instrument-agent

name	instrument-agent
description	Integrate DashClaw SDK into any agent using the 4-step governance loop
license	MIT
metadata	{"author":"ucsandman","version":"1.0.0","category":"integration"}

Instrument Your Agent with DashClaw

Help developers add DashClaw governance to any AI agent. Walk through the 4-step governance loop with working code.

The 4-Step Governance Loop

Every governed decision follows this deterministic flow:

1. Guard  → "Can I do this?"           (POST /api/guard)
2. Record → "I am doing this."         (POST /api/actions)
3. Verify → "I believe this is true."  (POST /api/assumptions)
4. Outcome → "This was the result."    (PATCH /api/actions/:id)

Step 0: Install & Initialize

Node.js

npm install dashclaw

import { DashClaw } from 'dashclaw';

const claw = new DashClaw({
  baseUrl: process.env.DASHCLAW_BASE_URL,
  apiKey: process.env.DASHCLAW_API_KEY,
  agentId: 'my-agent'
});

Python

pip install dashclaw

from dashclaw import DashClaw

claw = DashClaw(
    base_url=os.environ["DASHCLAW_BASE_URL"],
    api_key=os.environ["DASHCLAW_API_KEY"],
    agent_id="my-agent"
)

Step 1: Guard — Check Policy Before Acting

const decision = await claw.guard({
  action_type: 'deploy',
  declared_goal: 'Deploy build #402 to production',
  risk_score: 85,
  systems_touched: ['production', 'database'],
  reversible: false
});

// decision.decision: 'allow' | 'warn' | 'block' | 'require_approval'
if (decision.decision === 'block') {
  console.log('Blocked:', decision.reason);
  return;
}

decision = claw.guard(
    action_type="deploy",
    declared_goal="Deploy build #402 to production",
    risk_score=85,
    systems_touched=["production", "database"],
    reversible=False
)

if decision["decision"] == "block":
    print(f"Blocked: {decision['reason']}")
    return

Guard response shape:

{
  "decision": "require_approval",
  "action_id": "act_gd_abc123",
  "reason": "Risk score exceeds org threshold",
  "signals": ["Production access", "High risk score"],
  "risk_score": 75,
  "agent_risk_score": 85
}

Step 2: Record — Log the Action

const action = await claw.createAction({
  action_type: 'deploy',
  declared_goal: 'Deploy build #402 to production',
  risk_score: 85,
  reversible: false,
  systems_touched: ['production']
});
// action.action_id: 'ar_abc123'

action = claw.create_action(
    action_type="deploy",
    declared_goal="Deploy build #402 to production",
    risk_score=85,
    reversible=False,
    systems_touched=["production"]
)

Step 3: Verify — Record Assumptions

await claw.recordAssumption({
  action_id: action.action_id,
  assumption: 'Staging tests passed successfully',
  source: 'ci-pipeline'
});

claw.record_assumption(
    action_id=action["action_id"],
    assumption="Staging tests passed successfully",
    source="ci-pipeline"
)

Step 4: Outcome — Record the Result

await claw.updateOutcome(action.action_id, {
  status: 'completed',        // or 'failed'
  output_summary: 'Build #402 deployed successfully to production',
  timestamp_end: new Date().toISOString()
});

claw.update_outcome(action["action_id"], {
    "status": "completed",
    "output_summary": "Build #402 deployed successfully to production"
})

Action Context — Automatic Correlation (v2.7.0)

Instead of manually passing action_id to every call, use actionContext():

Node.js

const action = await claw.createAction({
  action_type: 'deploy',
  declared_goal: 'Deploy build #402 to production',
  risk_score: 85,
  reversible: false
});

// All operations auto-tagged with action.action_id
const ctx = claw.actionContext(action.action_id);
await ctx.sendMessage({ to: 'ops-agent', type: 'status', body: 'Starting deploy' });
await ctx.recordAssumption({ assumption: 'All CI checks passed' });

try {
  await actualDeploy(buildId);
  await ctx.updateOutcome({ status: 'completed', output_summary: 'Deployed successfully' });
} catch (err) {
  await ctx.updateOutcome({ status: 'failed', output_summary: err.message });
}

Python

action = claw.create_action(
    action_type="deploy",
    declared_goal="Deploy build #402 to production",
    risk_score=85,
    reversible=False
)

with claw.action_context(action["action_id"]) as ctx:
    ctx.send_message("Starting deploy", to="ops-agent")
    ctx.record_assumption({"assumption": "All CI checks passed"})

    try:
        actual_deploy(build_id)
        ctx.update_outcome(status="completed", output_summary="Deployed successfully")
    except Exception as e:
        ctx.update_outcome(status="failed", output_summary=str(e))

Messages and assumptions sent through the context appear correlated in the decision timeline at /decisions/{actionId} — showing the full causal chain of guard decision → messages → action → assumptions → outcome.

Complete Example

import { DashClaw } from 'dashclaw';

const claw = new DashClaw({
  baseUrl: process.env.DASHCLAW_BASE_URL,
  apiKey: process.env.DASHCLAW_API_KEY,
  agentId: 'deploy-agent'
});

async function governedDeploy(buildId) {
  // 1. Guard
  const decision = await claw.guard({
    action_type: 'deploy',
    declared_goal: `Deploy build #${buildId} to production`,
    risk_score: 85,
    systems_touched: ['production'],
    reversible: false
  });

  if (decision.decision === 'block') {
    console.log('Blocked:', decision.reason);
    return;
  }

  // 2. Record
  const action = await claw.createAction({
    action_type: 'deploy',
    declared_goal: `Deploy build #${buildId} to production`,
    : ,
    : 
  });

  
   ctx = claw.(action.);

   ctx.({ : , : , :  });
   ctx.({ :  });

  
   {
     (buildId);
     ctx.({
      : ,
      : 
    });
  }  (err) {
     ctx.({
      : ,
      : err.
    });
  }
}

Action Type & Risk Score Guide

Action Type	Risk Score	Reversible	Example
deploy	75-90	false	Production deployment
api_call	20-40	true	External API request
file_write	15-30	true	Local file modification
database	50-80	false	Schema migration, data deletion
security	80-95	false	Key rotation, permission changes
build	10-25	true	npm install, compilation
notify	5-15	true	Send email, Slack message

Risk scoring rule: DashClaw uses the HIGHER of computed risk and agent-reported risk. Always report honestly — inflating risk is better than under-reporting.

HITL Approval Flow

When guard returns require_approval:

if (decision.decision === 'require_approval') {
  console.log('Waiting for human approval...');
  await claw.waitForApproval(decision.action_id, {
    timeout: 300000  // 5 minutes
  });
  // Continues after approval, throws ApprovalDeniedError if denied
}

Environment Variables

Variable	Required	Description
DASHCLAW_BASE_URL	Yes	DashClaw instance URL
DASHCLAW_API_KEY	Yes	API authentication key
DASHCLAW_AGENT_ID	No	Default agent identifier

Validation

After instrumenting, run the integration validator:

node scripts/validate-integration.mjs --base-url $DASHCLAW_BASE_URL --api-key $DASHCLAW_API_KEY