multi agent

Multi-agent is the pattern most often reached for without cause in AI today. The signal is holding back unless the task genuinely calls for specialization or parallelism, knowing the supervisor and handoff patterns, and grasping why coordination magnifies failure.

Sub-agents are how you scale a complex agent task without one huge, noisy context. The signal is context isolation and clean delegation: a focused subtask in, a compact result out. Here is the answer.

Multi-agent systems succeed or fail on how agents share information and hand off work. The signal is naming the three communication patterns and the coordination failures that sink the naive version. Here is the answer.

Multi-agent systems coordinate through handoffs and shared state. The signal is knowing the orchestration topologies, what a clean handoff protocol carries, and why naive multi-agent often loses to a single good agent.