Initialize and configure Agent Control

# Only required if you are using an on-premises, standalone,
# or custom deployment
# export SPLUNK_AO_API_KEY="<your-splunk-ao-api-key>"
# export SPLUNK_AO_CONSOLE_URL="<your-splunk-ao-url>"
export SPLUNK_AO_API_URL="<your-api-url>"

export SPLUNK_AO_PROJECT="<project-name>"
export SPLUNK_AO_AGENT_STREAM="<agent-stream-name>"
export AGENT_CONTROL_URL="https://agent-control.<env>.<registered-domain>"
# The <registered-domain> is derived from your deployment URL
# Example URL: https://console.subdomain.yourcompany.com
# Example registered domain: yourcompany.com
export AGENT_CONTROL_TARGET_TYPE="agent_stream"
export AGENT_CONTROL_AGENT_NAME="default"
export AGENT_CONTROL_API_KEY_HEADER="Splunk-AO-API-Key"

import os
from uuid import uuid4

import agent_control
from splunk_ao.logger import SplunkAOLogger


def init_splunk_ao_and_agent_control() -> SplunkAOLogger:
    # These names must match the project/Agent stream where controls are bound.
    project_name = os.environ["SPLUNK_AO_PROJECT"]
    agent_stream_name = os.environ["SPLUNK_AO_AGENT_STREAM"]

    logger = SplunkAOLogger(
        project=project_name,
        agent_stream=agent_stream_name,
        mode=os.environ.get("SPLUNK_AO_MODE", "batch"),
    )

    logger.start_session(
        name=os.environ.get("SPLUNK_AO_SESSION_NAME", "agent-control-demo"),
        external_id=f"agent-control-demo-{uuid4()}",
        metadata={"source": "agent-control-demo"},
    )

    if logger.project_id is None or logger.agent_stream_id is None:
        raise RuntimeError("Splunk Agent Observability logger did not resolve project/Agent Stream IDs.")

    # Useful for downstream SDK integrations/debugging.
    os.environ["SPLUNK_AO_PROJECT_ID"] = logger.project_id
    os.environ["SPLUNK_AO_AGENT_STREAM_ID"] = logger.agent_stream_id

    agent_control.init(
        agent_name=os.environ.get("AGENT_CONTROL_AGENT_NAME", "my-agent"),
        agent_description="Agent Control app with Splunk Agent Observability logging",
        server_url=os.environ["AGENT_CONTROL_URL"],
        # Only required if you are using an on-premises, standalone, or custom deployment
        # api_key=os.environ["SPLUNK_AO_API_KEY"],
        api_key_header=os.environ.get("AGENT_CONTROL_API_KEY_HEADER", "Splunk-AO-API-Key"),
        observability_enabled=True,
        observability_sink_name="registered",

        # Controls are usually bound to the Splunk Agent Observability Agent Stream.
        target_type=os.environ.get("AGENT_CONTROL_TARGET_TYPE", "agent_stream"),
        target_id=logger.agent_stream_id,
    )

    return logger

logger = init_splunk_ao_and_agent_control()

trace = logger.start_trace(
    name="my-agent-run",
    input={"user_request": "Wire $15,000 to Horizon Robotics."},
)

try:
    # Run your decorated agent Steps here.
    result = run_agent()
finally:
    logger.conclude(output={"result": result})
    logger.flush()

from agent_control import control


@control()
async def call_llm(message: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4.1",
        messages=[{"role": "user", "content": message}],
    )
    return response.choices[0].message.content