I’ve been using, teaching, and reviewing Clean Architecture for years. And I’ll be honest: most teams don’t get it wrong because they don’t understand the theory. They get it wrong because the theory gets applied blindly—full of ceremony, layers, and abstractions that look clean on paper but collapse under real production pressure.
This post isn’t about explaining Clean Architecture. It’s about the traps I see repeatedly in code reviews, audits, and disaster debugging sessions. Some of these mistakes have cost my teams weeks of engineering time and painful operational outages. Hopefully you can dodge a few bullets here.
The trap of over-layering everything
Most Clean Architecture failures start with a simple misunderstanding: layering is not the architecture. Boundaries are. But I keep seeing stacks like:
API → Application → Domain → Infrastructure → Persistence → Mappers → DTOs → More DTOs
This isn’t architecture—it’s résumé-driven development.
One of my worst experiences with this was a payments system where every request took a scenic tour through six translation layers. Debugging became archaeology. Adding a feature took days because every DTO needed a cousin in every layer.
Forgetting that boundaries are about direction, not distance
I often ask teams: “Show me where business rules live.” They point to a /Domain folder with 200 anemic classes and maybe a few enums. That’s not a domain—that’s a graveyard.
Your domain layer should express:
- Invariants
- Policies
- Business rules
- Behavior
If all your logic lives in application services, and domain objects are just bags of properties, you don’t have Clean Architecture—you have layered CRUD.
Using interfaces for absolutely everything
I can’t count how many times I’ve seen codebases where every class has a matching interface, even for things that will never be swapped.
public interface IDateTimeProvider
{
DateTime UtcNow { get; }
}
public class DateTimeProvider : IDateTimeProvider
{
public DateTime UtcNow => DateTime.UtcNow;
}
No. Just… no. This isn’t Clean Architecture. It’s noise.
The biggest cost of unnecessary interfaces isn’t extra files—it’s accidental complexity. Teams freeze because the abstraction layers multiply faster than business needs.
Repositories becoming mini-ORMs
This one bit me hard early in my career. The repository pattern metastasized into a parallel ORM:
Add,Update,DeleteGetByIdFindByEmailFindByEmailAndStatusGetActiveUsersWithPendingSubscriptionsCreatedLastWeek
By the time I joined, there were 87 repository methods just to query users. All handwritten. Half inconsistent. And EF Core was doing the heavy lifting anyway.
When I replaced that system with a small set of aggregate-focused operations + typed specifications, performance improved and the codebase shrank by 40%.
Application services turning into god objects
Many teams create a UserService, OrderService, BillingService, and they become black holes of logic. Everything ends up inside them because “domain should be pure” and “infrastructure belongs outside”.
The result?
- Massive classes
- Duplicated logic between services
- No enforceable business invariants
- Hard-to-test flows
I once encountered a CustomerService file with 4,300 lines of code. It wasn’t a service. It was a landfill.
Domain events used everywhere… except where they make sense
I like domain events; they’re powerful when used correctly. But most teams treat them like confetti. Every state change publishes three events, even when nothing outside cares.
And then I see the opposite: critical business workflows are implemented with brittle RPC-like calls instead of domain events.
The rule of thumb I ended up following after some painful debugging episodes:
- Use domain events when the domain needs the reaction.
- Use integration events when other systems need the reaction.
- Do not use events as a default plumbing mechanism.
Infrastructure bleeding into the domain through leaky abstractions
I keep seeing domain models that expose Guid primary keys, EF Core attributes, or directly expose persistence behaviors. Every time that happens, a little piece of purity dies.
Your domain shouldn’t know:
- how it is stored
- how it is retrieved
- whether EF Core or Dapper is used
- whether IDs are
Guidorlong
Once I saw a domain entity with [Key], [Required], [MaxLength], and [ForeignKey] attributes. That isn’t a domain model. That’s persistence glue dressed as a domain model.
Over-optimizing for testability
Some developers take the testing argument too far. They refactor simple logic behind interfaces just so it can be mocked. The code becomes:
- harder to read
- harder to maintain
- slower to navigate
- full of indirection that adds no value
You should test behavior, not plumbing.
The illusion that Clean Architecture removes complexity
Clean Architecture doesn’t eliminate complexity. It organizes it. I’ve seen teams blame Clean Architecture when performance drops or development slows, but the real issue was over-engineering applied without understanding the problem domain.
One team wrapped every EF Core DbContext method in its own repository abstraction. They added mapping layers, DTO layers, API layers, domain layers, validator layers, and event-dispatch layers. By the time I arrived, 80% of the architecture was accidental and 20% was business logic.
Architecture should emerge as the domain clarifies—not the other way around.
Closing thoughts
When Clean Architecture works, it’s a joy. The code feels honest. Business rules are front and center. Infrastructure stays in its lane. Testing becomes easier. Everything flows.
When it goes wrong, you get ceremony without clarity—an architecture that looks impressive in diagrams but feels painful in production.
The mistakes in this post aren’t theoretical. They’re scars I’ve collected from real projects, including ones I personally designed before I knew better. If you avoid even a few of these traps, your next system will be simpler, safer, and much more aligned with the intent behind Clean Architecture.