Armorer v0.1.19: building the local ops layer for AI agents

Armorer v0.1.19, an experimental local control plane designed to manage the operational challenges of running AI agents, such as configuration drift and failed runs. Unlike an agent framework, Armorer functions as a local ops layer that helps install, configure, run, supervise, and recover agents. The project is still in development and prioritizes honest feedback from users already running local or self-hosted agent workflows.

We have been building Armorer as an experimental local control plane for AI agents. Getting one agent demo working is usually not the hard part. The harder part is everything right after that: provider configuration drift, Docker or Colima state, partial installs, failed runs, and figuring out what actually changed between attempts. So Armorer is not another agent framework. It is our attempt at a local ops layer for agents: install them, configure them, run them, supervise jobs, and recover when setup or runtime goes sideways. Repo: https://github.com/ArmorerLabs/Armorer It is still experimental, so we care a lot more about honest feedback from people already running local or self-hosted agent workflows than about pretending the product is finished.