A coding implementation for designing enterprise AI governance systems using the OpenGL Gateway policy engine, approval workflow, and auditable agent execution

!apt-get update -y
!apt-get install -y curl
!curl -fsSL https://deb.nodesource.com/setup_22.x | bash -
!apt-get install -y nodejs
!node -v
!npm -v
!npm install -g openclaw@latest
!pip -q install requests pandas pydantic


import os
import json
import time
import uuid
import secrets
import subprocess
import getpass
from pathlib import Path
from typing import Dict, Any
from dataclasses import dataclass, asdict
from datetime import datetime, timezone


import requests
import pandas as pd
from pydantic import BaseModel, Field


try:
   from google.colab import userdata
   OPENAI_API_KEY = userdata.get("OPENAI_API_KEY")
except Exception:
   OPENAI_API_KEY = None


if not OPENAI_API_KEY:
   OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")


if not OPENAI_API_KEY:
   OPENAI_API_KEY = getpass.getpass("Enter your OpenAI API key (hidden input): ").strip()


assert OPENAI_API_KEY != "", "API key cannot be empty."


OPENCLAW_HOME = Path("/root/.openclaw")
OPENCLAW_HOME.mkdir(parents=True, exist_ok=True)
WORKSPACE = OPENCLAW_HOME / "workspace"
WORKSPACE.mkdir(parents=True, exist_ok=True)


GATEWAY_TOKEN = secrets.token_urlsafe(48)
GATEWAY_PORT = 18789
GATEWAY_URL = f"http://127.0.0.1:{GATEWAY_PORT}"

config = {
   "env": {
       "OPENAI_API_KEY": OPENAI_API_KEY
   },
   "agents": {
       "defaults": {
           "workspace": str(WORKSPACE),
           "model": {
               "primary": "openai/gpt-4.1-mini"
           }
       }
   },
   "gateway": {
       "mode": "local",
       "port": GATEWAY_PORT,
       "bind": "loopback",
       "auth": {
           "mode": "token",
           "token": GATEWAY_TOKEN
       },
       "http": {
           "endpoints": {
               "chatCompletions": {
                   "enabled": True
               }
           }
       }
   }
}


config_path = OPENCLAW_HOME / "openclaw.json"
config_path.write_text(json.dumps(config, indent=2))


doctor = subprocess.run(
   ("bash", "-lc", "openclaw doctor --fix --yes"),
   capture_output=True,
   text=True
)
print(doctor.stdout(-2000:))
print(doctor.stderr(-2000:))


gateway_log = "/tmp/openclaw_gateway.log"
gateway_cmd = f"OPENAI_API_KEY='{OPENAI_API_KEY}' OPENCLAW_GATEWAY_TOKEN='{GATEWAY_TOKEN}' openclaw gateway --port {GATEWAY_PORT} --bind loopback --token '{GATEWAY_TOKEN}' --verbose > {gateway_log} 2>&1 & echo $!"
gateway_pid = subprocess.check_output(("bash", "-lc", gateway_cmd)).decode().strip()
print("Gateway PID:", gateway_pid)

def wait_for_gateway(timeout=120):
   start = time.time()
   while time.time() - start < timeout:
       try:
           r = requests.get(f"{GATEWAY_URL}/", timeout=5)
           if r.status_code in (200, 401, 403, 404):
               return True
       except Exception:
           pass
       time.sleep(2)
   return False


assert wait_for_gateway(), Path(gateway_log).read_text()(-6000:)


headers = {
   "Authorization": f"Bearer {GATEWAY_TOKEN}",
   "Content-Type": "application/json"
}


def openclaw_chat(messages, user="demo-user", agent_id="main", temperature=0.2):
   payload = {
       "model": f"openclaw:{agent_id}",
       "messages": messages,
       "user": user,
       "temperature": temperature,
       "stream": False
   }
   r = requests.post(
       f"{GATEWAY_URL}/v1/chat/completions",
       headers=headers,
       json=payload,
       timeout=180
   )
   r.raise_for_status()
   return r.json()


class ActionProposal(BaseModel):
   user_request: str
   category: str
   risk: str
   confidence: float = Field(ge=0.0, le=1.0)
   requires_approval: bool
   allow: bool
   reason: str

def classify_request(user_request: str) -> ActionProposal:
   text = user_request.lower()


   red_terms = (
       "delete", "remove permanently", "wire money", "transfer funds",
       "payroll", "bank", "hr record", "employee record", "run shell",
       "execute command", "api key", "secret", "credential", "token",
       "ssh", "sudo", "wipe", "exfiltrate", "upload private", "database dump"
   )
   amber_terms = (
       "email", "send", "notify", "customer", "vendor", "contract",
       "invoice", "budget", "approve", "security policy", "confidential",
       "write file", "modify", "change"
   )


   if any(t in text for t in red_terms):
       return ActionProposal(
           user_request=user_request,
           category="high_impact",
           risk="red",
           confidence=0.92,
           requires_approval=True,
           allow=False,
           reason="High-impact or sensitive action detected"
       )


   if any(t in text for t in amber_terms):
       return ActionProposal(
           user_request=user_request,
           category="moderate_impact",
           risk="amber",
           confidence=0.76,
           requires_approval=True,
           allow=True,
           reason="Moderate-risk action requires human approval before execution"
       )


   return ActionProposal(
       user_request=user_request,
       category="low_impact",
       risk="green",
       confidence=0.88,
       requires_approval=False,
       allow=True,
       reason="Low-risk request"
   )


def simulated_human_approval(proposal: ActionProposal) -> Dict(str, Any):
   if proposal.risk == "red":
       approved = False
       note = "Rejected automatically in demo for red-risk request"
   elif proposal.risk == "amber":
       approved = True
       note = "Approved automatically in demo for amber-risk request"
   else:
       approved = True
       note = "No approval required"
   return {
       "approved": approved,
       "reviewer": "simulated_manager",
       "note": note
   }


@dataclass
class TraceEvent:
   trace_id: str
   ts: str
   stage: str
   payload: Dict(str, Any)

class TraceStore:
   def __init__(self, path="openclaw_traces.jsonl"):
       self.path = path
       Path(self.path).write_text("")


   def append(self, event: TraceEvent):
       with open(self.path, "a") as f:
           f.write(json.dumps(asdict(event)) + "n")


   def read_all(self):
       rows = ()
       with open(self.path, "r") as f:
           for line in f:
               line = line.strip()
               if line:
                   rows.append(json.loads(line))
       return rows


trace_store = TraceStore()


def now():
   return datetime.now(timezone.utc).isoformat()


SYSTEM_PROMPT = """
You are an enterprise OpenClaw assistant operating under governance controls.


Rules:
- Never claim an action has been executed unless the governance layer explicitly allows it.
- For low-risk requests, answer normally and helpfully.
- For moderate-risk requests, propose a safe plan and mention any approvals or checks that would be needed.
- For high-risk requests, refuse to execute and instead provide a safer non-operational alternative such as a draft, checklist, summary, or review plan.
- Be concise but useful.
"""


def governed_openclaw_run(user_request: str, session_user: str = "employee-001") -> Dict(str, Any):
   trace_id = str(uuid.uuid4())


   proposal = classify_request(user_request)
   trace_store.append(TraceEvent(trace_id, now(), "classification", proposal.model_dump()))


   approval = None
   if proposal.requires_approval:
       approval = simulated_human_approval(proposal)
       trace_store.append(TraceEvent(trace_id, now(), "approval", approval))


   if proposal.risk == "red":
       result = {
           "trace_id": trace_id,
           "status": "blocked",
           "proposal": proposal.model_dump(),
           "approval": approval,
           "response": "This request is blocked by governance policy. I can help by drafting a safe plan, a checklist, or an approval packet instead."
       }
       trace_store.append(TraceEvent(trace_id, now(), "blocked", result))
       return result


   if proposal.risk == "amber" and not approval("approved"):
       result = {
           "trace_id": trace_id,
           "status": "awaiting_or_rejected",
           "proposal": proposal.model_dump(),
           "approval": approval,
           "response": "This request requires approval and was not cleared."
       }
       trace_store.append(TraceEvent(trace_id, now(), "halted", result))
       return result


   messages = (
       {"role": "system", "content": SYSTEM_PROMPT},
       {"role": "user", "content": f"Governance classification: {proposal.model_dump_json()}nnUser request: {user_request}"}
   )


   raw = openclaw_chat(messages=messages, user=session_user, agent_id="main", temperature=0.2)
   assistant_text = raw("choices")(0)("message")("content")


   result = {
       "trace_id": trace_id,
       "status": "executed_via_openclaw",
       "proposal": proposal.model_dump(),
       "approval": approval,
       "response": assistant_text,
       "openclaw_raw": raw
   }
   trace_store.append(TraceEvent(trace_id, now(), "executed", {
       "status": result("status"),
       "response_preview": assistant_text(:500)
   }))
   return result


demo_requests = (
   "Summarize our AI governance policy for internal use.",
   "Draft an email to finance asking for confirmation of the Q1 cloud budget.",
   "Send an email to all employees that payroll will be delayed by 2 days.",
   "Transfer funds from treasury to vendor account immediately.",
   "Run a shell command to archive the home directory and upload it."
)


results = (governed_openclaw_run(x) for x in demo_requests)


for r in results:
   print("=" * 120)
   print("TRACE:", r("trace_id"))
   print("STATUS:", r("status"))
   print("RISK:", r("proposal")("risk"))
   print("APPROVAL:", r("approval"))
   print("RESPONSE:n", r("response")(:1500))


trace_df = pd.DataFrame(trace_store.read_all())
trace_df.to_csv("openclaw_governance_traces.csv", index=False)
print("nSaved: openclaw_governance_traces.csv")


safe_tool_payload = {
   "tool": "sessions_list",
   "action": "json",
   "args": {},
   "sessionKey": "main",
   "dryRun": False
}


tool_resp = requests.post(
   f"{GATEWAY_URL}/tools/invoke",
   headers=headers,
   json=safe_tool_payload,
   timeout=60
)


print("n/tools/invoke status:", tool_resp.status_code)
print(tool_resp.text(:1500))