A group of AI agents can fail by going silent and stalling, or by confidently agreeing on something false. How do those differ?

This explores the difference between agents that break the protocol (stalling, going silent, failing to converge, which is the classic Byzantine failure, here without bad intent) and agents that get the content wrong (asserting something false or unverifiable). No note in the corpus defines these two terms as a pair. What follows is the line the material draws when you read the notes side by side.

The clearest split is in what a system can promise. In the Honest Quorum work, agreement between validators can be guaranteed deterministically from protocol assumptions alone. Whether the agreed answer is actually correct, and whether the group keeps making progress, rests only on statistical bounds over how validators behave, which the protocol can't enforce Can validator consensus guarantee both agreement and semantic correctness?. So a group can agree perfectly and be wrong together. Consensus machinery handles the first kind of fault and says nothing about the second.

The surprise is which fault shows up most in LLM agent groups. You might expect corrupted values, with agents quietly settling on wrong answers. Simulations instead show groups mostly failing by never finishing, through timeouts and stalled convergence. This gets worse as the group grows, even with no adversarial agents present Can LLM agent groups reliably reach consensus together?. That is a protocol failure that needs no false belief.

The two kinds of fault also come apart in the other direction. A verdict can match ground truth while the agents skipped the verification steps they were required to run Can a correct outcome hide protocol violations in multi-agent systems?. That is a right answer produced by a broken protocol, so checking outcomes alone can't tell you which kind of fault you have. Protocol violations also often live in sequences of actions. Per-action checks can't express a constraint that depends on history, so catching them takes monitors that keep state Can stateless checks ever catch sequence-level constraint violations?.

The content side is harder to inspect. Reasoning traces can omit what actually influenced a decision, or launder problematic reasoning into clean language Can we actually trust reasoning model outputs?. One note argues AI-generated knowledge is structurally like hearsay: it is unattributable and can't be checked against stable sources Does AI-generated knowledge have the same structure as hearsay?. Protocol faults leave evidence you can log, such as a timeout or a skipped step, while a wrong belief may leave none. That asymmetry is my inference from these notes, not something any one of them states outright.