Agent demos are easy. Agent operations need receipts.

Armorer Labs is building a local control plane for AI agents, addressing operational gaps like installation, configuration, observability, and recovery. The open-source project includes Armorer for managing agent workflows and Armorer Guard for generating structured receipts of runtime decisions. The team argues that trust in production agent systems will come from inspectable records rather than model confidence.

I keep seeing the same pattern with AI agents: the demo works, the first workflow is exciting, and then the boring operational questions show up. What is installed? Which model/provider/config is this run using? What tool calls happened? Which actions needed approval? Can I replay the failure, resume the run, or prove what changed? That gap is what we are building around at Armorer Labs. Armorer is a local control plane for AI agents. The goal is to make local and self-hosted agent workflows feel less like scattered scripts and more like supervised jobs: installable, configurable, observable, stoppable, and recoverable. Repo: https://github.com/ArmorerLabs/Armorer https://github.com/ArmorerLabs/Armorer The framing I like is: not another agent framework, but the local operations layer around the agents you already want to run. Armorer Guard is the companion idea for runtime decisions. If an agent, workflow, MCP server, or tool gateway makes a decision, I want a structured receipt for it: Repo: https://github.com/ArmorerLabs/Armorer-Guard https://github.com/ArmorerLabs/Armorer-Guard I do not think production agent systems will be trusted because the model sounds confident. They will be trusted because they leave boring, inspectable records. If you are building or running agents locally, I would love feedback on both repos. Stars are obviously helpful, but the more useful thing is sharp criticism: what would make these tools worth installing in your own workflow?