Conduct periodic access reviews and certifications. Implement access governance and recertification workflows. Use when managing access compliance.
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Deploy serverless functions on Google Cloud Functions. Configure triggers and manage deployments. Use when implementing serverless workloads on GCP.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Build and deploy event-driven serverless applications with Google Cloud Functions (Gen1 and Gen2).
gcloud) installed and authenticatedroles/cloudfunctions.developer (or roles/run.developer for Gen2)gcloud services enable cloudfunctions.googleapis.com cloudbuild.googleapis.com \
artifactregistry.googleapis.com run.googleapis.com eventarc.googleapis.com
| Feature | Gen1 | Gen2 (recommended) |
|---|---|---|
| Runtime | Cloud Functions infra | Built on Cloud Run |
| Max timeout | 9 minutes | 60 minutes |
| Max memory | 8 GB | 32 GB |
| Concurrency | 1 request/instance | Up to 1000/instance |
| Traffic splitting | No | Yes |
| Eventarc triggers | No | Yes |
# Python HTTP function
gcloud functions deploy hello-http \
--gen2 --region=us-central1 --runtime=python312 \
--trigger-http --allow-unauthenticated \
--entry-point=hello_http \
--memory=256Mi --timeout=60s \
--min-instances=0 --max-instances=100 \
--set-env-vars=APP_ENV=production --source=.
# Node.js HTTP function
gcloud functions deploy hello-node \
--gen2 --region=us-central1 --runtime=nodejs20 \
--trigger-http --allow-unauthenticated \
--entry-point=helloNode --memory=256Mi --source=.
gcloud pubsub topics create order-events
gcloud functions deploy process-order \
--gen2 --region=us-central1 --runtime=python312 \
--trigger-topic=order-events \
--entry-point=process_order \
--memory=512Mi --timeout=120s --retry \
--service-account=order-processor@${PROJECT_ID}.iam.gserviceaccount.com \
--source=.
gcloud functions deploy process-upload \
--gen2 --region=us-central1 --runtime=python312 \
--trigger-event-filters="type=google.cloud.storage.object.v1.finalized" \
--trigger-event-filters="bucket=my-upload-bucket" \
--entry-point=process_upload \
--memory=1Gi --timeout=300s --source=.
gcloud functions deploy daily-cleanup \
--gen2 --region=us-central1 --runtime=python312 \
--trigger-http --no-allow-unauthenticated \
--entry-point=daily_cleanup --source=.
gcloud scheduler jobs create http daily-cleanup-job \
--schedule="0 2 * * *" \
--uri="https://us-central1-${PROJECT_ID}.cloudfunctions.net/daily-cleanup" \
--http-method=POST \
--oidc-service-account-email=scheduler-sa@${PROJECT_ID}.iam.gserviceaccount.com \
--location=us-central1
# main.py
import functions_framework
import base64, json
from flask import jsonify
from google.cloud import firestore
@functions_framework.http
def hello_http(request):
"""HTTP Cloud Function."""
name = request.args.get("name", "World")
return jsonify({"message": f"Hello, {name}!", "status": "ok"}), 200
@functions_framework.cloud_event
def process_order(cloud_event):
"""Triggered by a Pub/Sub message."""
data = base64.b64decode(cloud_event.data["message"]["data"]).decode("utf-8")
order = json.loads(data)
db = firestore.Client()
db.collection("orders").document(order["id"]).set({
"status": "processing", "items": order["items"], "total": order["total"],
})
@functions_framework.cloud_event
def process_upload(cloud_event):
"""Triggered when a file is uploaded to Cloud Storage."""
data = cloud_event.data
bucket_name, file_name = data["bucket"], data["name"]
if not file_name.lower().endswith((".png", ".jpg", ".jpeg")):
return
from google.cloud import vision
client = vision.ImageAnnotatorClient()
image = vision.Image(source=vision.ImageSource(
gcs_image_uri=f"gs://{bucket_name}/{file_name}"))
labels = [l.description for l in client.label_detection(image=image).label_annotations]
print(f"Labels for {file_name}: {labels}")
# requirements.txt
functions-framework==3.*
google-cloud-firestore==2.*
google-cloud-storage==2.*
google-cloud-vision==3.*
flask>=2.0
// index.js
const functions = require("@google-cloud/functions-framework");
functions.http("helloNode", (req, res) => {
const name = req.query.name || "World";
res.json({ message: `Hello, ${name}!`, status: "ok" });
});
functions.cloudEvent("processMessage", (cloudEvent) => {
const data = Buffer.from(cloudEvent.data.message.data, "base64").toString();
console.log(`Processing: ${JSON.parse(data)}`);
});
gcloud functions list --gen2 --region=us-central1
gcloud functions describe hello-http --gen2 --region=us-central1
gcloud functions logs read hello-http --gen2 --region=us-central1 --limit=50
gcloud functions delete hello-http --gen2 --region=us-central1 --quiet
# Update env vars without redeploying code
gcloud functions deploy hello-http --gen2 --region=us-central1 \
--update-env-vars=APP_ENV=staging
# Test locally before deploying
functions-framework --target=hello_http --port=8080
resource "google_cloudfunctions2_function" "api" {
name = "hello-http"
location = "us-central1"
build_config {
runtime = "python312"
entry_point = "hello_http"
source {
storage_source {
bucket = google_storage_bucket.source.name
object = google_storage_bucket_object.source.name
}
}
}
service_config {
min_instance_count = 0
max_instance_count = 100
available_memory = "256Mi"
timeout_seconds = 60
service_account_email = google_service_account.fn.email
environment_variables = { APP_ENV = "production" }
}
}
resource "google_cloud_run_service_iam_member" "invoker" {
location = google_cloudfunctions2_function.api.location
service = google_cloudfunctions2_function.api.name
role = "roles/run.invoker"
member = "allUsers"
}
resource "google_cloudfunctions2_function" "processor" {
name = "process-order"
location = "us-central1"
build_config {
runtime = "python312"
entry_point = "process_order"
source {
storage_source {
bucket = google_storage_bucket.source.name
object = google_storage_bucket_object.source.name
}
}
}
service_config {
max_instance_count = 50
available_memory = "512Mi"
timeout_seconds = 120
service_account_email = google_service_account.fn.email
}
event_trigger {
trigger_region = "us-central1"
event_type = "google.cloud.pubsub.topic.v1.messagePublished"
pubsub_topic = google_pubsub_topic.orders.id
retry_policy = "RETRY_POLICY_RETRY"
}
}
| Symptom | Cause | Fix |
|---|---|---|
PERMISSION_DENIED on deploy | Missing Cloud Build or Artifact Registry perms | Grant roles/cloudbuild.builds.builder to Cloud Build SA |
| Function deploys but returns 403 | Missing roles/run.invoker for Gen2 | Add --allow-unauthenticated or grant invoker role |
| Cold start latency > 5s | Large dependencies or no min instances | Set --min-instances=1; reduce deps; use lazy imports |
| Pub/Sub messages redelivered | Function errors or times out | Increase --timeout; fix error handling; add dead-letter topic |
Build failed during deploy | Syntax error or missing dependency | Check gcloud builds log; verify requirements.txt |
| Cannot connect to VPC resource | Function not on VPC connector | Add --vpc-connector=my-connector to deploy |
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Conduct periodic access reviews and certifications. Implement access governance and recertification workflows. Use when managing access compliance.
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Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. Use when shipping agent features, validating prompt changes, or gating deployments on quality.
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Instrument AI agents with tracing, token metrics, latency, and cost visibility. Use for reliability and debugging.
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Secure AI agents against prompt injection, tool abuse, and data exfiltration with defense-in-depth controls. Use when building, deploying, or hardening agentic AI systems that invoke tools, access data, or interact with production infrastructure.
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Secure AI coding agents (Claude Code, Cursor, Codex, Copilot) with permission boundaries, secret protection, code review gates, and safe sandbox configurations for team environments.
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Use service mesh patterns for AI inference traffic management, mTLS, canary releases, policy enforcement, and cross-cluster resilience.
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