OpenAI Agents SDK

import asyncio
from agents import Agent, function_tool
from dotenv import load_dotenv

from hexgate.runtime import HexgateContext
from hexgate.adapters.openai import HexgateRunner

INSTRUCTION = (
    "You are a DevOps assistant for a Kubernetes platform. Help engineers read "
    "service logs, restart services, and scale deployments. Pull the service "
    "name, replica count, and environment (dev/staging/prod) from the request "
    "and act directly — the policy layer gates sensitive actions."
)


@function_tool
def read_logs(service: str, env: str) -> str:
    """Return recent log lines for `service` in `env` (dev/staging/prod)."""
    return f"(stub) {service}@{env}: 200 OK, 200 OK, WARN upstream slow"


@function_tool
def restart_service(service: str, env: str) -> str:
    """Restart `service` in `env`."""
    return f"(stub) restarted {service}@{env}"


@function_tool
def scale_deployment(service: str, replicas: int, env: str) -> str:
    """Scale `service` to `replicas` pods in `env`."""
    return f"(stub) scaled {service}@{env} to {replicas} replicas"


async def main():
    load_dotenv()

    agent = Agent(
        name="devops_agent",
        instructions=INSTRUCTION,
        tools=[read_logs, restart_service, scale_deployment],
        model="gpt-4o-mini",
    )

    runner = HexgateRunner()  # picks up HEXGATE_API_KEY from env
    result = await runner.run(
        agent,
        "Scale the checkout service to 5 replicas in staging.",
        hexgate_context=HexgateContext(user_id="engineer_1", session_id="session_1", user_roles=["operator"]),
    )
    print(result.final_output)


if __name__ == "__main__":
    asyncio.run(main())