Multi-agent systems—where distinct AI agents collaborate on a task—are a powerful architectural pattern. However, they introduce a valid engineering concern: Cumulative Error Probability.
If a workflow requires 5 sequential agents, and each performs with 90% accuracy, the overall system reliability is mathematically 59% (0.9^5). This can create operational bottlenecks if not managed.
To de-risk these systems, we recommend Flat Architectures. Instead of deep daisy-chains (A -> B -> C -> D), use a central "Orchestrator" pattern.
The Orchestrator holds the state and delegates atomic tasks to specialized workers, reviewing the output at each step. This allows for error correction between steps, preventing a small hallucination early on from cascading into a major failure.