Everyone is comparing agent frameworks: LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Claude Code, Codex, MCP routers, custom harnesses.
That comparison matters, but it misses the layer that starts hurting once the demo works.
The framework creates the workflow. It does not automatically answer:
- what is installed and running locally?
- which tools, MCP servers, skills, and providers are mounted?
- what repo, files, or workspace state were in scope?
- what did the agent change?
- which actions created side effects?
- which actions required approval, warning, redaction, block, or review?
- what evidence came from tests, evals, traces, or browser checks?
- what can be retried, resumed, rolled back, or cleaned up safely?
That is the layer we are building Armorer for: a local control plane around agents.
The split we are converging on:
Armorer: sessions, jobs, tool inventory, config, approvals, run records, and recovery
Armorer Guard: fast runtime decisions on proposed tool calls and model/tool-output transitions
The goal is not to replace agent frameworks. It is to make agents operable once they exist.
The artifact I keep coming back to is a run receipt.
A useful agent run receipt should capture:
- the agent/app, version, and config
- the mounted tools, MCP servers, skills, and providers
- the workspace/repo/files in scope
- checkpoints before and after the run
- tool calls and side effects
- approval and review decisions
- test/eval/check evidence
- retry, resume, rollback, and cleanup state
Without this, debugging agent runs turns into transcript archaeology.
With it, operating agents starts to feel more like operating software again.
Repos:
- Armorer:
Questions I would love feedback on:
- What is the minimum useful run receipt for an agent session?
- Which approval events should become first-class history?
- Where should MCP/tool metadata stop and runtime policy begin?
- What recovery action do you wish your agent harness exposed after a bad run?
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