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.

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.

MCP is a protocol for exposing tools and context to AI agents; sources debate whether it is the right abstraction layer, a strategic moat, or a limiting constraint analogous to a GUI.