Local coding agents are getting good enough that the bottleneck is no longer always the model.
The bottleneck is the boring operating surface around the agent.
When I run local agents, I want answers to simple questions:
- what agents are installed?
- which provider and model is each one configured to use?
- what jobs are currently running?
- where are the logs?
- what tools can this agent call?
- what failed last time?
- how do I stop, resume, or repair a run?
Most agent frameworks focus on building the agent. That is useful. But once you have more than one workflow, you start needing the same things every other local service needs: state, visibility, controls, and recovery.
That is the reason we are building Armorer.
Armorer is an experimental local control plane for AI agents. The goal is not to replace Claude Code, Codex, local LLM workflows, MCP servers, or whatever agent stack you already like. The goal is to give those workflows a local operations layer.
Repo: https://github.com/ArmorerLabs/Armorer
The mental model is simple: agents should feel less like loose scripts and more like supervised jobs.
A good local agent setup should make it easy to see:
- installed agents
- running jobs
- configuration state
- provider setup
- logs and recent output
- approvals and action history
- recovery paths after failure
If you are experimenting with local or self-hosted agents, I would love feedback. Stars help, but the more useful thing is hearing where your current agent setup still feels messy.
