Most codebases don't fail because developers can't write algorithms. They fail because the code doesn't reflect how the business actually thinks. You end up with a tangle of services manipulating data structures that mean nothing to anyone—including the developers who wrote them six months ago.

Domain-Driven Design offers a different path. Its tactical patterns—entities, value objects, and aggregates—give you a vocabulary for modeling complex business domains in code. These aren't abstract architectural concerns. They're practical tools for capturing the rules, relationships, and behaviors that make your software valuable.

The difference between a well-designed domain model and a collection of database tables wrapped in getters and setters is profound. One evolves gracefully as requirements change. The other fights you at every turn. Understanding these patterns is the first step toward building software that business stakeholders can actually recognize.

Consider two objects in your system: a customer and a shipping address. Both have attributes. Both can be stored and retrieved. But they're fundamentally different in one crucial way: how you answer the question "is this the same thing?"

A customer remains the same customer even if they change their name, email, or phone number. What matters is their identity—some stable reference that persists across time and attribute changes. This is an entity. You track it, you compare instances by ID, and two customers with identical attributes are still different customers.

A shipping address is different. "123 Main Street, Springfield" is the same address regardless of which object instance represents it. If all the attributes match, they're interchangeable. This is a value object. You compare them by their attributes, and you typically make them immutable—why would you "change" an address when you can simply create a new one?

Getting this distinction wrong creates subtle bugs and unnecessary complexity. Treat a value object as an entity, and you're tracking identity that doesn't matter, introducing database IDs where none are needed. Treat an entity as a value object, and you lose track of things that must be tracked. A money amount is a value. A bank account is an entity. Confuse them at your peril.

Takeaway

Ask yourself: if two objects have identical attributes, are they the same thing? If yes, use a value object. If identity matters independent of attributes, use an entity.

An e-commerce order contains line items. When you add an item, the order total must update. When you remove the last item, certain rules might apply. These objects form a consistency boundary—a cluster of entities and value objects that must change together to maintain business invariants.

This cluster is an aggregate. It has a root entity (the order) that serves as the single entry point for all modifications. External code never reaches inside to manipulate line items directly. Instead, it asks the order to perform operations, and the order ensures everything stays consistent.

Why does this matter? In a concurrent system, you need to lock things when making changes. Lock too little, and you get corrupted state. Lock too much, and your system grinds to a halt. Aggregates define the minimum unit of consistency—the smallest thing you need to lock for any given operation.

The art lies in drawing the right boundaries. Too large, and you create unnecessary contention—users can't edit their profile while an admin updates their permissions. Too small, and you split things that must stay consistent, forcing complex coordination. Look for true invariants: rules that must never be violated, even temporarily. Those define your aggregate boundaries.

Takeaway

Aggregates answer the question: what must change together atomically? Design them around true invariants, not convenience, and keep them as small as possible while still protecting consistency.

The anemic domain model is perhaps the most common anti-pattern in enterprise software. You have Customer, Order, and Product classes—but they're just data containers. All the actual logic lives in CustomerService, OrderService, and ProductManager, shuffling data between passive objects.

This scatters business rules across the codebase. Want to know how order pricing works? Check the OrderService, the DiscountCalculator, the TaxService, and maybe the PromotionEngine. The Order object itself knows nothing—it's just a struct with getters and setters.

A rich domain model inverts this. The Order knows how to calculate its total. It enforces the rule that you can't add items after payment. It rejects invalid state transitions. The object isn't a data bag—it's a living representation of a business concept, complete with behavior.

This makes business rules discoverable. Where's the logic for order cancellation? In the Order class. How does discount stacking work? The Order's applyDiscount method will show you. Services still exist, but they orchestrate operations across multiple aggregates rather than implementing core business logic. The domain objects become the single source of truth for how the business actually works.

Takeaway

When you find yourself writing a service method that takes an object, reads its data, makes a decision, and updates it—stop. That behavior probably belongs inside the object itself.

These three patterns—entities, value objects, and aggregates—form the foundation of tactical DDD. They're not complicated individually, but applying them well requires genuine thought about your domain. What really has identity? What must stay consistent together? Where does behavior belong?

The payoff is code that speaks the language of your business. Stakeholders can review it and recognize their concepts. New developers understand it faster. Changes in business rules map cleanly to changes in code.

Start small. Pick one bounded context, model it carefully, and see how it feels. The patterns will prove their worth when requirements change and your code adapts gracefully instead of fighting back.