With local AI agents increasingly writing and executing code autonomously, giving them unrestricted access to your machine is becoming a massive security risk. This is one of the primary reasons that agentic flows have so many flavors of approval that may need to happen throughout an agents course of action, though others include review points and being able to keep the agent on track.
I have been very much enjoying my increased use of GitHub Cloud Agents in my work and play, which is rather powerful if you can setup your entire stack (more or less accurately) in a remote environment using VMs and containers. On the project that I currently work the most I have a copilot-setup-steps.yaml file or 53 lines leveraging my existing docker compose based development environment setup of 41 services that only takes 2 minutes to “install” (multi repo clones, and dependency installation), then allowing agent to run various different development configurations depending on the tasks at hand, using a mixture of the services (or not).
However today is the first day I’ll be taking a very brief look at Docker AI Sandboxes, to try and do more of this locally and or on machines nearby…
