For AI Teams
The missing safety layer
for AI agents on your infrastructure.
LLMs and autonomous agents need isolated, governed, reproducible environments — not shared clusters where a bad run can affect production. MicroStax gives AI teams a cleaner environment model for sandboxing, repeatability, and controlled automation.
Isolated sandbox per agent run
Give agent runs isolated environments instead of shared mutable state so failures and experiments stay easier to reason about.
Governance & policy enforcement
Define what agents can deploy, which services they can reach, and which secrets they can access — enforced at the control plane level, not in the agent prompt.
Reproducible runs from blueprints
Start from Blueprint-defined environments so teams can repeat setup, share state intentionally, and inspect what actually changed between runs.
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