A Coding Implementation to Design an Enterprise AI Governance System Utilizing OpenClaw Gateway Coverage Engines, Approval Workflows and Auditable Agent Execution

!apt-get replace -y
!apt-get set up -y curl
!curl -fsSL  | bash -
!apt-get set up -y nodejs
!node -v
!npm -v
!npm set up -g openclaw@newest
!pip -q set up requests pandas pydantic


import os
import json
import time
import uuid
import secrets and techniques
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, Area


attempt:
   from google.colab import userdata
   OPENAI_API_KEY = userdata.get("OPENAI_API_KEY")
besides 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(mother and father=True, exist_ok=True)
WORKSPACE = OPENCLAW_HOME / "workspace"
WORKSPACE.mkdir(mother and father=True, exist_ok=True)


GATEWAY_TOKEN = secrets and techniques.token_urlsafe(48)
GATEWAY_PORT = 18789
GATEWAY_URL = f"

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))


physician = subprocess.run(
   ["bash", "-lc", "openclaw doctor --fix --yes"],
   capture_output=True,
   textual content=True
)
print(physician.stdout[-2000:])
print(physician.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):
   begin = time.time()
   whereas time.time() - begin < timeout:
       attempt:
           r = requests.get(f"{GATEWAY_URL}/", timeout=5)
           if r.status_code in (200, 401, 403, 404):
               return True
       besides Exception:
           go
       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, consumer="demo-user", agent_id="main", temperature=0.2):
   payload = {
       "model": f"openclaw:{agent_id}",
       "messages": messages,
       "user": consumer,
       "temperature": temperature,
       "stream": False
   }
   r = requests.publish(
       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
   class: str
   threat: str
   confidence: float = Area(ge=0.0, le=1.0)
   requires_approval: bool
   enable: bool
   motive: str

def classify_request(user_request: str) -> ActionProposal:
   textual content = user_request.decrease()


   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 textual content for t in red_terms):
       return ActionProposal(
           user_request=user_request,
           class="high_impact",
           threat="red",
           confidence=0.92,
           requires_approval=True,
           enable=False,
           motive="High-impact or sensitive action detected"
       )


   if any(t in textual content for t in amber_terms):
       return ActionProposal(
           user_request=user_request,
           class="moderate_impact",
           threat="amber",
           confidence=0.76,
           requires_approval=True,
           enable=True,
           motive="Moderate-risk action requires human approval before execution"
       )


   return ActionProposal(
       user_request=user_request,
       class="low_impact",
       threat="green",
       confidence=0.88,
       requires_approval=False,
       enable=True,
       motive="Low-risk request"
   )


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


@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, occasion: TraceEvent):
       with open(self.path, "a") as f:
           f.write(json.dumps(asdict(occasion)) + "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.hundreds(line))
       return rows


trace_store = TraceStore()


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


SYSTEM_PROMPT = """
You might be an enterprise OpenClaw assistant working beneath governance controls.


Guidelines:
- By no means declare an motion has been executed until the governance layer explicitly permits it.
- For low-risk requests, reply usually and helpfully.
- For moderate-risk requests, suggest a protected plan and point out any approvals or checks that may be wanted.
- For top-risk requests, refuse to execute and as a substitute present a safer non-operational various equivalent to a draft, guidelines, abstract, or evaluate plan.
- Be concise however helpful.
"""


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.threat == "red":
       end 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", end result))
       return end result


   if proposal.threat == "amber" and never approval["approved"]:
       end 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", end result))
       return end result


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


   uncooked = openclaw_chat(messages=messages, consumer=session_user, agent_id="main", temperature=0.2)
   assistant_text = uncooked["choices"][0]["message"]["content"]


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


outcomes = [governed_openclaw_run(x) for x in demo_requests]


for r in outcomes:
   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.publish(
   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.textual content[:1500])