution concept

A platform that treats agents and filesystems as first-class, decoupled primitives: package agent code with a config file and Dockerfile, deploy via CLI, and rely on the platform to handle sandboxing, messaging protocols, state persistence, storage, and file transfer. Filesystem workspaces persist across turns, can be shared between agents, and support direct user upload/download without backend proxying.

Who has it

Teams building coding agents, research agents, document processing agents, and internal tools; developers deploying agents that need filesystem access and sandboxed execution.

How they handle it today

Currently, teams must manually integrate multiple tools and services: package agent code themselves, set up sandbox environments, implement message streaming, manage state across conversations, and handle file transfer to and from the agent workspace.

Why now

The Claude Agent SDK and broader agentic AI systems are maturing, with teams building production agents that need reliable infrastructure. Developers are seeking platforms that abstract infrastructure complexity, following patterns established by Vercel and Replicate.

How it could charge

Subscription or usage-based pricing on deployment compute, storage, and API calls, similar to container deployment platforms or serverless functions. Likely tiered for development and production use.