Multi-machine access for AI agents and developers

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

Problem

Developers and AI agents need unified access to files, sessions, logs and control across multiple machines (Mac, Linux, Windows) without managing separate tools or manual SSH connections. Managing agent workflows across distributed infrastructure is complex and time-consuming.

Solution concept

A unified application that provides integrated access to agent sessions, desktops, files, logs and GitHub integration across all machines from one interface, with an MCP server for AI agent integration. Built as a self-hosted solution to avoid vendor lock-in and support local control.

Who has it

Software developers, AI engineers, and teams running AI agents that operate across multiple machines or require remote machine access.

How they handle it today

Today, developers and AI agents access remote machines through fragmented tools: SSH for command-line access, separate remote desktop solutions, manual log aggregation, GitHub API calls, and bespoke scripting to coordinate across systems. Managing agent sessions and files across machines requires context-switching between tools.

Why now

AI agents are becoming central to developer workflows and require reliable access to distributed compute resources. MCP (Model Context Protocol) standardization enables tighter AI integration. Increasing use of heterogeneous machine fleets (Mac for development, Linux for servers, Windows for specialized tools) creates demand for unified access.

How it could charge

Subscription pricing for commercial teams or enterprises using the hosted cloud version; open-source self-hosted model with optional support contracts or premium features like advanced log analytics, compliance features, or priority support for on-premise deployments.