Multi-agent error cascades: why chained AI agents amplify errors exponentially and how human checkpoints fix it

Short summary

Multi-agent AI systems behave like double pendulums: small errors in early agents cascade exponentially through the chain. Sean Moran's research shows unstructured multi-agent architectures amplify errors 17x compared to single-agent baselines. The fix is human checkpoints between phases—spending 10 minutes of review to prevent 10 hours of debugging broken output.

• •Multi-agent error cascades follow double pendulum dynamics—errors multiply non-linearly across handoffs
• •Unstructured multi-agent architectures amplify errors 17x vs single-agent baselines (Moran, Jan 2026)
• •HumanLayer's RPI methodology inserts human review between research, plan, and implement phases as circuit breakers

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