system-design

The article distinguishes between an AI agent's authority to act and having sufficient evidence to justify actions, questioning where the check for adequate evidence should reside in agent systems.

Experienced developers discuss how AI coding agents are transforming their roles into system designers, with concerns about cognitive overhead and the impact on junior developers.

The article reviews the architecture of a chatbot system that converts conversational input into structured data for a constraint solver, emphasizing reliability by separating LLM use for language understanding from deterministic code for decision-making.

The post discusses issues with binding approvals to exact actions in AI agent systems, emphasizing the need for precise mechanisms to handle changes and prevent approval fatigue.

The author explains why a flat agent structure failed in building Hospilot, a multi-agent system for hospital operations, and how adopting a three-level hierarchy (agent → sub-agent → task) improved modularity, planning efficiency, and scalability.

An interactive web-based atlas providing worked examples and architecture diagrams for common system design problems like Twitter feed and chat systems.

A comprehensive guide to core system design building blocks, including client-server architecture, scaling, and databases, to help with system design problems and interview preparation.

An analysis of agent task queues reveals that duplicate claims by different agents can both be verified but only one is counted, leading to invisible waste and issues with self-reported timestamps and lack of exclusion events.

The article explores the concept of 'word coding,' where documents and concepts are structured for AI systems to navigate, offering potential applications in research, writing, and business.

The article lists 10 GitHub repositories essential for system design, including resources for learning scalable architecture and preparing for technical interviews.

The open-source project system-design-101, created by the ByteByteGo team, simplifies system design interview knowledge points through architecture diagrams, has received 84.1k stars, and is suitable for interview preparation and team training.

Google has released a free 1-hour course on Graph Engineering, detailing the transition from single agents to a full 24/7 system with practical guides and self-improving graphs.

The author critiques that many claimed multi-agent systems are actually single agents with multiple roles, often used for marketing appeal rather than technical necessity.

Φ-Bench is a benchmark designed to evaluate large language models on their ability to engineer and optimize the LLM infrastructure stack, covering tasks from kernel optimization to end-to-end system design.

The article explains why AI demos are not suitable for production use and outlines key engineering practices needed to build reliable AI systems.

Engineers are losing grip on their systems due to rapid delivery and higher-order abstractions, which speed up happy paths but prolong failure paths without enough discussion.

The post outlines a mental model for implementing LLM guardrails as a distinct layer for inbound and outbound checks, highlighting the need for enforcement beyond system prompts and the trade-off with latency.

The book 'Grokking AI Applications' by Andrea De Mauro is an illustrated guide that teaches building AI applications like chatbots and agents, emphasizing system design and practical implementation with tools such as Langflow.

The article introduces system design for big tech interviews, explaining the concepts of High-Level Design (HLD) and Low-Level Design (LLD) with examples and key considerations.

The article distinguishes between two types of abstraction in system design: modularity abstraction, which hides internals, and modeling abstraction, which reduces systems to essential behaviors for formal reasoning.