Multi-Agent Systems Break Differently Than Single Agents

A single agent failing is a tractable problem. You have a bad output, a traceback, maybe a timeout. You fix the prompt or swap the model. Multi-agent pipelines fail differently: one agent produces plausible-looking garbage, the next agent consumes it without complaint, and by the time the third agent produces the final output it's confidently wrong in ways that are nearly impossible to trace back to the root cause. This post covers the mechanics of how failures compound across agent hops, the context propagation problem, and how to instrument a pipeline so you can actually diagnose failures when they happen. The Compounding Failure Problem In a single-agent system, the failure surface is contained. Bad input produces bad output and you can observe both. In a multi-agent pipeline: Agent A → output_A → Agent B → output_B → Agent C → final_output If Agent A produces subtly wrong output, Agent B receives it as ground truth. Agent B may produce output that looks internally consistent but is