Architecture Library
Reference guides for the design patterns that recur across AI systems, distributed backends, and data platforms. what problem each pattern solves and when to reach for it.
RAG (Retrieval-Augmented Generation)
AIGrounding LLM responses in retrieved documents via embeddings and vector search. the standard pattern for domain-specific AI applications.
Read profile →Multi-Agent Orchestration
AICoordinating specialized agents with shared memory, message passing, and a planner or supervisor to complete tasks no single agent handles well alone.
Event-Driven Architecture
SystemsServices communicate through asynchronous events rather than direct calls, improving decoupling and scalability at the cost of debugging complexity.
Microservices
SystemsDecomposing an application into independently deployable services, each owning its own data. trading operational overhead for team-level autonomy.
CQRS & Event Sourcing
DatabasesSeparating read and write models, and persisting state as an append-only log of events rather than mutable rows.
Data Lakehouse
DatabasesCombining the low-cost, schema-flexible storage of a data lake with the transactional guarantees and query performance of a warehouse.
Service Mesh
SystemsA dedicated infrastructure layer (sidecar proxies) handling service-to-service traffic, security, and observability without changing application code.
Serverless & FaaS
Open SourceFunctions that run on-demand without managing servers, scaling to zero when idle. well suited to bursty, event-triggered workloads.
Model Context Protocol (MCP)
AIAn open standard for connecting AI models to external tools, data sources, and services through a consistent client-server interface.