1. Pengantar
Ringkasan
Dalam codelab ini, Anda akan membuat asisten AI pribadi yang membantu Anda menganalisis data bisnis dan melakukan tugas lain melalui UI chat. Anda akan menggunakan layanan Cloud Run untuk menghosting agen pribadi Anda.
Agen Anda akan menggunakan sandbox Cloud Run. Sandbox Cloud Run adalah lingkungan runtime native, aman, dan sangat cepat yang dibuat khusus untuk mengeksekusi kode dan beban kerja agen yang tidak tepercaya, yang dimulai dalam milidetik. Sandbox memungkinkan Agen AI Anda menulis, menjalankan, dan menguji kode secara dinamis dengan cepat untuk memecahkan masalah analitis yang kompleks.
Catatan: Untuk memastikan pengalaman pengembangan yang lancar saat berjalan secara lokal versus dalam produksi:
- Di Produksi (sandbox Cloud Run): Agen menjalankan kode dengan aman di dalam playground yang terisolasi dan dalam container melalui biner sandbox khusus (/usr/local/gcp/bin/sandbox).
- Secara Lokal (Mesin Anda): Saat berjalan secara lokal, aplikasi mendeteksi bahwa lingkungan sandbox produksi tidak ada (IS_LOCAL_MODE = True). Agen menjalankan skrip Python dan perintah shell langsung di terminal sistem mesin host lokal Anda.
Yang akan Anda bangun:
Dalam skenario ini, Anda mengelola kedai kopi di kota universitas yang sedang bersiap menghadapi akhir pekan kelulusan yang ramai. Anda memerlukan agen untuk mereferensi silang data mentah Point-of-Sale (POS) dengan jadwal upacara universitas untuk menemukan hambatan operasional tersembunyi.
Agen menggunakan sandbox yang aman untuk menulis dan menjalankan skrip Python, menganalisis kompleksitas minuman versus jumlah kasir untuk merekomendasikan penyesuaian staf dan inventaris.
Agen mengirimkan ping kepada pemilik melalui UI chat tiruan dengan rekomendasi inventaris dan staf yang ditargetkan. Aplikasi ini menunggu izin eksplisit sebelum memperbarui spreadsheet dengan daftar tugas operasional untuk pengelola kedai kopi.
Yang akan Anda pelajari
- Cara membuat layanan Cloud Run
- Cara men-deploy agen ADK di layanan Cloud Run
- Cara membuat agen menjalankan kode di sandbox dalam layanan Cloud Run
- Cara membuat UI chat menggunakan WebSockets untuk berinteraksi dengan agen latar belakang
2. Penyiapan dan Persyaratan
Konfigurasi project ID dan region Anda.
GOOGLE_CLOUD_PROJECT=<YOUR_PROJECT_ID>
REGION=us-west2
gcloud config set project $GOOGLE_CLOUD_PROJECT
gcloud config set run/region $REGION
Berikut adalah variabel lingkungan yang akan digunakan di seluruh codelab ini. Anda dapat menyimpannya dalam file lingkungan dan "membuat sumber" file tersebut. Pastikan untuk menetapkan nilai project ID Anda dengan benar dan secara opsional region.
SA_NAME=coffee-shop-agent-sa
SERVICE_ACCOUNT_ADDRESS=$SA_NAME@$GOOGLE_CLOUD_PROJECT.iam.gserviceaccount.com
Aktifkan API yang diperlukan untuk Codelab ini
gcloud services enable --project $GOOGLE_CLOUD_PROJECT \
run.googleapis.com \
cloudbuild.googleapis.com \
artifactregistry.googleapis.com \
sheets.googleapis.com \
aiplatform.googleapis.com
3. Buat Akun Layanan & Spreadsheet
Membuat Akun Layanan khusus untuk layanan Cloud Run Anda direkomendasikan untuk memberikan hanya peran yang diperlukan.
Buat Akun Layanan untuk layanan Cloud Run
gcloud iam service-accounts create $SA_NAME \
--description="Service account for the Coffee Shop Agent Codelab" \
--display-name="Coffee Shop Agent SA"
Karena agen menggunakan Gemini API (GOOGLE_GENAI_USE_VERTEXAI=1), berikan peran Pengguna Agent Platform ke akun layanan ini di project Anda.
gcloud projects add-iam-policy-binding $GOOGLE_CLOUD_PROJECT \
--member="serviceAccount:$SERVICE_ACCOUNT_ADDRESS" \
--role="roles/aiplatform.user"
Agar Anda dapat menjalankan dan menguji agen secara lokal menggunakan akun layanan ini, berikan izin identitas Google Cloud pribadi Anda untuk meniru identitas akun layanan ini:
gcloud iam service-accounts add-iam-policy-binding \
coffee-shop-agent-sa@$GOOGLE_CLOUD_PROJECT.iam.gserviceaccount.com \
--member="user:$(gcloud config get-value account)" \
--role="roles/iam.serviceAccountTokenCreator"
Buat spreadsheet
Spreadsheet ini menampilkan penjualan yang terjadi tahun lalu selama akhir pekan wisuda.
- Buat Spreadsheet Google baru di Google Drive Anda.
- Salin Nilai yang Dipisahkan Koma (CSV) berikut ke dalam Google Spreadsheet baru Anda (misalnya, pilih sel A1 dan tempel).
Day,Time,Drip_Coffee,Cold_Brew,Extra_Espresso,Alt_Milk_Oz,Pastries,Cashiers_Working,Wait_Time_Minutes
Saturday,07:00:00,95,15,5,30,80,2,9
Saturday,08:00:00,80,25,10,35,60,2,7
Saturday,09:00:00,30,30,20,40,20,1,4
Saturday,10:00:00,40,130,95,50,25,2,4
Saturday,11:00:00,25,45,25,40,15,1,5
Saturday,12:00:00,30,35,15,45,20,1,3
Saturday,13:00:00,15,20,10,30,10,1,2
Saturday,14:00:00,45,60,30,65,30,2,8
Saturday,15:00:00,20,30,15,35,15,1,3
Saturday,16:00:00,25,35,10,50,20,1,4
Saturday,17:00:00,10,20,5,35,10,1,2
Saturday,18:00:00,20,40,10,190,65,2,12
Saturday,19:00:00,10,15,5,40,15,1,2
Sunday,07:00:00,90,10,5,35,75,2,8
Sunday,08:00:00,75,20,10,40,65,2,6
Sunday,09:00:00,25,25,15,35,15,1,3
Sunday,10:00:00,60,35,15,60,50,2,7
Sunday,11:00:00,20,30,10,35,10,1,3
Sunday,12:00:00,25,40,10,55,20,1,3
Sunday,13:00:00,15,25,5,40,10,1,2
Sunday,14:00:00,30,50,15,180,60,2,11
Sunday,15:00:00,15,25,10,45,15,1,3
Sunday,16:00:00,20,45,20,40,15,1,4
Sunday,17:00:00,10,25,10,30,10,1,2
Sunday,18:00:00,25,145,110,45,20,2,16
Sunday,19:00:00,15,30,15,35,10,1,4
- Saat data Anda masih dipilih, klik Data > Pisahkan teks menjadi kolom di menu atas Google Spreadsheet.
Pastikan akun layanan memiliki akses Editor ke spreadsheet. Hal ini mirip dengan cara Anda memberikan akses kepada rekan tim.
- Salin email lengkap akun layanan baru Anda
echo $SERVICE_ACCOUNT_ADDRESS
- Dari Spreadsheet Google Anda, klik Bagikan di pojok kanan atas
- Tempelkan email akun layanan, tetapkan izin ke Editor, lalu klik Bagikan. (Anda dapat menghapus centang Beri tahu).
- Catat ID spreadsheet yang akan Anda teruskan ke agen, misalnya https://docs.google.com/spreadsheets/d/<YOUR_SPREADSHEET_ID>/edit?gid=0#gid=0
SPREADSHEET_ID=<THE SPREADHSEET ID FROM ITS URL>
4. Membuat Agen ADK
Pertama, buat direktori untuk kode Anda.
mkdir coffee-mgr-agent && cd coffee-mgr-agent
Buat file requirements.txt.
fastapi>=0.100.0
uvicorn>=0.22.0
google-adk>=1.27.1
google-auth
google-api-python-client
Membuat Dockerfile
FROM python:3.11-slim
ENV PYTHONUNBUFFERED=1
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY main.py .
EXPOSE 8080
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8080"]
Membuat file main.py
import os
import asyncio
import subprocess
from pathlib import Path
from typing import List
from fastapi import FastAPI, HTTPException, WebSocket, WebSocketDisconnect
from fastapi.responses import HTMLResponse
from pydantic import BaseModel
from google.adk.agents import LlmAgent as Agent
from google.adk.tools import FunctionTool
from google.adk.apps import App
from google.adk import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types
SANDBOX_CLI = '/usr/local/gcp/bin/sandbox'
IS_LOCAL_MODE = not Path(SANDBOX_CLI).exists()
active_connections: list[WebSocket] = []
SPREADSHEET_ID = os.environ.get("SPREADSHEET_ID")
# And
def run_sandbox_process(args: list[str]):
cmd = args[2:] if IS_LOCAL_MODE and args[:2] == ['do', '--'] else ([SANDBOX_CLI] + args if not IS_LOCAL_MODE else args)
return subprocess.run(cmd, capture_output=True, text=True, timeout=10)
def execute_sandbox_command(command: str) -> str:
"""Executes arbitrary POSIX shell/bash commands inside sandbox."""
mode = "LOCAL" if IS_LOCAL_MODE else "CLOUD RUN SANDBOX"
print(f"[ADK Sandbox Tool] Starting {mode} shell run...")
try:
res = run_sandbox_process(['do', '--', '/bin/sh', '-c', command])
if res.returncode != 0:
return f"Execution Failed!\nExit Code: {res.returncode}\nStdout:\n{res.stdout}\nStderr:\n{res.stderr}"
return res.stdout
except Exception as err:
return f"Internal Sandbox Tool Error: {str(err)}"
def get_sheets_service():
"""Initializes and returns the Google Sheets client service."""
from google.auth import default
from googleapiclient.discovery import build
credentials, _ = default(scopes=[
'https://www.googleapis.com/auth/spreadsheets',
'https://www.googleapis.com/auth/cloud-platform'
])
return build('sheets', 'v4', credentials=credentials)
def read_spreadsheet_values(spreadsheet_id: str, range_name: str) -> str:
"""Reads a range of cells from a Google Spreadsheet."""
try:
service = get_sheets_service()
result = service.spreadsheets().values().get(spreadsheetId=spreadsheet_id, range=range_name).execute()
rows = result.get('values', [])
return str(rows) if rows else "No data found in the specified range."
except Exception as e:
return f"Read Error: {str(e)}"
def update_spreadsheet_values(spreadsheet_id: str, range_name: str, values: List[List[str]]) -> str:
"""Updates a range of cells in a Google Spreadsheet with the provided values."""
try:
service = get_sheets_service()
result = service.spreadsheets().values().update(
spreadsheetId=spreadsheet_id, range=range_name,
valueInputOption="USER_ENTERED", body={'values': values}).execute()
return f"Successfully updated {result.get('updatedCells')} cells in {range_name}."
except Exception as e:
return f"Write Error: {str(e)}"
def create_spreadsheet_tab(spreadsheet_id: str, tab_name: str) -> str:
"""Creates a new sheet tab in a Google Spreadsheet if it doesn't already exist."""
try:
service = get_sheets_service()
# Check if tab exists
spreadsheet = service.spreadsheets().get(spreadsheetId=spreadsheet_id).execute()
for sheet in spreadsheet.get('sheets', []):
if sheet.get('properties', {}).get('title') == tab_name:
return f"Sheet tab '{tab_name}' already exists."
# Create tab
body = {'requests': [{'addSheet': {'properties': {'title': tab_name}}}]}
service.spreadsheets().batchUpdate(spreadsheetId=spreadsheet_id, body=body).execute()
return f"Successfully created new sheet tab '{tab_name}'."
except Exception as e:
return f"Error creating sheet tab: {str(e)}"
# ==========================================
# 3. ADK AGENT & RUNNER SETUP
# ==========================================
root_agent = Agent(
name='secure_coding_assistant',
description='ADK agent capable of executing shell commands and managing Google Spreadsheets.',
model=os.environ.get('GEMINI_MODEL', 'gemini-3.1-flash-lite'),
instruction=(
f'You are an expert AI Business Analyst for a coffee shop during university graduation weekend.\n'
f'The Google Spreadsheet ID you are managing is: "{SPREADSHEET_ID}". Use this ID for all sheet operations.\n'
'1. Comparative Analysis Policy:\n'
f' - Ingest historical POS data from the "POS-2025" sheet tab using read_spreadsheet_values with spreadsheet_id="{SPREADSHEET_ID}".\n'
' - Receive the current graduation schedule directly from the manager\'s prompt (the manager will paste it and indicate it is the same schedule sequence as last year).\n'
' - Write a python3 script via the sandbox tool to:\n'
' a. Correlate the 2025 product spikes (Cold Brew, Alt Milk, Extra Espresso) with the specific ceremonies ending at those times.\n'
' b. Map those beverage profiles to the pasted schedule (which is the same sequence) to predict exactly when and where the 2026 spikes will occur.\n'
' c. Identify expected wait-time bottlenecks in 2026 based on the 2025 wait times for those same profiles.\n'
'2. Bottleneck Diagnostics (Playbook):\n'
' - If a predicted high-volume slot in 2026 is expected to have Wait_Time_Minutes > 10:\n'
' - If Cashiers_Working < 2: Recommend scheduling another cashier.\n'
' - If Cashiers_Working == 2 and complex items (Cold Brew, Extra Espresso, Alt Milk) spike: Deduce that the bottleneck is barista output, not cashiers. Recommend adding a "Support Barista" role to handle fulfillment.\n'
'3. Human-in-the-Loop Policy:\n'
' - Present your detailed data discoveries, wait-time bottlenecks, and actionable recommendations (stocking and staffing changes) to the manager.\n'
' - Highlight only two or three findings for specific ceremonies.\n'
' - Frame your recommendations as a clean list of suggested tasks for the manager\'s TODO list.\n'
' - Explicitly ask: "Would you like me to add these tasks to your \'TODO-2026\' TODO list?"\n'
' - Do NOT modify any spreadsheet data until explicit approval is given.\n'
'4. Post-Approval Policy:\n'
f' - Upon receiving explicit user approval, first verify if the "TODO-2026" sheet tab exists in spreadsheet "{SPREADSHEET_ID}".\n'
f' - If the "TODO-2026" sheet tab does not exist, use the tool create_spreadsheet_tab to create it in spreadsheet "{SPREADSHEET_ID}".\n'
f' - Once the tab exists, use update_spreadsheet_values to append the approved adjustments as tasks to the "TODO-2026" sheet tab.\n'
' - Write the rows under the headers: Task (the actionable job, e.g., "Schedule a Support Barista role for Saturday morning"), Category ("Staffing" or "Inventory"), Ceremony, and Date_Added (today\'s date).\n'
' - Always confirm to the user exactly what tasks you have written to their "TODO-2026" TODO list.'
),
tools=[
FunctionTool(func=execute_sandbox_command),
FunctionTool(func=read_spreadsheet_values),
FunctionTool(func=update_spreadsheet_values),
FunctionTool(func=create_spreadsheet_tab)
]
)
adk_app = App(name="secure_sandbox_app", root_agent=root_agent)
runner = Runner(app=adk_app, session_service=InMemorySessionService(), auto_create_session=True)
app = FastAPI(title="Secure ADK Sandbox Assistant")
# ==========================================
# 4. ENDPOINTS & WEBSOCKET ROUTING
# ==========================================
@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
await websocket.accept()
active_connections.append(websocket)
await websocket.send_text("🔌 System: Connected. Agent is ready...")
try:
while True:
owner_reply = await websocket.receive_text()
print(f"Owner replied via WS: {owner_reply}")
await websocket.send_text("_Agent is running tools and thinking..._")
new_message = types.Content(parts=[types.Part(text=owner_reply)])
events = await asyncio.to_thread(
runner.run,
user_id="local_user",
session_id="local_session",
new_message=new_message
)
final_response = "".join(
part.text
for event in events
if event.content and event.content.parts
for part in event.content.parts
if part.text
) or "Agent completed execution updates without text output."
await websocket.send_text(final_response.strip())
except WebSocketDisconnect:
active_connections.remove(websocket)
class UserPrompt(BaseModel):
prompt: str
@app.post("/chat")
def chat_with_agent(payload: UserPrompt):
"""Fallback HTTP POST endpoint if UI is not used."""
try:
events = runner.run(
user_id="local_user",
session_id="local_session",
new_message=types.Content(parts=[types.Part(text=payload.prompt)])
)
final_response = "".join(
part.text
for event in events
if event.content and event.content.parts
for part in event.content.parts
if part.text
)
return {"status": "success", "response": final_response.strip()}
except Exception as e:
raise HTTPException(status_code=500, detail=f"Agent loop failed: {str(e)}")
@app.get("/", response_class=HTMLResponse)
async def get_chat_ui():
"""Serves the warm coffee-themed chat interface."""
return """
<!DOCTYPE html>
<html>
<head>
<title>Coffee Shop Agent</title>
<!-- Load marked.js for client-side markdown rendering -->
<script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
<style>
body { display: flex; height: 100vh; margin: 0; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; }
#sidebar { width: 250px; background: #3E2723; color: #EFEBE9; padding: 20px; }
#sidebar h2 { font-size: 1.3em; margin-top: 10px; color: #FFF; border-bottom: 1px solid #5D4037; padding-bottom: 10px; }
#main { flex-grow: 1; display: flex; flex-direction: column; background: #FAF8F6; }
#chat-history { flex-grow: 1; padding: 20px; overflow-y: auto; background: #F5EFEB; }
#input-area { padding: 20px; border-top: 1px solid #D7CCC8; background: #FAF8F6; display: flex;}
input {
flex-grow: 1;
padding: 12px;
border-radius: 6px;
border: 1px solid #D7CCC8;
margin-right: 10px;
font-size: 1em;
background: #FFF;
transition: border-color 0.2s, box-shadow 0.2s;
}
input:focus {
outline: none;
border-color: #8D6E63;
box-shadow: 0 0 0 2px rgba(141, 110, 99, 0.25);
}
button {
padding: 10px 24px;
background: #6D4C41;
color: white;
border: none;
border-radius: 6px;
cursor: pointer;
font-weight: bold;
font-size: 1em;
transition: background-color 0.2s, transform 0.1s;
}
button:hover {
background: #5D4037;
}
button:active {
background: #4E342E;
transform: scale(0.98);
}
.message { margin-bottom: 15px; padding: 12px 16px; border-radius: 8px; max-width: 85%; line-height: 1.5; }
.user-msg { background: #EFEBE9; color: #3E2723; align-self: flex-end; margin-left: auto; border: 1px solid #D7CCC8;}
.agent-msg { background: #fff; color: #3E2723; border: 1px solid #E0DCD8; box-shadow: 0 1px 3px rgba(62,39,35,0.06); }
.msg-header { display: flex; justify-content: space-between; align-items: center; margin-bottom: 6px; }
.agent-name { font-weight: bold; color: #5D4037; margin: 0; font-size: 0.95em;}
.user-name { font-weight: bold; color: #8D6E63; margin: 0; font-size: 0.95em;}
.msg-timestamp { font-size: 0.8em; color: #8D6E63; font-weight: normal; }
.day-divider {
display: flex;
align-items: center;
text-align: center;
color: #8D6E63;
margin: 20px 0;
font-size: 0.85em;
font-weight: bold;
}
.day-divider::before, .day-divider::after {
content: '';
flex: 1;
border-bottom: 1px solid #D7CCC8;
}
.day-divider:not(:empty)::before {
margin-right: .5em;
}
.day-divider:not(:empty)::after {
margin-left: .5em;
}
/* Markdown Styling inside Messages */
.message p { margin: 4px 0 8px 0; }
.message p:last-child { margin-bottom: 0; }
.message ul, .message ol { margin: 4px 0 8px 0; padding-left: 20px; }
.message li { margin-bottom: 3px; }
.message h1, .message h2, .message h3, .message h4 { margin: 12px 0 6px 0; font-size: 1.15em; color: #3E2723; font-weight: bold; }
.message h1:first-child, .message h2:first-child, .message h3:first-child { margin-top: 0; }
/* Table Styling */
.message table { border-collapse: collapse; width: 100%; margin: 10px 0; font-size: 0.95em; }
.message th, .message td { border: 1px solid #D7CCC8; padding: 8px 10px; text-align: left; }
.message th { background-color: #F5EFEB; font-weight: bold; color: #3E2723; }
.message tr:nth-child(even) { background-color: #FAF8F6; }
/* Code / Blockquote styling */
.message code { background: #EFEBE9; padding: 2px 4px; border-radius: 3px; font-family: monospace; font-size: 0.9em; color: #5D4037; }
.message pre { background: #F5EFEB; padding: 10px; border-radius: 5px; overflow-x: auto; margin: 8px 0; border: 1px solid #D7CCC8; }
.message pre code { background: none; padding: 0; }
.message blockquote { margin: 8px 0; padding-left: 12px; border-left: 4px solid #6D4C41; color: #5D4037; }
</style>
</head>
<body>
<div id="sidebar">
<h2>☕ Coffee Shop Monitor</h2>
<p>Monitoring sheet...</p>
</div>
<div id="main">
<div id="chat-history"></div>
<div id="input-area">
<input type="text" id="msg" placeholder="Message Coffee Shop Monitor..." onkeypress="if(event.key === 'Enter') sendMessage()">
<button onclick="sendMessage()">Send</button>
</div>
</div>
<script>
const protocol = window.location.protocol === 'https:' ? 'wss:' : 'ws:';
const ws = new WebSocket(`${protocol}//${window.location.host}/ws`);
const history = document.getElementById('chat-history');
// Scroll to the bottom on load to show latest messages
history.scrollTop = history.scrollHeight;
ws.onmessage = function(event) {
// Parse markdown content from the agent
const parsedHtml = marked.parse(event.data);
const timeStr = new Date().toLocaleTimeString([], { hour: '2-digit', minute: '2-digit' });
history.innerHTML += `
<div class="message agent-msg">
<div class="msg-header">
<span class="agent-name">Inventory Agent APP</span>
<span class="msg-timestamp">${timeStr}</span>
</div>
<div>${parsedHtml}</div>
</div>`;
history.scrollTop = history.scrollHeight;
};
function sendMessage() {
const input = document.getElementById('msg');
const text = input.value;
if (!text) return;
// Parse user markdown if any (optional, but keeps styling consistent)
const parsedHtml = marked.parse(text);
const timeStr = new Date().toLocaleTimeString([], { hour: '2-digit', minute: '2-digit' });
history.innerHTML += `
<div class="message user-msg">
<div class="msg-header">
<span class="user-name">You</span>
<span class="msg-timestamp">${timeStr}</span>
</div>
<div>${parsedHtml}</div>
</div>`;
ws.send(text);
input.value = '';
history.scrollTop = history.scrollHeight;
}
</script>
</body>
</html>
"""
if __name__ == "__main__":
import uvicorn
port_val = int(os.environ.get('PORT', 8080))
uvicorn.run(app, host='0.0.0.0', port=port_val)
5. Men-deploy layanan Cloud Run
Daripada membangun image, Anda akan menggunakan buildpack Google Cloud untuk mengubah kode sumber aplikasi Anda menjadi image container yang siap produksi untuk Cloud Run.
gcloud beta run deploy coffee-mgr-agent \
--source=. \
--region=$REGION \
--sandbox-launcher \
--max-instances=1 \
--session-affinity \
--allow-unauthenticated \
--no-cpu-throttling \
--set-env-vars GOOGLE_GENAI_USE_VERTEXAI=1,GOOGLE_CLOUD_PROJECT=$GOOGLE_CLOUD_PROJECT,GOOGLE_CLOUD_LOCATION=global,SPREADSHEET_ID=$SPREADSHEET_ID \
--service-account $SERVICE_ACCOUNT_ADDRESS
6. Mulai chat dengan agen Anda
Setelah deployment, Anda akan melihat URL: https://coffee-mgr-agent-YOUR_PROJECT_ID.YOUR_REGION.run.app
Buka URL ini di browser Anda.
Kirim perintah berikut ke agen Anda.
The 2026 graduation schedule was just posted. It's the same schedule as last year. Can you review last year's POS data to help me prepare for this year?
Saturday, June 13:
College of Business (8:30 a.m.)
College of Science and Mathematics (12:30 p.m.)
College of Liberal Arts (4:30 p.m.)
Sunday, June 14:
College of Agriculture (8:30 a.m.)
College of Architecture (12:30 p.m.)
College of Engineering (4:30 p.m.)
Agen Anda akan merespons dengan rekomendasi yang mirip dengan berikut ini:
Based on the 2025 POS data, I have correlated graduation ceremony patterns with beverage demand spikes. Last year, the post-ceremony windows consistently experienced high demand for complex items (Cold Brew, Extra Espresso, and Alt Milk). Key Data Discoveries for 2026 - College of Business (Saturday 8:30 a.m. ceremony): - Significant demand spike observed at 10:00 a.m. Saturday. - Data: 130 Cold Brews and 95 Extra Espresso shots. This is a primary bottleneck for barista output. - College of Engineering (Sunday 4:30 p.m. ceremony): - Significant demand spike observed at 6:00 p.m. Sunday. - Data: 145 Cold Brews and 110 Extra Espresso shots. This is the highest intensity period of the weekend. - Alt-Milk Surge (Saturday/Sunday 2:00 p.m. post-ceremonies): - Consistent, high-volume Alt Milk consumption (65-180 oz) after the 12:30 p.m. ceremonies. Diagnostic & Staffing Recommendations Given that these complex beverage items (Cold Brew, Extra Espresso) correlate directly with your high-traffic post-ceremony windows, the risk of hitting wait times > 10 minutes is high. Assuming you typically have 2 cashiers, my diagnostic is that these are barista output bottlenecks, not cashier bottlenecks. Suggested Tasks for your TODO-2026 List: 1. Staffing: Schedule a dedicated "Support Barista" for Saturday 9:30 a.m. – 11:00 a.m. to handle the Cold Brew/Espresso rush. 2. Staffing: Schedule a dedicated "Support Barista" for Sunday 5:30 p.m. – 7:00 p.m. to manage the high volume after the Engineering ceremony. 3. Inventory: Pre-stage extra Alt Milk cartons at the mobile station for Saturday 1:30 p.m. and Sunday 1:30 p.m. shifts. Would you like me to add these tasks to your 'TODO-2026' TODO list?
Kemudian, agen akan meminta izin Anda (Human-in-the-Loop) untuk membuat/memperbarui sheet tab TODO-2026 Anda.
Beri respons agen Anda dengan Yes.
Sekarang, saat melihat sheet tab TODO-2026, Anda akan melihat sesuatu yang mirip dengan berikut ini:
Task Category Ceremony Date_Added Schedule a Support Barista role for Saturday morning rush Staffing College of Business 2025-05-22 Schedule a Support Barista role for Sunday evening rush Staffing College of Engineering 2025-05-22 Pre-stage extra Alt Milk cartons at mobile station Inventory College of Science/Math & Architecture 2025-05-22
7. Selamat!
Selamat, Anda telah menyelesaikan codelab.
Sebaiknya tinjau dokumentasi Cloud Run, dokumentasi sandbox Cloud Run, dan dokumentasi ADK.
Yang telah kita bahas
- Cara membuat layanan Cloud Run
- Cara men-deploy agen ADK di layanan Cloud Run
- Cara membuat agen menjalankan kode di sandbox dalam layanan Cloud Run
8. Pembersihan
Agar tidak menimbulkan biaya pada akun Google Cloud Anda untuk resource yang digunakan dalam tutorial ini, Anda dapat menghapus project atau menghapus setiap resource.
Opsi 1: Menghapus Resource
Berikut ringkasan singkat langkah-langkah untuk membersihkan project dan menghindari biaya yang tidak perlu.
Menghapus Layanan Cloud Run
gcloud run services delete coffee-mgr-agent --region $REGION
Hapus Akun Layanan: Hapus akun layanan spreadsheet khusus
gcloud iam service-accounts delete $SERVICE_ACCOUNT_ADDRESS
Opsi 2: Menghapus Project
Untuk menghapus seluruh project, buka Manage Resources, pilih project yang Anda buat di Langkah 2, lalu pilih Delete. Jika menghapus project, Anda harus mengubah project di Cloud SDK. Anda dapat melihat daftar semua project yang tersedia dengan menjalankan gcloud projects list. Jika ingin tetap menggunakan command line, Anda juga dapat menggunakan perintah ini:
gcloud projects delete ${GOOGLE_CLOUD_PROJECT}