This conversation explores making the Strategy pattern easier to implement in C# using a Domain-Specific Language (DSL), with particular focus on stakeholder involvement, complex context management, and cross-layer consistency.
Human: I would like to explore making the strategy pattern easier to implement in C# using a DSL.
Claude: The Strategy pattern is a great candidate for DSL implementation in C#! A DSL can eliminate much of the boilerplate code and make strategy selection more intuitive. Let me show you a few approaches, from simple to more sophisticated.
The DSL implementation provides several advantages over the traditional Strategy pattern:
Human: I would like to explore using a code transformer to take a simple DSL definition of a strategy and auto-generate the boilerplate with logic.
Claude: That’s a fascinating approach! Using source generators or code transformers to convert a simple DSL into the full Strategy pattern implementation would eliminate runtime overhead while keeping the clean syntax. Let me show you how to build this using C# Source Generators.
This source generator approach provides several powerful advantages:
[GenerateStrategy]Human: Can we mix in context predicate resolution for determining what strategy to use?
Claude: Absolutely! Let’s enhance the DSL to include context predicate resolution so the generator can automatically select strategies based on runtime conditions rather than just string keys. This makes the strategy selection much more intelligent and flexible.
This enhanced version includes powerful predicate resolution capabilities:
CanExecute predicates that automatically determine eligibility based on contextCanExecute method is checked in priority ordertrue is selectedHuman: There are cases where strategies may need to be built by examining context and branching to examine other context.
Claude: Absolutely! That’s a more sophisticated pattern where strategies need to make decisions dynamically based on what they discover in the context, then potentially branch out to gather additional context. Let me show you how the DSL could support this multi-stage context discovery.
This multi-stage branching approach handles the most complex real-world scenarios:
Human: I see a lot of verbosity in all of this. I’m aiming at a structured text document that can be used to generate the strategies.
Claude: You’re absolutely right! All that attribute decoration is way too verbose. A structured text DSL would be much cleaner and more maintainable.
This text-based DSL is much cleaner! Here’s what makes it powerful:
No more verbose attributes cluttering the code!
Multi-stage branching expressed clearly in the text format.
Natural boolean expressions instead of method decorations.
Real C# code where it matters most.
System behavior controlled through configuration, not code.
Human: Tell me what I don’t know in reference to this discussion. I have prompted and you gave solutions, but looking at the conversation, is there something I’m missing?
Claude: Looking back at our conversation, I see several important gaps that we haven’t addressed:
We’ve focused on context building but glossed over a critical issue: How do you efficiently select strategies when you have hundreds of them?
If you have 200+ strategies each with complex predicates, you can’t afford to evaluate every predicate for every request. You need:
Your text DSL looks clean, but in practice:
We touched on context discovery but missed the complexity:
Human: I was also thinking about predicate order, so I know about that.
Claude: Ah, you’re thinking ahead! Predicate order is indeed crucial - the difference between a fast system and one that times out.
Since you’re aware of that, let me dig into the really subtle stuff you might not have considered:
The order seems obvious, but what if debt_to_income calculation changes the credit score through a soft inquiry? Now your predicate evaluation has side effects and order becomes non-commutative.
If FastPath fails and you fall through to SlowPath, you’ve now poisoned the context cache with expensive data. Future requests under $1000 might accidentally use the cached expensive context, creating unpredictable performance.
Your multi-stage context loading introduces a nasty race condition where the credit score changes between evaluation steps, causing predicates to evaluate on stale data while strategy logic runs on mixed fresh/stale context.
Human: These seem common to rule engines, where predicate resolution can become a recursive nightmare.
Claude: Exactly! You’ve hit the core issue - this DSL is essentially reinventing a rule engine, and you’re walking straight into the same complexity traps that have plagued rule engines for decades.
What you’re building looks suspiciously like:
The “strategy pattern with context discovery” is really just rule-based systems with modern C# syntax sugar.
Human: Let me share a more concrete example I deal with: I have an edit button for each type of metadata. When it’s enabled, who can click it, when they can, the text of the button, the state of the data, etc.
Claude: Perfect! Now we’re talking about real complexity. That edit button is a microcosm of everything we’ve been discussing - it’s a simple UI element that actually embodies incredibly complex business logic across multiple stakeholders and contexts.
What looks like a simple <button>Edit</button> is actually a complex decision involving at least 6 different dimensions:
Each dimension has different owners but they interact in complex ways that nobody fully understands.
Human: I’m also exploring stakeholder involvement in code change.
Claude: Ah! Now we’re getting to the real reason behind the DSL exploration. This isn’t just about technical patterns - it’s about power structures and change ownership in software systems.
Your DSL is really asking: Who should be able to change what, and when?
The fundamental question: In a software system, who should have the power to change what, and how do you prevent that power from destroying the system?
Human: I think another major issue is that the app I’m thinking of is a metadata app and it’s highly configurable by the users, but we have a lot of business logic baked into it also.
Claude: Ah, that’s the killer combination - a metadata-driven configurable system with embedded business logic. You’re dealing with one of the hardest architectural problems in software.
You’ve got three different types of logic all tangled together:
This creates an architectural nightmare where everything depends on everything else in unpredictable ways.
Human: I’ve also noticed that some scenarios build on other ones. Every time I see these problems, I think of object merging in JavaScript, taking two objects and merging them together {...mybase, name: "henry", age: 40}.
Claude: Brilliant observation! You’re absolutely right - the compositional nature of these scenarios is exactly like JavaScript object spreading. The edit button logic is really layered object composition where each layer can override or extend the previous one.
Your edit button logic is essentially:
const editButtonState = {
...platformDefaults, // Base behavior
...securityLayer(user, entity), // Security overrides
...workflowLayer(entity), // Workflow overrides
...tenantCustomization(tenant), // Tenant overrides
...userPreferences(user), // User overrides
...temporalOverrides(system) // Time-based overrides
};
Human: Now how do you make that concept usable across layers of the app, the UI web client, the API backend, and maybe the database CRUD?
Claude: Excellent question! The compositional pattern needs to serialize and execute consistently across all layers. The key insight is that composition is a portable algorithm - the same merge logic can execute in JavaScript, C#, SQL, or any layer.
Human: Again, like we explored before, but at what cost/trade-off?
Claude: You’re absolutely right to ask - the compositional cross-layer approach comes with significant costs. Let’s be brutally honest about the trade-offs:
Development Complexity:
Operational Complexity:
You’ve traded O(1) boolean check for O(n) layer composition with expression parsing overhead.
What You Gain:
What You Pay:
Human: This all seems like a very complex engineering problem.
Claude: You’re absolutely right. This IS a very complex engineering problem, and we’ve been building a Rube Goldberg machine to solve what might be a much simpler issue.
Most “edit button” logic in production systems looks like this:
public bool CanShowEditButton(User user, Entity entity)
{
if (!user.HasPermission("edit")) return false;
if (entity.Status == "archived") return false;
if (entity.LockedBy != null && entity.LockedBy != user.Id) return false;
if (_systemState.MaintenanceMode) return false;
return true;
}
That’s it. Simple, fast, debuggable, maintainable.
We got carried away exploring patterns without asking the fundamental question: “What problem are we actually trying to solve?”
The real problems are usually:
This conversation perfectly illustrates how engineers love to solve interesting problems even when the business need is simple. We explored some genuinely cool patterns, but we also demonstrated the danger of over-engineering.
Sometimes the best engineering solution is no engineering - just clear, simple code that works and can be understood by the next developer who has to maintain it.
The edit button doesn’t need to be architecturally interesting. It just needs to work.
This exploration of strategy patterns and DSLs reveals both the power and the peril of sophisticated software architecture. While we discovered fascinating approaches to managing complex business logic across multiple stakeholders and system layers, we also uncovered the fundamental tension between flexibility and simplicity.
The key insights:
The compositional strategy pattern we explored is genuinely powerful for certain use cases, but it’s crucial to honestly assess whether your problem actually requires that level of sophistication. Sometimes the best engineering decision is to choose the boring, simple solution that works reliably and can be easily maintained by your team.
This conversation was exported from Claude AI on [current date]. Grammar has been corrected for clarity while preserving the technical content and conversational flow.