How do you build an AI integration layer with proper abstraction and provider flexibility?

An AI abstraction layer decouples application logic from specific AI providers, enabling provider switching, fallback, and A/B testing. Architecture: define a standardized AI interface (e.g., complete(prompt, options) - response), implement provider adapters (OpenAI, Anthropic, Google, local models) behind this interface, add a router that selects providers based on configuration, cost, or request characteristics. Implementation: create an AIGateway class with methods for chat completion, embedding, and moderation. Each method accepts a standardized request format and returns a standardized response. Provider adapters translate between standard format and provider-specific APIs. Router logic: primary/fallback (try OpenAI, fall back to Anthropic on failure), cost-optimized (route simple queries to cheaper models), latency-optimized (route to the provider with lowest current latency), and capability-based (route function calling to providers that support it). Advanced features: request/response logging for observability, token usage tracking for cost management, prompt versioning and management, and response caching. Libraries: LiteLLM provides a unified interface for 100+ LLM providers, or build a custom layer for tighter control. Testing: mock the AI interface for unit tests, use recorded responses for integration tests, and implement provider health checks with automatic failover. This abstraction is critical for production systems where provider outages, price changes, or new model releases require rapid adaptation.