Govern AI agents before they act.
DashClaw is the governance layer for AI agents that touch real systems. It sits between agents and the world, evaluates policy on every risky action, routes human approval where it is required, records verifiable evidence, and tracks terminal outcomes so a retried agent never silently double-executes.
Plugs into the agents you already run: Claude Code, Codex, Hermes Agent, OpenClaw, Claude Desktop, and Claude Managed Agents. Framework integrations for LangChain, CrewAI, AutoGen, LangGraph, and OpenAI Agents SDK. Any other runtime over MCP, the Node/Python SDK, or direct REST.
The loop, end to end: intent → guard → approve → record. Rendered with Remotion from media/remotion/.
**Mission Control.** Fleet posture, the intervention queue, and a live ledger of governed events on one calm screen. Repeated signal occurrences collapse into one row; dismissing it clears them all.
**Spend and posture, measured.** Analytics prices every action and breaks enforcement down by agent and type. Governance posture is one gaming-resistant score: a policy only counts when replaying real traffic proves it fires, and drafting a policy never raises the number.
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/analytics · cost, volume, enforcement |
/posture · proven coverage, not vibes |