What happens when your AI agent decides to delete a production database at 2 AM? Right now, most agent frameworks just... let it.
DashClaw is a new open-source project that addresses this gap by sitting between an AI agent and the tools it calls. Instead of letting agents execute actions directly, DashClaw intercepts each tool call, surfaces it for human review, and waits for approval before anything runs.
The problem it solves is real: as agents move from chatbot demos to production workflows touching actual infrastructure, the lack of visibility into their decision-making becomes a liability. An agent might call an API, trigger a deployment, or modify data based on incomplete context, and you would not know until after the damage is done.
DashClaw works as middleware. You wire it between your agent and its tool integrations, and every proposed action gets logged and held for inspection. Think of it as a pull request review process, but for agent behavior instead of code changes.
The project is early-stage and available on GitHub. It is not a polished enterprise product, but the concept fills an obvious hole in the current agent tooling landscape. Most agent frameworks (LangChain, CrewAI, AutoGen) focus on making agents more capable. Very few focus on making agents more auditable.
For teams running agents in production or planning to, this is the kind of guardrail worth evaluating now rather than after an incident.