Building Digital Twins for AI Agents

Agents, by definition, act upon their environment. Yet the quality of their actions depend entirely on how accurately and swiftly they perceive the state of their world. Imagine trying to make your way through a crowded room, guided only by a photo taken five minutes earlier. You reach for a chair that’s been moved, step into someone’s path who wasn’t there before, and navigate a space that no longer matches reality. This is how AI agents behave when they’re forced to act without access to live, consistent state.
The conceptual solution to this problem, as laid out in our original post, is simple: give agents a digital twin. This twin allows agents to observe, reason, and act based on the current truth of your organization. And thanks to Materialize, building it is now straightforward. Materialize connects directly to your operational systems, maintains always-fresh views into every aspect of your business, and surfaces them in a form agents can understand and use.
This guide is a technical companion to that idea, showing you how to move from concept to implementation using Materialize and Strands Agents. By the end, you’ll have a practical blueprint for making your most important business concepts available to agents that need them.
Defining the Digital Twin
A digital twin for AI agents isn’t just another copy of your data. It’s a structured always-current representation of the core entities that define your business like customers, inventory, shipments. And the live properties that shape their meaning, like availability, status, or tier. Think of it as a continuously updating map of your operational world. A semantic model that stays in sync with reality.
Each view corresponds to a concrete entity along with its properties. What this approach avoids is forcing agents to guess how these entities are defined or stitched together. The model context protocol, or MCP, provides a standardized way to surface these views as tools so agents can reliably observe their current state.
And as your library of views grows, so too does your system’s semantic richness. Agents gain a broader and more coherent understanding of your business, one well-modeled object at a time.
Building the Live Bridge
Consider a same-day delivery service working to launch a new feature: an agent that can shop for ingredients based on a customer’s recipe. The goal is to eliminate friction from meal planning by letting an agent do the work. But implementing it means stitching together a complex set of systems. ERP for inventory, CRM for customer data, marketing for promotions. The challenge is surfacing the live state of all these systems in one cohesive, agent-readable model.
The team starts with the most foundational questions, what is actually in stock right now.
They start by connecting their ERP system, where inventory is managed, directly to Materialize. Materialize mirrors every change as it occurs in real time, allowing the team to build a live view of the product entity.
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They start by creating a view in Materialize, products, that agents can reference. products is a comprehensive inventory summary that joins all of our products from our product_catalog with our inventory_levels table. We expose the product name, id, availability, number of fulfillment centers, and stock level to the agents. This curated view gives agents access to the exact information they need, saving agents token cost and leaving out confidential information from our ERP system.
The index tells Materialize to keep the results of the view always fresh and available.
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With the live, in memory, view of products and their inventory information, the team defines comments for their MCP server for the products view itself and its properties. These comments define in natural language what the view and properties for the agents.
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Just like that, the team has enabled agents to look up the current state of any product based on its product id. Agents leverage the natural language descriptions provided to understand the semantics of the entity. This means agents can autonomously determine when and how to look up products and interpret properties like stock levels to make live decisions.
From here, the team can reach for a framework like Strands Agents, to quickly build an AI Agent that assists with shopping carts. It takes a customers recipe and observes the current state of each product through the products view to ensure all the ingredients for a recipe are available.
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Expanding Capabilities
With inventory live, the team turns to customer context. They connect their CRM and build a unified customer view that merges identity, preferences, and trust signals into a single, live model.
Now the agent can do more than check what’s in stock, it can personalize its choices. If the customer is allergic to dairy, it swaps ricotta for a cashew-based alternative. If they're gold-tier, it prioritizes ingredients available for same-day delivery from their preferred warehouse. The agent isn’t just filling a cart, it’s tailoring the experience to the individual, using fresh data at every step.
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What started as a basic cart builder is now a fulfillment strategist, personalization engine, and promotion optimizer. Because with each new view your agents grow more capable. They gain the context to personalize decisions, coordinate operations, and respond to change. All because of the data it can see.
Materialize is the platform for building these agent-ready digital twins. Just write SQL. Under the hood, our breakthrough in incremental view maintenance keeps everything fresh and at scale. You can run Materialize as a managed service or deploy it privately in your own cloud.
If you're working to make your operational data ready for AI, we’d love to help. Book a 30-minute introductory call with us here.
