DashClaw — Governance for AI Agent Fleets

Open-source AI governance runtime

Intercept agent actions before they reach production.

DashClaw is the policy firewall for AI agents.

DashClaw governs the moment agent intent becomes real-world action. Enforce policies, require human approval, and record verifiable evidence in one runtime.

Works with OpenAI, Claude, CrewAI, LangChain, AutoGen, OpenClaw, or any custom agent.

MIT Licensed. Self-host in seconds.

Decision Interception Demo

DashClaw decision interception in action

The interception layer for AI agents

DashClaw governs the moment where agent intent becomes real-world action.

This interception is where trust is created.

Agents retain autonomy.

Organizations retain control.

DashClaw sits between agents and the systems they control

DashClaw intercepts actions before they reach real-world systems.

Autonomous Actor

AI Agent

OpenAI · Claude · CrewAI · OpenClaw

DashClaw Runtime

Policy Engine

Approval Routing

Evidence Ledger

Real-world Targets

External Systems

GitHub · APIs · Databases · Infrastructure

AI agents introduce a new runtime problem

Deterministic code paths
Predictable outputs
Traceable call stacks
Debuggers work

Actions generated from goals
Non-deterministic outputs
No call stack to trace
Debuggers are not enough

Developers need governance over agent decisions.

Not just logs. Not just traces. A runtime that governs the decision itself.

The Decision Runtime

DashClaw is built around five primitives that form a decision runtime for autonomous systems.

Sync local data to Neon dashboard

Actor: moltfire

Confidence: 50%

Intent

Agents declare what they want to do.

Guard Policyactive

Block if risk > 80

Risk >= 80 → block

gp_0423d9749de847b8be38ff3a

Guard

Evaluate policies before agents act.

Human Approvalpending

REVIEW: data alerting

Agent: api-monitor

Approval

Pause risky decisions for human review.

Executionsuccess

ACTION SUCCESSFUL

Synced 80 rows + 6 calendar events

Duration: 6.25s

Action

The governed decision is executed.

Decision Proofverified

Cryptographically Signed

act_1386c4ee-2529-4c79-9455

Evidence

A signed replay is recorded for audit.

Governance logic belongs in the runtime, not hardcoded in your agents.

Works with your agent stack

DashClaw is the governance layer for existing agent frameworks.

One SDK. Full decision infrastructure.

Get governance in 60 seconds

Zero-dependency Node.js and Python clients. Adding governance requires only a small wrapper around risky actions.

View full SDK docs

SDK Example

// 1. Initialize DashClaw

const claw = new DashClaw()

// 2. Intercept before you act

const { decision } = await claw.guard({

actionType: 'deploy',

riskScore: 85

})

// 3. Follow the decision

if (decision === 'allowed')

{

// execute real-world action

}

What developers use DashClaw for

Practical scenarios where decision governance creates trust.

Prevent risky deployments

Intercept deploy commands from agents and require approval when risk thresholds are exceeded.

const decision = await claw.guard(

{

actionType: "deploy",

environment: "production",

riskScore: 92

}

)

• Request human approval
• Pause execution
• Record evidence for audit

Control autonomous API usage

Agents interacting with third-party APIs can be governed with policies.

await claw.guard(

{

actionType: "external_api_call",

provider: "stripe",

amount: 2000

}

)

• Limit spending thresholds
• Block dangerous actions
• Trigger approval workflows

Detect agent reasoning drift

Track assumptions agents rely on and detect when they become invalid. DashClaw records agent assumptions and decision context.

Assumptions DivergenceDRIFT DETECTED

Logic Baselinev1.2.0

Current ContextUnverified state

When assumptions diverge from reality, the system flags drift immediately.

Produce audit trails

Every governed action generates structured evidence records ready for compliance and review.

{
  "agent": "deployment-bot",
  "action": "deploy",
  "riskScore": 85,
  "policy": "production_guard",
  "approval": "granted"
}

• Compliance reporting
• Debugging agent failures
• Governance review

When governance becomes operations

Once decisions are governed, DashClaw provides the operational visibility required to run agent fleets at scale.

Mission Control

Real-time control tower for fleet posture and active interventions.

Decision Replay

Visual causal chains that explain exactly why an agent chose an action.

Policy Engine

Semantic guardrails that evolve with your organization without code changes.

Risk Signals

Automated detection of autonomy spikes, drift, and failure loops.

CLI Approval Channel

Approve or deny agent actions from the terminal. Works with Claude Code, Codex, and any terminal-first workflow.

Claude Code Hooks

Govern Claude Code tool calls via lifecycle hooks. No SDK instrumentation required.

Every governed decision produces audit-ready evidence.

SOC 2ISO 27001GDPRNIST AI RMF

Compliance Engine

Control-level gap analysis with remediation priorities.

Policy Testing

Run tests against all active guard policies.

Audit Evidence

Generate audit-ready evidence from live behavior.

Detect when agent autonomy goes wrong

Automatic detection of autonomy breaches and logic drift.

Autonomy Spike

Agent taking too many actions without human checkpoints

High Impact, Low Oversight

Critical actions without sufficient review

Repeated Failures

Same action type failing multiple times

Stale Loop

Open loops unresolved past their expected timeline

Assumption Drift

Assumptions becoming stale or contradicted by outcomes

Stale Assumption

Assumptions not validated within expected timeframe

Stale Running Action

Actions stuck in running state for over 4 hours

Integration Surfaces

DashClaw provides multiple integration surfaces: SDK instrumentation, a CLI approval channel, and Claude Code lifecycle hooks.

Policy & Guard

Define, test, and enforce guard policies. Centralize governance logic.

claw.guard({ type: "deploy", risk: 85 })

Decision Ledger

Immutable record of every agent intent and outcome. Prove accountability.

claw.createAction({ goal: "Database cleanup" })

Risk Monitoring

Automatic detection of risky behavior patterns across your agent fleet.

signal: autonomy_spike detected for agent-1

Operational visibility for agent fleets

Once decisions are governed, Mission Control provides the operational visibility required to run fleets at scale.

Live operational data:

live actions
policy decisions
pending approvals
integrity signals
agent health

Run agents with permissioned autonomy.

DashClaw lets agents move fast without giving up control. Intercept risky actions. Require approval when needed. Prove every decision afterward.