Design patterns for AI agents acting across multi-step tasks, covering how tool access, memory, orchestration topology, and coordination overhead shape whether an agent system works in practice.

AI coding assistants accelerate development but introduce tradeoffs around skill atrophy, codebase design, verification, and security that shape how much value they actually deliver.

Deliberate construction and management of the information fed into an LLM's context window, treated as a first-class engineering problem spanning retrieval strategy, knowledge structure, memory systems, and token efficiency.

A broad category of platforms, libraries, and infrastructure spanning version control, CI systems, language toolkits, AI coding agents, and operational dashboards, increasingly shaped by AI-native patterns and the MCP ecosystem.

MCP is a protocol for exposing tools and context to AI agents; sources debate whether it is the right abstraction layer, a strategic moat, or a limiting constraint analogous to a GUI.