AI agents are LLM-powered systems that plan, act, and iterate autonomously; active research and engineering practice reveal deep tensions between coordination complexity, reliability, tool design, and the human oversight they still require.

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.

LLM inference spans the full stack from VRAM constraints and quantization choices on consumer hardware to latency optimization in production agent services, with tooling debates about transparency, local runtimes, and cost-efficient alternatives to large models.

Systems design is the practice of structuring software components so each can evolve, fail, or be replaced independently; sources here address this through agent architecture, interpreter construction, durable execution, module depth, and container isolation.