"""

Show how a non-sandbox agent can hand work to a sandbox agent.

The intake agent never sees a workspace directly. It hands document-heavy work

to a sandbox reviewer, and that reviewer then hands the synthesized result to a

plain account-facing writer.

"""

import argparse

import asyncio

import sys

from pathlib import Path

from agents import Agent, Runner

from agents.run import RunConfig

from agents.sandbox import SandboxAgent, SandboxRunConfig

from agents.sandbox.sandboxes.unix_local import UnixLocalSandboxClient

if __package__ is None or __package__ == "":

sys.path.insert(0, str(Path(__file__).resolve().parents[2]))

from examples.sandbox.misc.example_support import text_manifest

from examples.sandbox.misc.workspace_shell import WorkspaceShellCapability

DEFAULT_QUESTION = (

"Review the attached onboarding packet and draft a short internal note for the account "

"executive about what to confirm before kickoff."

)

async def main(model: str, question: str) -> None:

# The manifest becomes the workspace that only the sandbox reviewer can inspect.

manifest = text_manifest(

{

"customer_background.md": (

"# Customer background\n\n"

"- Customer: Bluebird Logistics.\n"

"- Region: North America.\n"

"- New purchase: analytics workspace plus SSO.\n"

),

"kickoff_checklist.md": (

"# Kickoff checklist\n\n"

"- Security questionnaire is still in review.\n"

"- Two customer admins still need to complete access training.\n"

"- Target kickoff date is next Tuesday.\n"

),

"implementation_scope.md": (

"# Implementation scope\n\n"

"- The customer wants historical data migration for 5 years of records.\n"

"- Data engineering support is available only starting next month.\n"

),

}

)

# This final agent does not inspect files. It only rewrites reviewed facts into a note.

account_manager = Agent(

name="Account Executive Assistant",

model=model,

instructions=(

"You write concise internal updates for account teams. Convert the sandbox review "

"into a short note with a headline, the top risks, and a recommended next step."

),

)

# This sandbox agent can inspect the workspace, then hand its findings to the writer above.

sandbox_reviewer = SandboxAgent(

name="Onboarding Packet Reviewer",

model=model,

instructions=(

"You inspect onboarding documents in the sandbox, verify the facts, then hand off "

"to the account executive assistant to draft the final note. Do not answer the user "

"directly after reviewing the packet."

),

default_manifest=manifest,

handoffs=[account_manager],

capabilities=[WorkspaceShellCapability()],

)

# The starting agent is a normal agent. It only decides when to hand off into the sandbox.

intake_agent = Agent(

name="Deal Desk Intake",

model=model,

instructions=(

"You triage internal requests. If a request depends on attached documents, hand off "

"to the onboarding packet reviewer immediately."

),

handoffs=[sandbox_reviewer],

)

result = await Runner.run(

intake_agent,

question,

run_config=RunConfig(sandbox=SandboxRunConfig(client=UnixLocalSandboxClient())),

)

print(result.final_output)

if __name__ == "__main__":

parser = argparse.ArgumentParser()

parser.add_argument("--model", default="gpt-5.5", help="Model name to use.")

parser.add_argument("--question", default=DEFAULT_QUESTION, help="Prompt to send to the agent.")

args = parser.parse_args()

asyncio.run(main(args.model, args.question))