A digital twin is a living, computational mirror of a real system—updated from data, runnable in silico, and used to predict, optimize, and explain. The idea has taken hold in manufacturing, supply chain, healthcare, and infrastructure. The open question is how you build that mirror when the system is made of many interacting actors, not just a single asset or a few equations. Agent-based simulation is a natural fit: it lets you represent each actor (machine, truck, person, facility) with its own state and behavior, and the twin evolves over time as those agents interact.

Why agent-based for digital twins?

Real operations are rarely well captured by a single aggregate model or a single discrete-event process. You have:

• Heterogeneous actors — Different roles, rules, and constraints (e.g. generators, lines, repair teams, control rooms).
• Local decisions — Each actor reacts to what it sees (inventory, orders, failures), not just to a global set of equations.
• Emergent outcomes — System-level behavior (throughput, resilience, congestion) arises from many local interactions.

Agent-based models excel at exactly this: many entities, each with state and behavior, producing emergent dynamics. That’s why platforms like AnyLogic combine agent-based modeling with discrete-event and system dynamics—so you can mix “this fleet of vehicles” (agents) with “this process flow” (discrete events) and “this market feedback” (system dynamics) in one digital twin.

BigBear.ai’s ProModel.ai takes a related direction: a cloud-based digital twin fed by real-time operational data, so the twin stays in sync with the real system and supports both strategic planning and operational decisions. The emphasis is on connecting data to simulation and turning the twin into a decision support asset.

Prorok is built for agent-based digital twins at scale. You define entities and their behaviors in a multiverse runtime; the same world can be driven by historical or live data, stepped for scenarios, and replayed for analysis. The goal is to make it straightforward to go from “my system has these kinds of actors and these rules” to a runnable twin that you can query, visualize, and connect to AI-driven analysis.

What you can twin with Prorok

Conceptually, anything you can describe as entities + behavior + interaction is a candidate:

• Supply chains — Sites, vehicles, orders, and inventories; delays, failures, and policy changes.
• Infrastructure — Grids, nodes, lines, loads, and repair crews; faults and recovery.
• Operations — Lines, cells, workers, and jobs; schedules and disruptions.
• Urban and logistics — Zones, traffic, facilities, and demand patterns.

In the Prorok repo you’ll find examples that lean in this direction: e.g. an infrastructure model (electricity grid with generators, transmission, load zones, and repair teams) and factory and retail models. They illustrate how to represent real-world structure and behavior in an agent-based world, then step it, replay it, and (in the future) feed it with real data or AI-generated scenarios.

From model to twin

A digital twin typically implies:

1. A model — Entities, state variables, and rules (behaviors) that define how the system evolves.
2. Data binding — Initial state or ongoing updates from the real system (or from historical datasets).
3. Execution and analysis — Running the twin for forecasting, what-if analysis, or optimization; comparing runs to reality.

In Prorok, the model is the multiverse blueprint: entities, components, and behaviors. Execution is the cluster stepping the simulation and exposing state via queries and replays. Data binding and live sync are the next step—connecting real-time or batch data to world state so the twin stays aligned with the system it mirrors. We’ll cover that in later posts and docs.

If you’re new to agent-based simulation on Prorok, the next post will get you started: Getting Started with Agent-Based Simulation.