AI Agents & Applications · intermediate · concept 159 of 176

🤖Multi-Agent Systems & Orchestration

Splitting a task across several LLM agents with separate contexts and roles, coordinated by an orchestrator that fans out subtasks and merges what comes back. It pays off when subtasks are genuinely independent and each needs its own large context. It also multiplies token spend and makes failures harder to trace, so a single well-scoped agent is often the better answer. Research simulacra like Stanford's generative agents showed dozens of LLM agents producing believable social behavior.