Per-Agent AI Testing Misses the Real Failures Hidden in Handoffs

A developer running a three-agent AI system — comprising a planner, researcher, and critic — found that despite each agent scoring around 0.9 individually, the overall team produced wrong answers roughly one-third of the time. The root cause was information loss during handoffs between agents, such as a constraint set by the planner being silently ignored by the researcher in the next turn. The author notes that grading agents in isolation is structurally blind to these inter-agent gaps, since failures live in the seams rather than in any single turn. To fix this, the developer shifted focus to evaluating handoffs directly, asking whether constraints were preserved, whether each agent stayed within its designated role, and whether the final output remained consistent with earlier turns. The key insight is that a multi-agent pipeline is a chain, not a collection of independent functions, and its reliability depends on how faithfully each transition preserves what matters.