What is a Digital Twin for AI Agents?

When orgs trying to put AI agents into production see that their systems are failing, they don’t realize that their current data stack is the problem.

Traditional data architectures rely on ETL (extract, transform, load) processes that run periodically: maybe every few minutes, maybe hourly, maybe overnight. But AI agents require live data, not traditional batch processing.

A digital twin platform is the live operational data infrastructure that agentic applications need to succeed in production. In this post you will learn about digital twins that are specifically for AI (with a quick overview of other types of digital twin architectures). We explore the data needs of agentic AI applications and the problems that digital twins solve for production agentic systems.

A digital twin is an exact, always-current model of your organization’s systems and the relationships between them.

In an AI context, a digital twin isn't just a virtual replica of your production system: it's an abstraction layer that models your entire business domain. Instead of abstract data held in low-level tables, though, a digital twin speaks the language relevant to your company such as customers, orders, suppliers, and routes.

Without a digital twin, agents must query raw database tables or event streams for properties (for example, orders_table and customer_table. Then they have to work to figure out the joins (for example, joining orders and customers properties into a new CustomerOrders property) themselves, along with any relevant business logic.

With a digital twin, agents can interact with coherent entities like "Customer" or "Order" or “CustomerOrders“ that already encode the relationships and business rules.

Digital twin architecture is designed to transform raw operational data into a defined, relevant and discoverable form that actually reflects how the business thinks about itself.

Digital twins map to other data architecture elements essential to effective agents: domain-driven design, semantic layers, and data products in an operational data mesh — all of which depend on data that is live and above all accurate.

Agents act, and actions have consequences.

When an agent acts, those consequences ripple outward, and they’re not always obvious. This matters because your organization isn't a collection of disconnected databases. It's an interdependent system where a single action touches multiple downstream systems. A customer refund triggers adjustments for inventory counts, loyalty balances, and cash-flow projections; a tweak to a logistics route triggers cost rebalancing and updated delivery times.

Agents make these decisions and take these actions based on the data they’re given. But what if that data doesn’t reflect your system’s actual current state?

Traditional data architectures rely on ETL (extract, transform, load) processes that run periodically: maybe every few minutes, maybe hourly, maybe overnight. This latency creates a gap between when an action happens and when its effects become visible in the data that the agent queries for its work, and one of two failure modes result:

• If the agent waits for the data to catch up, it's blocked and sits idle and unproductive
• If the agent doesn't wait, it makes decisions based on a state of the world that no longer exists

Neither option is acceptable. Agentic AI systems must have a live operational data infrastructure that ensures agent operations are always current and accurate.

What agents need instead is a live digital twin that lets them observe, reason, and act based on the current truth of your organization. An agent working in a digital twin environment stays in sync with reality: when the agent issues a refund, the twin immediately reflects downstream impacts in other business areas. No waiting on the next ETL update cycle. No stale reads.

Data freshness and accuracy is the obvious win here, but digital twins also deliver a second major benefit: making your agents faster and more reliable while reducing your agent system’s cost and increasing throughput.

A digital twin for AI agents isn’t simply another copy of your data. It’s a way to give your agents a tight feedback loop that enables them to complete a task faster, make the most accurate decision, and return the highest quality result.

Here’s what the agentic workflow looks like, with and without digital twin architecture:

1. Observe. An agent needs to understand the state of the world at any moment: Where is that shipment right this second, and where is it going next? With a digital twin, that view is already assembled and the agent can access the context it needs in milliseconds.Without a digital twin, the agent must consult low-level data tables and then craft a query (possibly an expensive one) to get the environmental context they need.
2. Think. The agent next calls on an LLM to reason about the data it just retrieved so it knows what to do next. Is the package delayed? Does policy call for a refund or a reroute? Do I need more data?
3. Act. Decision made, the agent acts. This results in writes to operational systems.With a digital twin, data updates within seconds. Every other agent immediately sees the new state and adjusts their workflow accordingly. Without a digital twin, the data doesn’t change until the next batch update. No matter how many times it runs its loop and writes results to the operational system, it can still only access the same stale data as if nothing ever happened.

Agents in a digital twin architecture can be significantly more efficient because the data products they query for information are defined ahead of time. Any less-refined data has already been transformed into governed, discoverable, and easily parsed data products, so agents don’t waste inference cycles piecing tables together or doing complex joins. They simply complete their task, watch the digital twin refresh, and are ready to start the loop again: a simple, elegant and highly efficient workflow.

For AI features, the majority of costs come from compute: the processing cycles required to run LLM inference, execute queries, and transform data. Storage is relatively cheap; network is generally manageable; compute is where the money goes.

Digital twins let you manage your agentic system’s costs.

Human employees might run a handful of queries per hour, or check a dashboard a few times a day. Because agents operate continuously and at machine speed, though, they can potentially generate hundreds or even thousands of operations per minute. If your data architecture requires significant compute for every query (recalculating joins, re-aggregating data, reprocessing business logic on demand), this agent-scale traffic becomes prohibitively expensive. Your costs scale linearly (or worse) with query volume.

A properly designed digital twin absorbs that compute cost differently by giving your agent access to pre-defined data products, and any less-refined data has already been transformed into governed, discoverable, and easily parsed views. You pay the compute cost once (when the underlying data changes) and then serve queries cheaply from pre-computed results. This inverts the cost model: instead of paying per query, you pay per data change. The digital twin does the expensive work upfront so agents can query cheaply at scale.

Vectors are the language of AI, and they provide policies, patterns, and history for agents to follow. 

Working with vector databases is simple, at least on the surface. You take unstructured data, embed it, and write to your database along with the attributes you assign to it for filtering and reranking based on business logic. Vectors themselves are created by pipelines, which translate unstructured data into vector embeddings with attributes.

• Vector embeddings (generally just called “vectors”) represent an actual, numerical, LLM-readable data object that captures the semantic meaning, context, and relationships between data points.
• Vector attributes are human-defined rules and domain knowledge that describe that data object, existing as structured metadata that gets fed to the agent/model as input. Attributes are where business logic lives.

Both embeddings and their attributes are subject to change as upstream data changes. But building live data pipelines that can keep vector embeddings and attributes fresh for accurate, up-to-the-minute AI results is extremely difficult.

Think of vectors and their attributes as providing the ground rules for the game. The digital twin, then, tells us what all the players are doing right now. You need both of these for the AI agent to play, or take confident action.

Digital twins function like a speed layer for giving your agents trustworthy context. Because they encode the data relationships and business rules that must be instantaneously available for agentic decision-making, they are the ideal environment for updating vectors and their attributes to accurately represent your current system state in more or less real time.

In more traditional data architectures, when an agent writes to the operational system and changes the source data, it’s difficult to know which vectors are affected and what part of those vectors needs updating. Changing a single customer record, for example, can require recalculating attributes for thousands (even millions) of vectors because it's too complex to determine exactly which vectors are affected. Teams take the safe but expensive route of re-embedding everything to ensure freshness, and infrastructure costs balloon from compute waste.

Digital twins, though, can use incremental view maintenance (IVM) as the engine for keeping data views and transformations up to date without having to reprocess everything to see the results of an update. Incremental views make it economically feasible to keep agents always current with the impact of their actions. (Many of the core ideas behind this groundbreaking and efficient way to transform data as inputs change, like differential dataflow, were invented by Materialize co-founder, Frank McSherry).

As you define data products in your digital twin to depict the most important parts of your business, agents get continually improving and continually updating views of your system’s state. With IVM, the digital twin stays current without expensive batch jobs and reads stay cheap — even at agent scale.

Materialize is a platform for creating agent-ready digital twins, just using SQL. It is built around a breakthrough in incremental-view maintenance, and can scale to handle your most demanding context retrieval workloads. Deploy Materialize as a service or self-manage in your private cloud.

We’d love to help you make your operational data ready for AI. Go to materialize.com/demo/ to book a 30-minute introductory call.