Provisioned and wired for production, not a throwaway container.
- Isolated VM per customer
- Docker, Caddy, firewall, ports set up for you
- Custom domains with automatic HTTPS
- Larger VMs available on request
Infrastructure for AI agents
Give your AI agent a real dev environment
Not a browser sandbox and not an agent loose on a raw VPS. A dedicated Linux VM with governed pipelines, durable project memory, and a full audit trail.
Real infrastructure for AI agents to build, test, deploy, recover, and keep context.
Isolated VM per customer
Any MCP model, in parallel
Governed deploy & rollback
Durable context
What "real infrastructure" actually means
Five things an agent needs to do serious work, and what YokeDev gives it for each. Bring any MCP-capable model (Claude, ChatGPT, Codex, and others); the environment is the same.
The platform checks the agent's work so regressions surface early.
- Smart testing driven by knowledge-map dependents
- Nightly quality reports
- AI audits & reviews of changes
- Doc & header freshness checks
A predictable path from code to live, with safety on the way out.
- Build & deploy containers with staged checks
- Verified releases to production
- Doc leak gate on public and customer-facing surfaces
An agent will get things wrong; the environment makes that safe.
- Git history + one-step rollback
- Per-session worktrees, no collisions
- Session and client isolation for parallel agents
Durable, structured context instead of forgetting between sessions.
- AI Context headers on files
- Knowledge-map scanner maps files and dependents
- File read / write nudges guide edits
- PM dashboard: goals, DoDs, blockers, progress
Up and running in minutes
From idea to a governed deployment flow, without stitching together scripts, terminals, and ad-hoc logs.
Provision a dedicated Linux VM with baseline runtime and platform wiring.
Claude, ChatGPT, Codex, and other MCP-capable models can work at the same time.
Work is tied to outcomes, checkpoints, and a clear definition of done.
Checks, reviews, and a governed deploy flow keep releases predictable.
Run it your way
Public by default, with private and self-hosted options as your needs grow.
Lock a project to a private Tailscale network so the VM and its tooling are reachable only inside your tailnet, not the public internet.
Connect an on-prem or self-hosted model to a YokeDev environment, so sensitive code stays inside your own boundary.
Start small and scale up. Larger VMs are available when a project outgrows its tier.
No lock-in to one vendor. Use the model you prefer, or several at once, against the same governed environment.
Why not just point an agent at a raw CLI or VPS?
| Question | Raw CLI / VPS | YokeDev |
|---|---|---|
| Can the AI write code and run commands? | Yes | Yes |
| Is work tied to goals and a DoD? | Manual discipline | Built-in workflow |
| Does it keep durable context between sessions? | No, it forgets | Knowledge map + AI headers + memory |
| Can it recover from a bad change? | Hope you have a backup | Git history + one-step rollback |
| Can multiple models work safely in parallel? | High collision risk | Session-isolated worktrees |
| Is testing scoped to what changed? | Run everything, or guess | Knowledge-map dependent testing |
| Are internal docs kept out of public surfaces? | On you to remember | Doc leak gate |
| Can you audit every action? | Fragmented shell history | Full tool-call trace by goal and session |
Same raw capability as an agent on a box, plus the context, governance, recovery, and audit trail that make it safe for real teams and real production work.
Give your agent somewhere real to work
Bring your own MCP-capable AI, set a goal, and let it build, test, deploy, and recover on a dedicated VM with guardrails.