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 tooling and architectural choices underlying AI agent deployments, covering orchestration strategy, memory systems, observability, and the tradeoffs between single- and multi-agent approaches.

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.