Originally published on Loop & Retry — field notes on building LLM agents that survive production.
The pitch for multi-agent systems is redundancy and specialization: split the task, let a planner plan and a critic critique, and the whole is more reliable than the parts. Sometimes. But a team of agents also opens a class of failures that a single agent simply cannot have — failures of coordination, not of competence. And several of them get worse, not better, as you add agents.
This is the companion to how agent failures cascade: that post was about one bad step propagating down a single trajectory. This one is about the failures that need more than one actor to exist at all.
Failure mode 1: correlated collapse
The redundancy argument assumes independence. Three agents voting on an answer beats one if their errors are uncorrelated — then a majority is unlikely to be simultaneously wrong. But agents on the same base model, given the same prompt framing, fail in the same direction. They share the base model's blind spots, they read the same poisoned tool output, they inherit the same ambiguous instruction. Their errors are correlated near 1, so the "vote" doesn't average out error — it amplifies confidence in it. You get three agents agreeing on the wrong answer and a system that now reports high consensus, which downstream logic reads as high reliability.
