How to actually build Multitenant Agents without Redesigning your Architecture

#ai #multitenant #architecture

Multitenancy is often treated as a systems-level problem. Most teams assume they need to overhaul their infrastructure to support multiple users or agents, when in reality, if your system can isolate context, persist memory intelligently, and handle scoped user sessions, you’re already 80% of the way there.

This article explains how to build multitenant agents without redesigning your architecture and explores the practical paths teams can take today to support multiple users from a single agent setup.

What is a Multitenant Agent in AI?

In AI, multitenancy refers to the ability of a single AI system to serve multiple users while keeping each user's data, context, and memory completely isolated. These users are referred to as “tenants”. With multitenancy, even though everyone interacts with the same model, each user gets a private, personalised experience, as if the system were built just for them. This is achieved through proper context handling, session management, and memory separation, without creating new model instances for every user.

A multitenant agent is an AI agent that serves multiple customers (called tenants) simultaneously, while keeping their data, configurations, and interactions completely isolated. That means the same backend, different experiences per user and no mix-ups or data leaks.

A multitenant AI agent can be seen as an apartment building with various tenants(the users), sharing the same elevators, electricity, water, and security. However, tenants have their own private, customised space, different decorations, and furniture inside each apartment. No tenant can see or access another’s apartment in this building. Their spaces are entirely separate, even though they share the same building infrastructure.

A multitenant agent means you don't have to create a new instance for every user or client. An instance is a running version of a program, tool, or service. This architecture offers leverage over traditional single-tenant approaches, where each user requires their own dedicated agent instance.

How to approach Multitenancy in AI agents.

There are three major ways to approach multitenancy in AI agent systems. Each has different implications for control, scalability, and developer experience. Your path will shape your ability to build and how well your agent performs under real-world load.

The Custom Route: Total Control, High Complexity

The most hands-on approach is to build multitenancy from scratch. In this setup, you manage user sessions, store memory, and inject context into every interaction. This usually involves creating a dedicated data layer for storing user-specific memory, using tokens or session IDs to track activity, and writing custom logic to route requests and responses correctly per tenant.

Every time a user interacts with your agent, your system must detect their identity, retrieve their past interactions, apply their settings, and store the result in their isolated memory space. This can work beautifully when done right, providing precise control over how agents behave for different users.

But this level of control comes at a cost. You’re maintaining every part of the infrastructure, memory management, context scoping, session recovery, data isolation, and more. Unless you're backed by a strong engineering team or working in a highly regulated space, this route tends to slow you down more than it empowers you.

MCP (Model Context Protocol):

MCP, or Model Context Protocol, is a structured way to provide an AI agent with everything it needs to act intelligently for a specific user or task. It wraps together instructions, memory, context, personalisation, and task-specific inputs into one package. This protocol is crucial in helping AI agents behave consistently, recall important information, and tailor their responses to different users. If you’re building a personal growth coach, a customer support agent, or a SaaS onboarding bot, MCP is how the AI understands who it’s talking to, what the situation is, and how to behave.

How then does MCP enable multitenancy?

In a multitenant system, where many users or teams are using the same AI infrastructure, MCP is what makes personalisation possible without creating separate AI models for each tenant.

It acts as the boundary between users, loading in their specific memory, preferences, and goals into the AI’s “mind” for each session.

But the downside is, while MCP solves a lot of personalization and scaling problems.

First, because everything the model knows in a session is passed via MCP, the prompt size can become large, which leads to high token usage (i.e., more expensive API calls and slower response times). If you’re injecting long-term memory, task data, system prompts, and personalisation into every single request, that’s a lot of overhead.

Second, there’s a real risk of context conflict or bloat. If the MCP isn’t structured well, for example, if overlapping or outdated memory is injected, the model may become confused, generate irrelevant output, or start mixing tenant data. That’s a big problem in multitenant systems where user isolation is a non-negotiable. One small prompt engineering error or memory leak, and suddenly, User A’s preferences or data might influence User B’s session.

Another issue is maintenance and governance. As you scale, keeping MCP clean, up-to-date, and secure for every tenant becomes complex. You’ll need rules to prune memory, control scope, and validate the data being injected. Otherwise, you risk injecting stale information, which leads to hallucinations or broken workflows.

FASTN:

By now, you’ve seen the two most common ways teams try to “make multitenancy work.”

One gives you control, at the cost of complexity.

The other gives you speed at the cost of durability.

Let’s be honest: neither is ideal for most product teams.

With the custom route, you're effectively building a second product under the hood, managing databases, writing session handling logic, allocating memory, and debugging context bleed issues. All to make your agent usable by more than one person.

Imagine launching a B2B AI tool for HR teams, where every time a new company signs up, your engineers must manually configure how context is stored, what data is remembered, and how that data is retrieved for each user. That’s not scale, that’s technical debt.

With MCP, you can move faster. But the minute someone says, “Can the agent remember what I said last week?” or “Can we track decision history across the team?” you would be back to hacking around prompt windows, compressing memory. Think of a founder building an AI coach for sales teams. It works great with MCP at demo time. But once users start asking for coaching feedback based on their full conversation history, performance drops and personalization breaks.

What if instead of making you build multitenancy or simulate it, you use a native system that supports:

Scoped sessions for each user or tenant

Persistent memory, tied to identity

Private configurations per tenant

Shared logic and infrastructure

Zero leakage, zero duplication

But FASTN goes beyond just session isolation. It includes a Multi-Tenant Embedded App Store built directly into your agent.