What does the runtime architecture of a real multi-agent system look like?

I think I finally realized my confusion about “AI agents”. Most tutorials/frameworks talk about: * agents * memory * orchestration * multi-agent systems * statefulness …but almost nobody explains the actual runtime architecture clearly. What I’m trying to understand is: If I have multiple agents: * planner * researcher * executor * reviewer that should: * run at different times * share memory/context * communicate with each other * survive restarts/failures * possibly run for hours/days then what does a REAL production setup look like? Are people actually: * running separate Python workers/containers? * using Temporal/Celery/queues? * storing shared memory in Postgres/Redis/vector DBs? * using LangGraph/CrewAI/Praison/etc only as orchestration layers? * relying on Claude/OpenAI managed runtimes instead? Where does “statefulness” actually live in practice? I come from an automation/RPA background, so I naturally think in terms of: * workflows * queues * retries * orchestration * durable execution But agent tutorials often make it sound like autonomous magical entities rather than distributed systems. Would really appreciate explanations from people running real agent systems in production: * architecture diagrams * infra stack * orchestration choices * memory strategies * lessons learned * what NOT to use Especially interested in: * Temporal * LangGraph * Claude Managed Agents * n8n * Windmill * Composio * custom Python approaches * hybrid deterministic + agentic systems