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.

The practical discipline of building, evaluating, and operating systems that use large language models, spanning knowledge architecture, agent control flow, inference optimization, and the human and organizational costs of getting it wrong.

RAG grounds LLM outputs in external documents at query time, but its limitations around cross-document synthesis have pushed practitioners toward alternatives like compiled knowledge bases that pre-synthesize information into structured, queryable Markdown.

Recurring patterns across component design, API validation, durable execution, and multi-agent systems show that good software architecture consistently pushes complexity to boundaries and keeps individual units of code focused on a single concern.