wehub-resource-sync
105 lines
3.9 KiB
Python
105 lines
3.9 KiB
Python
"""
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Show how a non-sandbox agent can hand work to a sandbox agent.
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The intake agent never sees a workspace directly. It hands document-heavy work
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to a sandbox reviewer, and that reviewer then hands the synthesized result to a
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plain account-facing writer.
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"""
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import argparse
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import asyncio
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import sys
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from pathlib import Path
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from agents import Agent, Runner
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from agents.run import RunConfig
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from agents.sandbox import SandboxAgent, SandboxRunConfig
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from agents.sandbox.sandboxes.unix_local import UnixLocalSandboxClient
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if __package__ is None or __package__ == "":
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sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
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from examples.sandbox.misc.example_support import text_manifest
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from examples.sandbox.misc.workspace_shell import WorkspaceShellCapability
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DEFAULT_QUESTION = (
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"Review the attached onboarding packet and draft a short internal note for the account "
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"executive about what to confirm before kickoff."
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)
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async def main(model: str, question: str) -> None:
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# The manifest becomes the workspace that only the sandbox reviewer can inspect.
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manifest = text_manifest(
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{
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"customer_background.md": (
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"# Customer background\n\n"
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"- Customer: Bluebird Logistics.\n"
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"- Region: North America.\n"
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"- New purchase: analytics workspace plus SSO.\n"
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),
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"kickoff_checklist.md": (
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"# Kickoff checklist\n\n"
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"- Security questionnaire is still in review.\n"
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"- Two customer admins still need to complete access training.\n"
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"- Target kickoff date is next Tuesday.\n"
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),
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"implementation_scope.md": (
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"# Implementation scope\n\n"
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"- The customer wants historical data migration for 5 years of records.\n"
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"- Data engineering support is available only starting next month.\n"
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),
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}
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)
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# This final agent does not inspect files. It only rewrites reviewed facts into a note.
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account_manager = Agent(
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name="Account Executive Assistant",
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model=model,
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instructions=(
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"You write concise internal updates for account teams. Convert the sandbox review "
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"into a short note with a headline, the top risks, and a recommended next step."
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),
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)
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# This sandbox agent can inspect the workspace, then hand its findings to the writer above.
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sandbox_reviewer = SandboxAgent(
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name="Onboarding Packet Reviewer",
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model=model,
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instructions=(
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"You inspect onboarding documents in the sandbox, verify the facts, then hand off "
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"to the account executive assistant to draft the final note. Do not answer the user "
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"directly after reviewing the packet."
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),
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default_manifest=manifest,
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handoffs=[account_manager],
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capabilities=[WorkspaceShellCapability()],
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)
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# The starting agent is a normal agent. It only decides when to hand off into the sandbox.
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intake_agent = Agent(
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name="Deal Desk Intake",
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model=model,
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instructions=(
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"You triage internal requests. If a request depends on attached documents, hand off "
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"to the onboarding packet reviewer immediately."
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),
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handoffs=[sandbox_reviewer],
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)
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result = await Runner.run(
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intake_agent,
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question,
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run_config=RunConfig(sandbox=SandboxRunConfig(client=UnixLocalSandboxClient())),
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)
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print(result.final_output)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", default="gpt-5.6-sol", help="Model name to use.")
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parser.add_argument("--question", default=DEFAULT_QUESTION, help="Prompt to send to the agent.")
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args = parser.parse_args()
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asyncio.run(main(args.model, args.question))
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