For AI Teams
Sandboxes for AI agents
that don't touch production.
LLM agents need isolated, repeatable environments — not shared clusters where one bad run reaches a real customer. MicroStax gives AI teams disposable sandboxes with policy controls, audit trails, and snapshot replay.
Isolated sandbox per agent run
Each agent run gets its own environment. One agent crashing or going rogue can’t corrupt another run — easier to debug, safer to experiment.
Governance & policy enforcement
Set hard rules on what an agent can deploy, which services it can call, and which secrets it can read. Enforced by MicroStax — not by hoping the prompt holds.
Reproducible runs from blueprints
Every run starts from the same blueprint. Diff two runs to see exactly what the agent changed — code, data, config — instead of guessing.
Task-oriented MCP surface
Agents can work with environment tasks like create, inspect, diagnose, and share through the MicroStax MCP server.
Branch isolation for agent experiments
Run different agents against different branches of your application in parallel, each in a fully isolated environment. Compare outcomes without cross-contamination.
Scoped credentials — never shared
Each environment gets scoped credentials valid only for that run. Agents never see production secrets. Credentials expire on TTL or environment teardown.
Native MCP Support
Works with GitHub Copilot, Claude, and any MCP agent
MicroStax exposes a Model Context Protocol server so AI tooling can work with the same environment tasks and control-plane state as the CLI and dashboard.
Read the MCP Server docs →Also relevant