multi agent

Multi-agent is the most over-applied pattern in AI right now. The signal is resisting it unless the task truly needs specialization or parallelism, knowing the supervisor and handoff patterns, and understanding why coordination amplifies failure.

Sub-agents are how you scale a complex agent task without one giant, 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 live or die 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 one good agent.

The most role-defining judgment call in Applied AI: a shiny benchmark number against the operational price of a system nobody can debug at 2am. Most candidates ship on the number and lose the loop there. Here is the answer that passes.