In production agentic systems documented across the publicly observable 2025–2026 deployments, the observable band of internal LLM calls per user-facing request sits between 1:18 and 1:60 across documented deployments, with tail cases regularly exceeding 1:400 — meaning unit-economics, latency budgets, and observability scopes built on a 1:1 mental model under-provision by one to two orders of magnitude.

Claim created at publish; review on 60-day cadence (the model-capability and agentic-framework release tempo means the fan-out ratio band can shift within a single quarter). The 1:18 floor is derived from constrained, well-instrumented production deployments documented in LangSmith multi-step agent case studies and the Anthropic Building Effective Agents post (December 2024); the 1:60 upper band reflects research and orchestration agents documented in the Microsoft Magentic-One paper (arXiv 2411.04468) and Helicone cost-per-task analysis; the 1:400 tail reflects unbounded ReAct agents documented in OpenAI Agents SDK warnings and LangSmith trace data. The band is asserted as the observable range across documented case studies, not as a measured median across a systematic sample — the systematic cross-deployment study does not yet exist in the public literature. Three triggers to revisit before next cadence: (a) a frontier model that internalises multi-step reasoning within a single API call, collapsing the billing-layer fan-out ratio, which would require reframing the cost section while preserving the latency and observability sections; (b) a major observability platform (LangSmith, Helicone, Datadog AI) publishing a cross-deployment fan-out ratio study that either validates or revises the 1:18–1:60 band with systematic data; (c) provider-level task-completion pricing replacing per-call pricing, which changes the unit-economics framing without changing the detection or latency implications. Sister claims: AM-013 (Q1 2026 agentic AI thresholds — the detection-time and observability themes are directly related), AM-148 (GPT-5.5 vs Opus 4.7 cost-per-task framing — the fan-out ratio is a multiplier on the per-call cost comparison). The MTTD-for-Agents tool-use-frequency Z-score tripwire described in this article is the primary detection mechanism; full methodology at /mttd/.