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Google Cloud Solution Multi Agent Security

GoogleOfficialPublished by Google

  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
  • Low risk
  • Has scripts

From the author

Designs, deploys, and secures Google Cloud Agent Gateway solutions. Use when the user needs to configure multi-agent security, ingress (CLIENT_TO_AGENT), or egress (AGENT_TO_ANYWHERE) patterns involving Model Armor, IAP, and Agent Registry. Don't use for general Cloud Load Balancing or basic VPC setup not related to Agent Gateways.
— the description by Google

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach to designing, deploying, and securing Google Cloud Agent Gateway solutions. It covers the configuration of ingress (CLIENT_TO_AGENT) and egress (AGENT_TO_ANYWHERE) patterns, integrating essential security components such as Model Armor, Identity-Aware Proxy (IAP), and Agent Registry. The skill includes templates for configuration files, infrastructure deployment scripts, and validation procedures to ensure secure communication between agents and external services.

What it does

  • Generates configuration files for Agent Gateway ingress and egress policies.
  • Provides infrastructure deployment commands for Google Cloud services.
  • Configures Model Armor guardrails for content authorization and filtering.
  • Sets up IAP and Authz policies to manage agent identity and access.
  • Offers troubleshooting steps for common ingress and egress connectivity issues.
  • Supports complex routing scenarios including Hybrid VPN and GKE internal load balancing.

When to use it

  • When you need to secure multi-agent communication patterns in Google Cloud.
  • When configuring ingress or egress gateways for Agent Engine workloads.
  • When implementing Model Armor guardrails to filter agent prompts and responses.
  • When setting up private connectivity between agents and internal MCP tool backends.

Advantages

  • Ensures consistent security posture across multi-agent architectures.
  • Provides ready-to-use configuration templates and deployment scripts.
  • Includes specific validation steps to verify security policy enforcement.
  • Addresses complex networking requirements like PSC and DNS peering.

Usage examples

“Design a secure ingress and egress architecture for my multi-agent system.”
The agent provides a Mermaid diagram of the architecture and generates the necessary YAML configuration files for ingress and egress gateways.
“How do I configure Model Armor for my Agent Gateway?”
The agent generates the model-armor-config.yaml file with required filters and provides the commands to apply it to the gateway.
“My agent is getting a 403 error when accessing an egress tool.”
The agent provides troubleshooting steps to verify IAM bindings for roles/iap.egressor and checks the CEL conditions on the tool metadata.
“Generate the configuration for a multi-agent Cloud Run egress setup.”
The agent creates the specific egress gateway configuration, registers the services in the Agent Registry, and provides the updated IAP policy.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/google/google-cloud-solution-multi-agent-security/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip google-cloud-solution-multi-agent-security.zip -d .agents/skills

Documentation by Google

Agent Gateway multi-agent security

Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read and follow the instructions in Usage Attribution.

Critical Enforcement Rules & Rationale

  • Gcloud Release Tracks: Always use the exact release tracks specified in the commands (e.g., gcloud beta network-services agent-gateways). Omitting these prefixes causes commands to fail because Agent Gateway features are located in specialized, non-default namespaces.

  • API Enablement: Include modelarmor.googleapis.com in the API enablement list when setting up guardrails. Excluding it prevents Model Armor policies and filters from successfully attaching to the Gateway.

  • Egress Verification: Egress policy verification requires using the Python script (scripts/verify_egress_policies.py), not curl. Egress gateways rely on runtime SDK lifecycle handling and JWT context that a standard curl command cannot simulate correctly.

  • Model Armor Keys: In model-armor-config.yaml, always include both piAndJailbreakFilterSettings and sdpFilterSettings (filterEnforcement: ENFORCE). Invalid or missing filters cause deployment validation failures or lead to silent bypasses of the guardrails.

  • Subnet Private Access: Any subnet hosting a Private Service Connect network attachment for Egress Gateways must have private_ip_google_access = true enabled in Terraform. Disabling this blocks connectivity to Google-managed endpoints, causing total routing failures for agents.

  • Direct Delivery: Immediately provide the requested architecture, configuration files, CLI commands, scripts, and diagrams in full. Do not stop at a planning phase, do not generate a plan artifact, and do not ask for user confirmation before delivering outputs.

  • No Infrastructure Execution: Do not attempt to run deployment or verification commands (such as gcloud, kubectl, terraform, or curl) against real cloud resources during design. You are generating plan configurations, not executing them.

[!IMPORTANT] Just-In-Time (JIT) Resource Loading Protocol: Inspect template files in assets/ and executable scripts in scripts/ using view_file as needed for extended configurations, deployment scripts, and test suites.


Quick Reference: Required Filenames

Always generate files with these exact names when requested:

  1. agw-ingress-config.yaml (assets/agw-ingress-config.yaml)

  2. agw-egress-config.yaml (assets/agw-egress-config.yaml)

  3. agw-authz-extension.yaml (assets/agw-authz-extension.yaml)

  4. agw-authz-policy.yaml (assets/agw-authz-policy.yaml)

  5. model-armor-config.yaml (assets/model-armor-config.yaml)

  6. sgp-policy.yaml (assets/sgp-policy.yaml)

  7. iap-policy.json (assets/iap-policy.json)

  8. model-armor-payload.json (assets/model-armor-payload.json)


1. Dual Ingress & Egress Architecture Design (dual_ingress_egress_architecture_design)

  • Ingress Pattern: CLIENT_TO_AGENT fronted by Ingress Control Plane (Agent Gateway, Model Armor).

  • Egress Pattern: AGENT_TO_ANYWHERE utilizing Egress Control Plane (Agent Gateway, roles/iap.egressor CEL policies, Cloud DNS) and Egress Data Plane (PSC Interface, Cloud Run, PSC Google APIs Global Endpoint), coordinated via Agent Registry & Agent Engine runtime.

  • Mermaid Diagram:

    graph TD
        Client["External Clients"] -->|HTTPS / MCP| GLB["Global Load Balancer"]
        GLB --> Ingress["Ingress Agent Gateway (CLIENT_TO_AGENT)"]
        Ingress --> MA["Model Armor (CONTENT_AUTHZ)"]
        MA --> Agent["Agent Engine Agents (BillingAgent, SupportAgent, FraudAgent)"]
        Agent --> Egress["Egress Agent Gateway (AGENT_TO_ANYWHERE)"]
        Egress --> PSC["Private Service Connect Network Attachment"]
        PSC --> Tools["Private MCP Tool Backends"]

2. Ingress & Egress Guardrail Policy Config (ingress_and_egress_guardrail_policy_config)

When requested for Ingress & Egress guardrail policy configs, you MUST generate and create all required files in the workspace:

  • agw-ingress-config.yaml (assets/agw-ingress-config.yaml): Declares governedAccessPath: CLIENT_TO_AGENT with protocols HTTP and MCP.

  • agw-egress-config.yaml (assets/agw-egress-config.yaml): Declares governedAccessPath: AGENT_TO_ANYWHERE with protocol MCP.

  • agw-authz-extension.yaml (assets/agw-authz-extension.yaml): Configures AuthzExtension service for IAP authorization.

  • agw-authz-policy.yaml (assets/agw-authz-policy.yaml): Configures AuthzPolicy action ALLOW targeting both Ingress and Egress gateways.

  • iap-policy.json (assets/iap-policy.json): Binds roles/iap.egressor with CEL condition checking iap.googleapis.com/mcp.toolName == 'get_account_balance' && iap.googleapis.com/mcp.tool.isReadOnly == true.

  • model-armor-config.yaml (assets/model-armor-config.yaml): Enables piAndJailbreakFilterSettings and sdpFilterSettings with filterEnforcement: ENFORCE.

  • sgp-policy.yaml (assets/sgp-policy.yaml): Implements Natural Language Constraints blocking transactions > $1000 and sanitizing PII.


3. Ingress & Egress Infrastructure Deployment (ingress_and_egress_infrastructure_deployment)

Inspect and provide the step-by-step gcloud CLI commands from scripts/deploy_infrastructure.sh:

  1. Enable Required APIs: compute, networkservices, networksecurity, modelarmor, iap, agentregistry, serviceextensions, and aiplatform.

  2. Import Agent Gateways: Ingress (agw-ingress-config.yaml) and Egress (agw-egress-config.yaml) via gcloud alpha network-services agent-gateways import.

  3. Import Authz Extension: agw-authz-extension.yaml via gcloud beta service-extensions authz-extensions import.

  4. Import Authz Policy: agw-authz-policy.yaml via gcloud beta network-security authz-policies import.


4. Ingress & Egress Security Validation (ingress_and_egress_security_validation)

When validating security for Ingress and Egress:

  1. Ingress 403 Unauthenticated Test: Provide the copy-pasteable verification curl command from scripts/validate_ingress_unauth.sh sending an unauthenticated POST request to the Reasoning Engine endpoint expecting HTTP 403 Forbidden.

  2. Python Egress Verification Script (MUST use Python script snippet, NOT curl): Provide the Python verification script snippet from scripts/verify_egress_policies.py sending JSON-RPC tools/call requests (get_account_balance) through the Egress Gateway to verify HTTP 200 for allowed tools.

  3. Model Armor Test Payload: Generate model-armor-payload.json (assets/model-armor-payload.json) containing prompt injection/jailbreak instructions.


5. Troubleshooting Ingress & Egress Failures (troubleshooting_ingress_and_egress_failures)

  • Ingress 403 (Client-to-Agent):

    • Root Cause: Unauthenticated client requests or missing/invalid OAuth 2.0 / IAP identity tokens.

    • OAuth Configuration Steps:

      1. Configure OAuth 2.0 Client ID credentials in Google Cloud Console.

      2. Grant the client identity / service account roles/iap.httpsResourceAccessor permission.

      3. Exchange credentials with Google OAuth to acquire an OIDC / OAuth ID token.

      4. Pass the token in the Authorization: Bearer <TOKEN> header.

    • Verification Command: Provide the curl command from scripts/verify_ingress_auth.sh.

  • Egress 403 (Agent-to-Anywhere):

    • Root Cause: Missing roles/iap.egressor IAM bindings on the Agent Identity, malformed principal ID, or mismatched CEL condition on tool metadata.

    • Fix Command: Provide the exact gcloud command from scripts/fix_egress_iap.sh.


6. Hybrid VPN Connectivity & Egress Routing (hybrid_vpn_connectivity_egress_routing)

  • Terraform HCL: Refer to baseline Terraform config in assets/main.tf for VPC, subnets (private_ip_google_access = true), PSC network attachment, Cloud DNS private forwarding for aws.internal., and HA VPN gateway/router.

  • Egress Gateway Config (agw-egress-config.yaml): Generate configuration declaring governedAccessPath: AGENT_TO_ANYWHERE, pointing to the PSC network attachment, and referencing aws.internal. in dnsPeeringConfig (see assets/agw-egress-config.yaml).

  • Python SDK Deployment Script: Refer to scripts/hybrid_vpn_agent.py for the complete script initializing Vertex AI with agent_to_anywhere_config referencing the Egress Gateway, enabling telemetry, and deploying HybridAgent using types.IdentityType.AGENT_IDENTITY.


7. Private Egress GKE Internal Load Balancer (private_egress_gke_internal_load_balancer)

  • Expose GKE internal MCP tool server via an Internal Load Balancer (ILB) at literal IP 10.0.1.50, connecting via Agent Gateway PSC Interface + Cloud DNS Private zone.

  • Cloud DNS Record Mapping: Provide the command from scripts/create_gke_dns_record.sh mapping the private domain to GKE's private ILB IP 10.0.1.50.

  • Explicit TLS Warning: Agent Gateway egress does not natively trust self-signed certificates or private enterprise CAs. You must use publicly trusted TLS certificates signed by a trusted Certificate Authority (e.g., Let's Encrypt).


8. Governance Controls Model Armor SGP (governance_controls_model_armor_sgp)

When configuring dual safety layers with Model Armor on Ingress and SGP on Egress:

  1. Model Armor Config: Generate model-armor-config.yaml (assets/model-armor-config.yaml) with piAndJailbreakFilterSettings and sdpFilterSettings (filterEnforcement: ENFORCE).

  2. Semantic Governance Policy: Generate sgp-policy.yaml (assets/sgp-policy.yaml) with Natural Language Constraints blocking transactions > $1000 and sanitizing PII.

  3. Curl PATCH Command: Provide the curl command from scripts/enforce_sgp_patch.sh to update authzExtensions with sgpEnforcementMode set to ENFORCE.


9. Multi-Agent Cloud Run Egress Routing (multi_agent_cloud_run_egress_routing)

Do NOT produce a plan artifact or stop at planning. When configuring multi-agent Cloud Run egress routing, you MUST directly provide and generate ALL required components:

  1. Egress Gateway Config (agw-egress-config-run.yaml): Generate configuration declaring governedAccessPath: AGENT_TO_ANYWHERE, PSC network attachment, and DNS peering for *.run.app (see assets/agw-egress-config-run.yaml).

  2. Register Cloud Run Services in Agent Registry: Provide the registration commands from scripts/register_cloud_run_services.sh registering all 3 Cloud Run services (marketing-tool-service, sales-tool-service, support-tool-service) in the us-east4 Agent Registry.

  3. iap-policy.json (Multi-Agent): Generate iap-policy.json (assets/iap-policy-multi-agent.json) containing all 3 principal:// bindings in the members list under roles/iap.egressor.

  4. Python SDK Deployment Script: Refer to scripts/multi_agent_cloud_run.py for the complete GenAI SDK deployment script.


10. Advanced Model Armor Filtering (advanced_model_armor_filtering)

For custom keyword matching, configure userDefinedFilterSettings (see assets/model-armor-advanced.yaml).


11. Known Traps & Gotchas (known_traps_and_gotchas)

  • network_attachment is ForceNew: Enabling Semantic Governance Policies (SGP) or modifying network attachments after the initial Terraform apply will force-recreate the gateway resource. If not managed carefully, this can cause dependency deadlocks during destroy operations. Plan infrastructure sequencing accordingly.

  • Authz Policy Limit: An Agent Gateway allows at most 4 custom authorization policies attached concurrently. Ensure your security posture consolidates rules within this limit.

Files

scripts/multi_agent_cloud_run.py

"""Example Python SDK configuration for multi-agent Cloud Run egress."""

from google.adk.agents import llm_agent
from google.adk.integrations import agent_registry
import vertexai
from vertexai import agent_engines
from vertexai import types

# Initialize Vertex AI client with v1beta1 API for Agent Engine & Identity
client = vertexai.Client(
    project="{project_id}",
    location="us-east4",
    http_options=dict(api_version="v1beta1"),
)

# 1. Resolve MCP Toolset from Agent Registry
registry = agent_registry.AgentRegistry(
    project_id="{project_id}", location="us-east4"
)
servers = registry.list_mcp_servers(
    filter_str='displayName="Marketing Tool Service"'
)["mcpServers"]
server_name = servers[0]["name"]
marketing_toolset = registry.get_mcp_toolset(server_name)

# 2. Define ADK Agent
agent = llm_agent.LlmAgent(
    model="gemini-2.5-flash",
    name="marketing_agent",
    instruction="You are a marketing assistant.",
    tools=[marketing_toolset],
)
agent_instance = agent_engines.AdkApp(agent=agent)

# 3. Configure Egress Gateway routing
agent_gateway_config = {
    "agent_to_anywhere_config": {
        "agent_gateway": (
            "projects/{project_number}/locations/us-east4/"
            "agentGateways/agw-tools-run"
        )
    }
}

# 4. Deploy Agent Engine via client.agent_engines.create
remote_app = client.agent_engines.create(
    agent=agent_instance,
    config={
        "display_name": "MarketingAgent",
        "agent_gateway_config": agent_gateway_config,
        "identity_type": types.IdentityType.AGENT_IDENTITY,
        "staging_bucket": "gs://{staging_bucket}",
        "requirements": [
            "google-cloud-aiplatform[adk,agent_engines]",
            "google-adk[mcp]==2.4.0",
        ],
    },
)

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

What our scanner noticed· Low risk

  • Scripts that your agent may run

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Attribution and licence

Published by
Google
Publisher
Google (Official)
Source
google/skills · skills/cloud/google-cloud-solution-multi-agent-security · commit 2194d3b
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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