System Design
37 articles on system design.
System DesignFault Tolerance for AI Agent Pipelines: Retry and Recovery Patterns
How to build a fault tolerant AI agent pipeline with retries, circuit breakers, checkpoints, dead-letter queues, and graceful degradation so one failed step never crashes the whole run.
System DesignHierarchical Agent Architecture: The Supervisor Agent Pattern
The supervisor agent pattern turns a chaotic flat multi-agent system into a clean hierarchy. Here is how to design the routing, state, and error recovery.
System DesignCascading Failure: When One Command Takes Down Everything
How a single mistyped command sparked the 2017 AWS S3 outage, why cascading failures spread through dependencies and retries, and the patterns that stop them.
System DesignUber System Design: Geospatial Matching, Surge Pricing, ETA
Design Uber the way interviewers want: a spatial index at the center, with geospatial matching, real-time surge pricing, and ML-based ETA prediction built on top.
System DesignZero Downtime Database Migration: The Cassandra Story
A zero downtime database migration lets you move off Cassandra while users keep reading and writing. Here are the five phases, using Discord's move to ScyllaDB as the example.
System DesignSynchronous vs Asynchronous Communication Explained
The choice between synchronous and asynchronous communication shapes latency, failure modes, and coupling. Here is how request-response, fire-and-forget, and event-driven differ, and when to use each.
System Design4 Caching Strategies for System Design
Master cache-aside, read-through, write-through, and write-behind patterns. Learn when each strategy wins, what breaks, and how to explain the tradeoffs in system design interviews.
System DesignAI Guardrails: How to Build a Safe LLM Application
AI guardrails are the safety layers around a language model: input filtering, output validation, content classification, and human-in-the-loop escalation. A system design guide with code.
System DesignVector Embedding Models: Generation, Versioning, and Drift
How to run vector embedding models in production: batch and real-time generation, versioning without breaking your index, drift detection, and incremental updates.