Use Cases — Canyon Road

Start with the environments where AI execution is already happening: endpoints, CI, internal automation, and sensitive systems. Apply least privilege at runtime, and keep full audit trails.
Copilots and desktop tools running with employee credentials.
Headless agents executing in automation pipelines without UI.
Fleet-wide policy, approvals, SIEM, and kill switch.
Desktop AI tools can read files, access browser sessions, and connect to arbitrary services using user credentials. Supervised workflows still produce unsafe execution, especially when copilots automate actions.
Unsupervised agents execute fast with no UI prompts. They fetch dependencies, run commands, and make network calls. A single unsafe step can become automated and repeated.
Teams are experimenting with agents that run terraform, kubectl, database actions, and incident tasks. These agents are powerful by default and operate in high-risk contexts.
AI tools and agents are increasingly pointed at codebases, tickets, docs, and data stores. Sensitive paths, tokens, and secrets are frequently within reach.
When something goes wrong, you need a fast, centralized response. Without a control plane, teams scramble to change configs or uninstall tools.
If your AI runs on laptops/desktops, start with Beacon.
If your AI runs headless in CI/containers, start with AgentSH.
If you need centralized policy, approvals, SIEM, and kill switch, add Watchtower (and scale across both environments).
We'll help you prioritize the right starting point.