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.

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.

LLM orchestration coordinates multiple language model agents through structured pipelines, separating planning, generation, and evaluation roles to produce reliable outputs from long-running autonomous tasks.

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.