Agent engine instance

[Adiideas]

Hi,

How do we ensure compute isolation in agent engine?

scenario- If i have 3 different agents running on same engine, how do we ensure there is no data or compute overlap and each has its own compute space?

can multiple engine instances be provisioned in single gcp project?

if no, does engine support namespace seggregation?

thanks

[Joy_S]

Hi @Adiideas,

Welcome to Google Cloud Community!

To answer your question, yes, you can. You can have Multiple VMs, Multiple GKE clusters or Multiple agent deployments (even in the same cluster, using namespaces) in one GCP project. When we say "Agent Engine" in Google Cloud, we’re really just talking about the system that runs your agents behind the scenes—usually powered by Vertex AI. Even if multiple agents run on the same setup, Google Cloud gives you tools to keep their compute and data separate. How you set that up depends on how much isolation you need and which GCP tools you’re using. Here are the main ways GCP helps keep things separate:

• Virtual Machines (VMs): If each agent runs on its own VM, GCP gives strong isolation—each one has its own operating system, CPU, and memory. They’re completely separate.
  
• Containers and Kubernetes (GKE): If you're using containers (common for AI stuff), GKE (Google Kubernetes Engine) usually runs the show. Inside a GKE cluster, you can split things up using namespaces. Each agent can run in its own namespace, and you can limit how much CPU and memory each one gets.
  
• Storage Separation: Each agent should use its own storage (like a separate Cloud Storage bucket, its own BigQuery dataset, or Cloud SQL database). This keeps data from getting mixed up.
  
• Permissions (IAM): Each agent should have its own service account with only the permissions it needs. This way, agents can't access each other’s data by mistake (or on purpose).
  
• Network Rules: You can also block or allow traffic between agents using Kubernetes network policies—so they only talk to what they’re allowed to.

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