# Agents launch at full price

*Status: searched 2026-07-31. Both blindnesses are documented on Claude Code
itself; the unified thesis and the cheap-variant pattern are the deltas.*

**Claim:** the model does not economize delegation. Left alone, it
launches as many agents as the task shape suggests — without counting —
and every subagent inherits the strongest available model, because
nothing in the model's sense of a good plan includes the bill. Fan-out
size and lane models are governed from outside or not at all.

## The two blindnesses

- **Count-blindness.** The model sizes a fan-out by the task's structure,
  not its budget: one lane per file, one per dimension, one per question,
  however many that makes. My repo carries a hard cap (at most twelve
  agents per workflow) because sizing was never reasoned — and the cap
  rule records that the owner has questioned too FEW lanes as well as too
  many. It is not that the model over-launches; it is that the number is
  never a decision.
- **Tier-blindness.** Subagents default to the expensive model. The model
  never volunteers "this lane is grep-shaped, use the small model" — the
  economizing thought is simply absent, the same cost-blindness that
  prices a 500k-token demonstration at zero
  ([the demonstration reflex](the-demonstration-reflex.md), which is this
  finding's demo-shaped special case).

## What works

Everything that works is imposed, none of it volunteered:

- **A tier ladder written as caps, enforced by lint.** My repo's
  delegation rule names a model tier per task kind (frontier only for
  load-bearing verification and synthesis; mid-tier for implementation;
  small models for grep-shaped fan-out), makes every tier a CAP rather
  than a floor, requires named owner consent for the top tier — and a
  repo check FAILS any saved workflow lane whose model is unset. The
  fail-closed lint is the load-bearing part: the default it overrides is
  "inherit the expensive one."
- **Cheap variants of expensive skills.** I keep a second version of my
  deep-research skill with the same harness shape — fan out searches,
  fetch, adversarially verify, synthesize — where only scoping and
  synthesis run on the session model; search runs on the smallest tier
  and fetch/verify on the middle one. Same output shape, a fraction of
  the cost. The point is who authored it: the human. The model, asked to
  research something, reaches for the full-price version every time; the
  economical variant exists only because I built it and told sessions
  when to use it.
- **A price on the ask.** Fan-outs above a size threshold are proposed
  with lane count and models named before dispatch, and demonstrations
  are purchases ([the demonstration reflex](the-demonstration-reflex.md)).

## Prior art

**Verdict: PARTIAL.** Both blindnesses are documented, including on Claude
Code itself. Anthropic's own multi-agent research writeup (2025) reports
"early agents… spawning 50 subagents for simple queries" and hand-written
scaling rules as the fix. A practitioner post, "The Subagent Tax" (Systima,
2026-07-22), measures both on Claude Code directly — fan-outs reaching 41,
then 102, then 415 agents, and Explore agents "inherit the parent model
rather than defaulting to Haiku." On the academic side, BAGEN
(arXiv:2606.00198) finds frontier models "consistently over-optimistic" about
their own spend, and BAMAS (arXiv:2511.21572, AAAI 2026) sets agent count and
per-agent tier jointly with an external optimizer, stating that "existing work
rarely addresses how to structure multi-agent systems under explicit budget
constraints." pilotfish's per-role model pins remain closest to the
tier-ladder half. The delta: the two blindnesses as one structural property
(nothing in the model's plan prices tokens), the thesis that every working
delegation economy is human-imposed and machine-enforced, and the
human-authored cheap-variant-of-an-expensive-skill as the practical fix —
none found stated as such.