AI Agents & Multi-Agent Systems

Concept

AI agents use an LLM to plan and call tools in a loop to accomplish goals. Multi-agent systems coordinate several specialized agents. On GCP you build them with the Agent Development Kit (ADK) and run them on Agent Engine or Cloud Run.

Problem it solves. Agents handle open-ended tasks that need tool use, retrieval, and multi-step reasoning — but need guardrails, evaluation, and loop/cost controls to be production-safe.

Limitations

• Service quotas and limits apply — always check current documentation
• Regional availability and launch stage vary by feature
• Cost scales with usage; model it before committing

Interactive architecture

flowchart LR
  U[User / Client] --> LB[External Load Balancer]
  LB --> SVC["AI Agents & Multi-Agent Systems"]
  SVC --> DB[(Database)]
  SVC --> LOG[Cloud Logging]
  SVC --> MON[Cloud Monitoring]
  classDef gcp fill:#e8f0fe,stroke:#4285f4,color:#174ea6;
  class SVC,DB,LOG,MON,LB gcp;

flowchart LR
  U[User / Client] --> LB[External Load Balancer]
  LB --> SVC["AI Agents & Multi-Agent Systems"]
  SVC --> DB[(Database)]
  SVC --> LOG[Cloud Logging]
  SVC --> MON[Cloud Monitoring]
  classDef gcp fill:#e8f0fe,stroke:#4285f4,color:#174ea6;
  class SVC,DB,LOG,MON,LB gcp;

How it works

The sections below break AI Agents & Multi-Agent Systems down into request/event flow, authentication and authorization, the internal actions Google Cloud performs, and the failure and monitoring signals you should watch in production.

1. 1 · Request or event arrivesEntry point (load balancer, trigger, or API)

   Traffic reaches AI Agents & Multi-Agent Systems through a defined entry point with TLS termination and, where relevant, Cloud Armor protection.

   ⚠ 429/5xx if quotas or backends are exhausted 📈 Request count, latency, and error-rate metrics 💰 Ingress is generally free; egress and requests are billed
2. 2 · AuthenticationIAM / identity layer

   The caller's identity (user, service account, or federated workload) is verified before any work is done.

   ⚠ 401/403 on invalid or missing credentials 📈 Cloud Audit Logs (Admin & Data Access)
3. 3 · AuthorizationIAM policy evaluation

   IAM checks whether the principal holds a role granting the required permission on the resource, honoring inheritance and deny policies.

   ⚠ 403 PERMISSION_DENIED 📈 Policy Analyzer, audit logs
4. 4 · Service performs the workAI Agents & Multi-Agent Systems

   The service executes the requested action, reading or writing data and emitting structured logs and traces.

   ⚠ Retryable vs non-retryable errors; watch for partial failure 📈 Structured logs, Cloud Trace spans 💰 Compute/storage/IO billed per the pricing model
5. 5 · Response / acknowledgementCaller

   A result is returned synchronously, or an acknowledgement/notification is emitted for asynchronous flows.

   📈 SLI dashboards, error budgets

Hands-on example

Cloud Console

1. Open the Cloud Console and navigate to the AI Agents & Multi-Agent Systems section
2. Enable the required API(s) for your project
3. Create the resource with least-privilege settings
4. Verify it appears and note its identifiers

Code

# Set project context
gcloud config set project MY_PROJECT

# Enable required APIs (replace with the service's API)
gcloud services enable SERVICE.googleapis.com

# Create / describe the resource (see per-service docs for exact flags)
# gcloud <group> create NAME --region=us-central1

# Verify
# gcloud <group> describe NAME --region=us-central1

# Uses Application Default Credentials (no service-account keys).
# pip install google-cloud-<service>
from google.cloud import <service>  # replace with the concrete client

def main() -> None:
    client = <service>.Client()
    # ... call the API using the client, letting ADC handle auth ...
    print("connected")

if __name__ == "__main__":
    main()

terraform {
  required_providers {
    google = { source = "hashicorp/google", version = "~> 6.0" }
  }
}

provider "google" {
  project = var.project_id
  region  = var.region
}

# resource "google_..." "this" {
#   name     = "example"
#   # least-privilege, private-by-default configuration
# }

Troubleshooting

• 403 → check IAM roles and the caller's identity
• Connectivity failures → check VPC, firewall, and Private Google Access
• Unexpected cost → check billing exports and Recommender

Production considerations

What interviewers expect you to know

Common mistakes

• Granting overly broad IAM roles (e.g. project-level Owner/Editor)
• Using service-account keys where Workload Identity Federation would work
• Exposing services publicly when they should be private
• Choosing the wrong compute or storage primitive for the access pattern
• Retrying without backoff, or ignoring idempotency
• Ignoring quotas, dead-letter handling, and structured logging
• Leaving demo resources running and ignoring data-transfer costs
• Mixing production and development in one project

Knowledge check