Predictive Coordination of LLM Agents Via Semantic Failure Attribution

Teams of LLM agents often perform worse than a single agent, and recent benchmarks trace the losses to broken expectations and unkept commitments rather than to weak individuals. A coordinator that wants to avoid such failures must learn whom to trust. We prove it cannot learn this from outcomes: in a sequential-stage model of collaboration, a weak agent with a compatible partner and a strong agent with an incompatible partner induce identical success rates for every plan, so any estimator of individual competence suffers an error bounded away from zero, for any number of episodes and under any adaptive policy. Semantic attribution breaks the tie. If failures are labeled by cause through any invertible confusion channel $M$, the parameters become identifiable and estimation is $\sqrt{n}$-consistent at a price ${\sigma }_{\min }(M{)}^{-1}$, so the language model need only be informative, not accurate. On this result we build LPC (LLM-native Predictive Coordination), which gives an LLM coordinator belief filtering over competence and pairwise compatibility, predictive plan scoring, posterior-sampling exploration, and a computable re-coordination threshold as tools under typed contracts. We then measure $M$ for four LLMs on 96 multi-agent coding failures whose cause is fixed by construction. All are informative, but all share one bias: they identify a faulty patch almost perfectly and miss the interaction failure, blaming the implementer for what is in fact a broken agreement, which is precisely the failure outcome data also cannot see. Driven by these measured channels, LPC closes 65% to 70% of the reputation-to-oracle gap, beating plan-level Thompson sampling by a margin that grows with population size.