Autonomous systems that plan, act, and verify across tool calls and multi-step workflows, with active debate over architecture choices, coordination costs, memory design, state management, and the governance infrastructure needed to make them reliable.

The systems, abstractions, and operational layers that make AI models usable at scale, from compute and caching to routing, governance, agent hosting, and credential management.

LLM orchestration covers the control structures, harness designs, and coordination patterns that govern how language models are invoked, sequenced, and supervised — whether in single-agent loops or across distributed multi-agent pipelines.

MCP is an open protocol for exposing tools and context to AI agents; sources debate whether it belongs in developer workflows or enterprise governance layers, while implementations range from code intelligence servers to token-compression proxies.

Observability spans infrastructure, distributed systems, and AI agents — the practice of making system internals legible through traces, events, and feedback signals so engineers can understand, debug, and improve what they've built.