DashClaw – intercept and audit AI agent decisions before they execute

Hi HN,

I’ve been experimenting with autonomous agents for the past year, and I kept running into the same uncomfortable problem:

Agents were making decisions I couldn’t fully see or justify.

They could call tools, trigger actions, and make assumptions based on incomplete context. Once systems start touching real infrastructure, that becomes pretty scary.

So I built DashClaw.

DashClaw sits between an agent and the tools it wants to use. Instead of executing actions directly, agents call DashClaw first.

DashClaw can:

• evaluate the decision • apply policy rules • require approval • log reasoning and assumptions • record the final outcome

The idea is to create a *decision trail* for agent systems so you can answer:

Why did the agent think this was okay? What information did it rely on? Who approved the action?

The project includes:

• Node and Python SDKs • a decision ledger for agent actions • policy guardrails before execution • a mission control dashboard for fleet activity • a self-hosted architecture

It’s completely open source and designed to be lightweight enough to run locally with agent frameworks.

I'm still figuring out what the right abstraction layer is for this kind of infrastructure, so I’d love feedback from people building agents.

Repo: https://github.com/ucsandman/DashClaw