The Coming Era of Composable Intelligence: Designing Abstractions Across LLMs

We're building software backwards.

For decades, we've obsessed over abstractions that hide complexity—ORMs that obscure SQL, frameworks that mask HTTP, cloud services that abstract away infrastructure. We've become masters at creating clean interfaces over messy implementations.

But with LLMs, we're doing the opposite. We're writing raw prompts, hardcoding model names, and coupling our business logic directly to specific AI providers. We're treating Claude, GPT-4, and Gemini like databases without connection pools, APIs without rate limiting, or services without circuit breakers.

The developers building AI applications today are making the same architectural mistakes we made with databases in the 1990s. And just like then, the solution isn't better prompts or smarter fine-tuning.

The solution is better abstractions.

The Current State: Primitive Coupling

Walk through any AI codebase today and you'll see code that would make a senior architect wince:

# This is how most AI apps work today if model == "gpt-4": response = openai_client.chat.completions.create( model="gpt-4", messages=[{"role": "user", "content": prompt}], temperature=0.7 ) elif model == "claude": response = anthropic_client.messages.create( model="claude-3-sonnet-20240229", messages=[{"role": "user", "content": prompt}], max_tokens=1000 ) 

This is 1995-era database code. Hardcoded connection strings, vendor-specific SQL dialects, and zero abstraction between business logic and data access. We learned better with databases. We built ORMs, connection pools, and database-agnostic query builders.

We need the same evolution for LLMs.

The Architecture We Actually Need

The future of AI development isn't about prompt engineering. It's about intelligence architecture—designing systems where different AI capabilities can be composed, swapped, and orchestrated without rewriting your entire application.

Think of each LLM not as a monolithic service, but as a collection of specialized capabilities:

• Reasoning engines (Claude 3.5 Sonnet for complex analysis)
• Code interpreters (GPT-4 for programming tasks)
• Creative generators (various models for different content types)
• Fast responders (smaller models for quick interactions)
• Multimodal processors (vision-enabled models for image analysis)

The abstraction layer should let you compose these capabilities like microservices, routing requests based on complexity, context, and performance requirements.

Learning from Distributed Systems

The patterns we need already exist in distributed systems architecture. We just need to apply them to AI:

Circuit Breakers for Model Failures
When Claude is down, automatically route to GPT-4. When GPT-4 is rate-limited, fall back to local models. Build resilience into the intelligence layer the same way we build it into API gateways.

Load Balancing Across Models
Route simple queries to fast, cheap models. Escalate complex reasoning to premium models only when needed. Think of it as intelligent tiering—like caching strategies, but for cognition.

Canary Deployments for Model Updates
When a new model version releases, gradually shift traffic while monitoring output quality. Roll back if performance degrades. Treat model updates like any other dependency change.

Service Mesh for AI
Create a unified interface that handles authentication, rate limiting, monitoring, and routing across all AI providers. Your application code shouldn't know or care which specific model is handling the request.

The Composability Problem

The real challenge isn't technical—it's conceptual. We need to stop thinking about LLMs as magic black boxes and start thinking about them as composable intelligence primitives.

Each model has different strengths:

• Claude excels at nuanced reasoning and following complex instructions
• GPT-4 handles code generation and mathematical problem-solving
• Gemini processes multimodal inputs effectively
• Local models provide privacy and cost control

But today's AI applications treat model selection as a binary choice. You pick one model and stick with it. This is like choosing a single database for all your data needs—sometimes you need Redis for caching, Postgres for transactions, and Elasticsearch for search.

Intelligent applications should compose models dynamically.

For a complex analysis task, you might:

1. Use a fast model to classify the request type
2. Route to a reasoning-heavy model for the main analysis
3. Use a code-specialized model to generate examples
4. Employ a creative model to format the final output

Each step uses the optimal intelligence for that specific subtask.

The Abstraction Layers We're Missing

Building composable AI systems requires abstractions at multiple levels:

Capability Abstraction
Instead of calling specific models, call capabilities: intelligence.reason(), intelligence.generate_code(), intelligence.analyze_image(). The abstraction layer handles routing to the best available model for each capability.

Context Management
Conversations and context should persist across model switches. If you start a coding session with GPT-4 and fall back to Claude due to rate limits, the context should transfer seamlessly.

Quality Assurance Layers
Build automated quality checks that evaluate outputs and re-route to different models if the first attempt doesn't meet standards. Think of it as integration testing for AI responses.

Cost Optimization
Automatically route to the cheapest model that can handle the request quality requirements. Start with fast, inexpensive models and escalate only when necessary.

Tools That Enable This Future

Platforms like Crompt AI are already moving in this direction by providing unified interfaces across multiple models. But we need more than just multi-model chat interfaces. We need:

Developer-First API Abstractions
Tools that let you define intelligence workflows without hardcoding model names. Something like:

pipeline = IntelligencePipeline() .classify(complexity="auto") .route_by_capability() .with_fallbacks() .monitor_quality() 

Context-Aware Routing
Systems that learn which models work best for your specific use cases and automatically optimize routing based on historical performance.

Intelligence Observability
Monitoring and debugging tools that show you how requests flow through different models, where bottlenecks occur, and how to optimize your intelligence architecture.

You can experiment with multi-model approaches today using tools like Claude 3.7 Sonnet for complex reasoning, GPT-4o mini for quick responses, and the AI Research Assistant for deep analysis—but doing so manually rather than through architectural abstraction.

The Design Patterns Emerging

Smart teams are already developing patterns for composable AI:

The Strategy Pattern for Models
Define a common interface for intelligence operations and implement different models as strategies. Switch strategies based on context, performance, or availability.

The Observer Pattern for Quality Control
Set up observers that monitor AI outputs and trigger fallbacks or re-routing when quality thresholds aren't met.

The Factory Pattern for Model Selection
Create factories that instantiate the right model configuration based on request characteristics, user preferences, or system state.

The Adapter Pattern for Provider APIs
Build adapters that translate between your application's intelligence interface and each provider's specific API format.

The Challenges Ahead

Building truly composable AI systems isn't trivial. We're dealing with challenges that don't exist in traditional software:

Semantic Consistency
Different models might interpret the same prompt differently. Ensuring consistent behavior across model switches requires careful prompt design and validation.

Context Transfer
Moving conversations between models while maintaining context and personality is complex. Unlike stateless API calls, AI interactions are inherently stateful.

Quality Measurement
How do you automatically assess whether Claude's response to a complex reasoning task is better than GPT-4's? Quality metrics for intelligence are harder to define than response times or error rates.

Cost Prediction
Token costs vary dramatically between models and use cases. Predicting and optimizing costs across a composed system requires sophisticated modeling.

The Developer Experience We're Building Toward

The future AI development experience should feel like working with any other well-designed API:

# What we're building toward intelligence = ComposableAI() .with_reasoning_model("claude-3.5-sonnet") .with_code_model("gpt-4") .with_fallbacks(["local-model"]) .with_quality_gates() result = intelligence.analyze( content=document, requirements=["extract-key-points", "generate-summary", "suggest-actions"], quality_threshold=0.85, max_cost_per_request=0.10 ) 

The abstraction handles all the complexity—model selection, fallback logic, quality assurance, cost optimization—while exposing a clean, predictable interface to your application code.

The Shift in How We Think

This architectural evolution requires a fundamental shift in how we think about AI in software systems.

Instead of asking "Which model should I use?" we should ask "What intelligence capabilities does this feature require?" Instead of optimizing prompts for specific models, we should design intelligence workflows that can adapt to different cognitive engines.

Instead of treating AI as a magical external service, we should treat it as a architectural concern—something that requires the same careful design, monitoring, and optimization as any other critical system dependency.

The Road Ahead

We're at the beginning of this transition. Most AI applications today are still in the "hardcoded database connections" phase. But the patterns are emerging, the abstractions are becoming clear, and the tools are starting to appear.

The developers who understand this shift—who start building composable intelligence systems today—will have a significant advantage as the AI ecosystem matures. They'll build applications that are more resilient, more cost-effective, and more adaptable to the rapid pace of AI innovation.

The age of prompt engineering is ending. The age of intelligence architecture is beginning.

The question isn't whether this transition will happen. The question is whether you'll lead it or follow it.

-Leena Malhotra :)