AI Architecture
System design patterns for AI-powered applications. RAG pipelines, orchestration layers, and infrastructure decisions.
6 articlesin AI Architecture
How to Build a Production-Ready AI System (Azure OpenAI + AI Search — Real Architecture)
Azure OpenAI + AI Search + embeddings — real-world architecture for production AI systems, including legacy data, orchestration, hybrid retrieval, cost control, and failure modes.
Vector Database Selection for Production RAG
Cosmos DB, AI Search, Qdrant, Pinecone — benchmarks, cost, and operational complexity for production vector search.
Multi-Agent Architecture Patterns in Production
Orchestrator, supervisor, and swarm patterns for multi-agent systems with real trade-offs and failure modes.
Event-Driven AI: Building Async Pipelines for LLM Workloads
Service Bus, Event Grid, and queue-based orchestration for AI tasks that don't belong in the request-response path.
The AI Gateway Pattern: Why Every Production LLM Needs One
API Management, rate limiting, semantic caching, and cost control with Azure APIM as an AI Gateway.
Designing RAG Systems That Actually Scale
Chunking strategies, embedding pipelines, retrieval patterns, and when RAG breaks down in production systems.