Sufficiently Advanced Agents Are Indistinguishable from Erlang: An Operational Mapping from OTP to Multi-agent LLM Systems

Abstract

Multi-agent LLM systems are rediscovering the engineering problem that produced Erlang/OTP: how to build systems more reliable than their individually unreliable parts. We treat this convergence as a predictive engineering model rather than an analogy. We develop a term-by-term correspondence between OTP constructs and agent systems, grading twenty mappings as exact, loose, or breaking; the breaking mappings mark the real limits of the transfer: context isolation, payload trust, and mid-inference preemption. The correspondence draws on production experience: Erlang ingest fleets processing millions of tweets per hour, and our present agent runtime's independent reproduction of failure shapes OTP was built to survive: orphaned workers, token-burning polling loops, and sessions held open across hour-scale delays. We then test supervision as a capability multiplier in preregistered experiments that cross supervision architecture with model tier. Across 2,400 episodes and eight model tiers, OTP-disciplined supervision never loses to bare agent loops, leads at four of the five tiers where the arms separate, and shows its largest edge where models partially succeed (pass^4 at 3B: 95% supervised vs. 65% bare; at 32B: 70% vs. 45% with retry and 5% without). A preregistered matched-compute control and a prospective replication (640 episodes in total) rule against the evaluated best-of-N control as the explanation: supervision beat nominally token-matched best-of-N in both (paired deltas +0.33 and +0.26, p <= 0.004), and the preregistered selector added zero successes over its first candidate; compute parity itself was not certified in either run. On hard decomposable tasks, supervised clean-context decomposition lifts a 32B model from 57% to 82%, monotonically from one to sixteen LLM actors, on a prospectively frozen holdout. We close with the problems our work to date leaves open: semantic failure detection, context leakage, and restart economics.