Multi-Tenant Isolation

This is the part most people skip. This is the part that matters.

When multiple clients share AI infrastructure — a SaaS product, a managed service, a shared platform — each client's data, model context, and agent behavior must be isolated from every other client's. Multi-tenant isolation failure does not just leak data. It leaks data between competitors who are both your clients. The reputational and legal consequences are existential.

1. Context Window Isolation No client's data should ever appear in another client's context window. This means separate model instances or rigorous context management that guarantees tenant boundaries. Shared model instances with per-request context injection must prove that context from Tenant A cannot bleed into Tenant B's session through conversation history, cached responses, or shared memory.
2. Data Store Isolation Separate databases, separate encryption keys, separate access credentials per tenant. Logical isolation within a shared database — row-level security, tenant ID columns — is the minimum. Physical isolation — separate database instances — is the standard for regulated industries.
3. Network Isolation per Tenant Each tenant's agent traffic should be segmented into its own network namespace. A compromised agent serving Tenant A should not be able to reach Tenant B's services. This combines microsegmentation with tenant-aware routing to create isolation that survives infrastructure-level compromise.