Remember when building software meant creating one big application that did everything? Those days are gone. Now we’re building systems that can change themselves based on what’s happening around them.

This isn’t just about microservices or cloud architecture. It’s about something bigger. We’re moving toward systems that understand context, adapt to new requirements, and even suggest better ways to work.

What Makes Architecture “Composable”?

Traditional systems are like LEGO sets with instructions. You follow the manual, build what’s on the box, and that’s it. Composable architecture is different. It’s like having a box of LEGO pieces where you can build anything, take it apart, and build something else entirely.

The key difference? Each piece knows what it does and how to connect to other pieces. But it doesn’t care about the bigger picture. That’s handled by something else.

In enterprise systems, we call these pieces “Packaged Business Capabilities” or PBCs. Think of them as self-contained business functions that can work alone or with others. A user management PBC handles authentication. A payment PBC processes transactions. A recommendation PBC suggests products.

The magic happens when these pieces can be combined in different ways without breaking each other.

How AI Changes Everything

Here’s where things get interesting. AI services aren’t just another type of microservice. They’re different because they can make decisions about how to work.

Traditional services follow rules. If this happens, do that. AI services can look at context and decide what makes sense right now. They can learn from patterns and adapt their behavior.

This changes how we think about composition. Instead of hard-coding which services talk to each other, we can let AI figure out the best path based on what’s happening.

The Building Blocks

Let’s look at the core pieces that make this work:

Micro-Frontends

These are user interface components that can be loaded independently. Each team owns their part of the UI. When you need to update the checkout flow, you don’t touch the product catalog. You just update the checkout micro-frontend.

The AI layer can decide which frontend components to load based on user behavior or business rules. New user? Load the onboarding components. Returning customer? Skip straight to the main interface.

API-Driven Backends

Every service exposes its functionality through well-defined APIs. But here’s the twist. These APIs aren’t just for humans to call. They’re designed for AI to discover and use.

APIs include metadata about what they do, what they need, and what they return. This lets AI services understand capabilities without reading documentation.

Event-Driven Integration

Services don’t just respond to requests. They publish events when things happen. Other services can listen to these events and react.

AI can analyze event patterns to understand system behavior. It can detect when services are struggling and suggest alternatives. It can identify bottlenecks and recommend changes.

Dynamic Composition in Action

Here’s how AI-driven orchestration actually works:

Service Discovery and Selection

Instead of hard-coding service endpoints, AI can find the best service for each request. It looks at:

• Current system load
• Service performance metrics
• Request context
• Business rules

// AI-driven service selection
const context = {
  userId: "user123",
  requestType: "recommendation",
  userPreferences: userProfile.preferences,
  currentLoad: systemMetrics.load
};

const bestService = await aiOrchestrator.selectService(context, {
  criteria: ["accuracy", "latency", "cost"],
  weights: { accuracy: 0.6, latency: 0.3, cost: 0.1 }
});

const result = await bestService.execute(request);

Adaptive Workflows

Workflows can change based on conditions. If the primary recommendation service is slow, the system can switch to a faster but less accurate one. If a user is on mobile, it might use a different set of services optimized for mobile.

// C# example of adaptive workflow
public async Task<RecommendationResult> GetRecommendations(UserContext context)
{
    var availableModels = await modelRegistry.GetAvailableModels();
    
    var bestModel = availableModels
        .Where(m => m.Metrics.Accuracy > 0.9)
        .OrderBy(m => m.Metrics.Latency)
        .FirstOrDefault();
    
    if (bestModel == null)
    {
        // Fallback to faster, less accurate model
        bestModel = availableModels
            .OrderBy(m => m.Metrics.Latency)
            .First();
    }
    
    return await bestModel.ExecuteAsync(context);
}

Vector Search for Context Matching

AI services can use vector embeddings to find the most relevant components for each request. This goes beyond simple keyword matching.

// Finding contextually relevant services
const requestEmbedding = await embeddingService.createEmbedding(request);
const relevantServices = await vectorDatabase.search({
  vector: requestEmbedding,
  filter: { serviceType: "recommendation" },
  limit: 5
});

const bestMatch = relevantServices[0];

Designing for Change

Building systems that can evolve requires different thinking. Here are the key patterns:

Pluggable Domain Modules

Each business domain should be a separate module that can be plugged in or out. The user domain doesn’t need to know about payments. The recommendation domain doesn’t need to know about inventory.

This sounds simple, but it’s hard to do right. The trick is defining clear boundaries and sticking to them.

Contract-First API Design

APIs should be designed as contracts between services. These contracts include:

• What the service does
• What inputs it expects
• What outputs it provides
• What errors it might return
• Performance characteristics

When you change a service, you either maintain the contract or create a new version. AI can help manage this by detecting breaking changes and suggesting migration paths.

Observability-First Design

You can’t adapt what you can’t see. Every service needs to expose metrics, logs, and traces. But more than that, it needs to expose business metrics.

How many recommendations are being served? What’s the conversion rate? Which models are performing best? This data feeds back into the AI orchestration layer.

A Real Example: Recommendation Engine

Let’s walk through a recommendation system that uses these principles:

The Setup

We have multiple recommendation models:

• Collaborative filtering (good for popular items)
• Content-based filtering (good for niche items)
• Deep learning model (good for complex patterns)
• Rule-based system (good for business logic)

The AI Orchestrator

The orchestrator looks at each request and decides which model to use:

class RecommendationOrchestrator {
  async getRecommendations(userContext, productContext) {
    // Analyze context
    const contextAnalysis = await this.analyzeContext(userContext, productContext);
    
    // Select best model
    const model = await this.selectModel(contextAnalysis);
    
    // Get recommendations
    const recommendations = await model.recommend(userContext, productContext);
    
    // Log for learning
    await this.logRecommendation(contextAnalysis, model, recommendations);
    
    return recommendations;
  }
  
  async selectModel(context) {
    const availableModels = await this.modelRegistry.getAvailableModels();
    
    // Score each model based on context
    const scoredModels = await Promise.all(
      availableModels.map(async (model) => {
        const score = await this.scoreModel(model, context);
        return { model, score };
      })
    );
    
    // Return best scoring model
    return scoredModels
      .sort((a, b) => b.score - a.score)[0].model;
  }
  
  async scoreModel(model, context) {
    let score = 0;
    
    // Performance score
    score += model.metrics.accuracy * 0.4;
    score += (1 - model.metrics.latency / 1000) * 0.3;
    
    // Context relevance
    if (context.userType === 'new' && model.specializesIn('new_users')) {
      score += 0.2;
    }
    
    if (context.productCategory && model.specializesIn(context.productCategory)) {
      score += 0.1;
    }
    
    return score;
  }
}

The Models

Each model is a separate service with its own API:

// Collaborative filtering model
class CollaborativeFilteringModel {
  async recommend(userContext, productContext) {
    const similarUsers = await this.findSimilarUsers(userContext.userId);
    const recommendations = await this.getPopularAmongSimilar(similarUsers);
    return recommendations;
  }
  
  get metrics() {
    return {
      accuracy: 0.85,
      latency: 200,
      specializesIn: ['popular_items', 'trending']
    };
  }
}

// Deep learning model
class DeepLearningModel {
  async recommend(userContext, productContext) {
    const embedding = await this.createUserEmbedding(userContext);
    const recommendations = await this.neuralRecommend(embedding);
    return recommendations;
  }
  
  get metrics() {
    return {
      accuracy: 0.92,
      latency: 800,
      specializesIn: ['complex_patterns', 'personalization']
    };
  }
}

The Learning Loop

The system learns from every interaction:

class LearningService {
  async processFeedback(recommendationId, userAction) {
    const recommendation = await this.getRecommendation(recommendationId);
    const model = recommendation.model;
    
    // Update model performance metrics
    await this.updateModelMetrics(model.id, {
      action: userAction,
      timestamp: Date.now()
    });
    
    // Retrain if needed
    if (this.shouldRetrain(model)) {
      await this.scheduleRetraining(model);
    }
  }
  
  shouldRetrain(model) {
    const recentAccuracy = this.calculateRecentAccuracy(model);
    return recentAccuracy < model.baselineAccuracy * 0.9;
  }
}

Governance and Evolution

Building self-adapting systems doesn’t mean giving up control. You need governance to ensure changes make sense.

Architecture Knowledge Graphs

Keep a graph of your system architecture. This shows how services connect, what data flows between them, and how changes might affect other parts.

AI can use this graph to understand system dependencies and suggest safe changes.

Aligning with Enterprise Architecture

This approach fits with established frameworks like TOGAF. The key is treating AI services as first-class architectural components.

• Business Architecture: AI services support business capabilities
• Application Architecture: AI services are composable application components
• Data Architecture: AI services need access to data with proper governance
• Technology Architecture: AI services run on appropriate infrastructure

Change Management

Even self-adapting systems need change management. Define:

• What changes are allowed automatically
• What changes need human approval
• How to rollback problematic changes
• How to monitor system health

The Future: Adaptive Architecture

We’re moving toward systems that don’t just respond to change—they anticipate it. They learn from patterns, predict problems, and adapt before issues become critical.

This isn’t about replacing human architects. It’s about giving them better tools. Instead of spending time on routine decisions, architects can focus on strategy and innovation.

The systems become more resilient because they can handle unexpected situations. They become more efficient because they optimize themselves. They become more valuable because they learn and improve over time.

Getting Started

If you want to explore this approach, start small:

1. Pick one business capability that could benefit from AI
2. Design it as a composable service with clear APIs
3. Add basic orchestration that can choose between a few options
4. Measure everything so you can see what’s working
5. Iterate and expand based on what you learn

The goal isn’t to build the perfect system on day one. It’s to build systems that can get better over time.

And that’s the real power of composable architecture in the age of AI. Not just building systems that work today, but building systems that will work better tomorrow.