Multi-agent systems break where agents coordinate.
This investigation begins much as the last investigation began: with a run that looks workable and a seemingly adequate summary for the supervisor from three individual specialists. The trace looks busy but largely as intended, with a nice stack of model calls as specialists work individually to develop focused answers to well-specified questions from the supervisor. Then there is a human query that changes everything: who knew what when, what did they find out when, and which thing found along the way actually drove the investigation in a particular direction.
Silence.
That silence is the collaboration plane missing from the architecture.
A common architecture for multi-agent systems has a Supervisor issuing requests to Workers, each of which performs a focused task. The results are returned to the Supervisor, which synthesizes the output from all the workers into a final answer for the Human. LangChain describes this as a subagent architecture, where the main agent calls subagents as tools, keeps track of the conversation memory, and uses stateless Worker calls as a way to isolate context and run in parallel (LangChain multi-agent subagents).
