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 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.

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.