One Platform.
Agents and Workflows.

import os
import time

from conductor.ai.agents import Agent, AgentRuntime, resume, tool
from conductor.client.configuration.configuration import Configuration

SERVER_URL = os.environ.get("CONDUCTOR_SERVER_URL", "http://localhost:8080/api")
LLM_MODEL = os.environ.get("CONDUCTOR_AGENT_LLM_MODEL", "openai/gpt-4o-mini")


@tool(approval_required=True)
def deploy_to_production(service: str, version: str) -> dict:
    """Deploy a service to the production environment. Requires human approval."""
    # Stand in for your real rollout, e.g. pipeline.rollout(service, version)
    return {"service": service, "version": version, "status": "rolled out"}


agent = Agent(
    name="production_deploy_agent",
    model=LLM_MODEL,
    tools=[deploy_to_production],
    instructions=(
        "You roll out production changes. "
        "When asked to deploy, call deploy_to_production "
        "with the service name and version."
    ),
)


if __name__ == "__main__":
    config = Configuration(server_api_url=SERVER_URL)

    with AgentRuntime(config) as runtime:
        runtime.deploy(agent)
        handle = runtime.start(agent, "Deploy 'payments-api' v2.4.1 to production now.")
        execution_id = handle.execution_id
        print(f"started: {execution_id}")

    ## …weeks later, from anywhere

    with AgentRuntime(config) as runtime:
        handle = resume(execution_id, agent, runtime=runtime)

        while not handle.get_status().is_waiting:
            time.sleep(1)

        call = handle.get_status().pending_tool["toolCalls"][0]
        print(f"awaiting approval: {call['name']}({call['args']})")

        handle.approve()
        handle.join().print_result()