---
name: automation-shape-routing
description: 'Front door for agent automation — decide the SHAPE (Workflow vs ATM vs skill), then hand off. Triggers: "build automation", "convert skills to workflows", "which shape".'
practices:
- hexagonal-architecture
- team-topologies
- pragmatic-programmer
hexagonal_role: supporting
consumes: []
produces: []
context_rel:
- kind: supplier-to
  with: skill-builder
- kind: supplier-to
  with: workflow-builder
skill_api_version: 1
context:
  window: inherit
  intent:
    mode: task
  intel_scope: none
metadata:
  tier: meta
  dependencies: []
output_contract: 'a routing verdict — Workflow | ATM swarm | plain skill — with the deciding axis named'
---

# Automation Shape Routing — Workflow vs ATM vs Skill

> **The trap this kills:** "I built a lot of skills; they should become
> workflows." Mostly false. Most orchestration-looking skills are either
> long-lived/human-attachable (stay ATM) or hard-sequential (stay skills). The
> win is the routing rule, not a migration project.

## The three shapes

| Shape | What it is | Mechanism |
|---|---|---|
| **Workflow** | Deterministic, reproducible orchestration of subagents | Claude `Workflow` tool — `agent({schema})`, `parallel()`, `pipeline()`, `phase()`, loop-until-budget. In-process, headless, ~16 concurrent. |
| **ATM swarm** | Long-lived, human-in-the-loop multi-agent run | `atm` (the CLI) driven by [`/using-atm`](../using-atm/SKILL.md) — persistent tmux panes running whole `/rpi`/`/evolve` loops over a bead queue, with attach + nudge + kill/relaunch and mail/locks coordination. |
| **Plain skill** | One model reasoning through a procedure or knowledge | A single `SKILL.md`. No fan-out, or a strictly sequential edit-loop. |

## The decision rule (three axes)

Ask in order:

1. **Is there real orchestration at all?** (fan-out / barrier / multi-stage, OR a
   loop with parallelism to exploit) — if **no** → **plain skill**. Stop.
2. **Must a human attach and steer mid-run?** Or does it run for *hours*, do
   open-ended *file edits*, juggle a *fluid population* (rate limits, kill/
   relaunch, prompt-cache rounds), or relay between *cross-model* panes? — if
   **yes** → **ATM swarm**.
3. Otherwise — fixed DAG, agents return **structured JSON** (not free-form edits
   needing review), no attach needed, you want it **reproducible + headless** →
   **Workflow**.

**One-line litmus:**
> deterministic DAG + structured JSON + no human-attach + headless-wanted → **Workflow**
> long-lived + attachable + open-ended file edits / fluid population → **ATM**
> no fan-out, or hard-sequential edit loop → **plain skill**

## Spike-validated nuances (2026-05-29)

A live three-legged spike (`~/dev/agentops-3cat-spike/`) measured the same task on
all three backends. Two findings refine the rule:

1. **The primary axis is control-plane vs in-session, not "parallel vs serial."**
   **ATM is a control-plane** that *runs Claude/Codex/Gemini as panes* — it is not a
   peer of the native runtimes, it is the supervisor tier above them. Choose ATM when
   you need the control plane (attach/steer, persistence, multi-vendor); choose
   in-session native (Workflow/Task) when you don't.
2. **Parallel buys quality/independence, NOT wall-clock — at small N.** Measured: a
   3-way Workflow fan-out **tied** a single sequential agent on wall-clock (191s vs
   180s) and cost **~2.7× the tokens** — because the synthesis barrier eats the
   parallel gain. What it bought was depth + independent fresh-eyes (the sequential
   leg self-reported "monoculture" bias). So: reach for parallel `Workflow` when you
   want *independent verification / fresh eyes*, not for speed. For speed, you need
   large N **and** no barrier — use `pipeline()` (no barrier), not `parallel()`.

Degradation (ATM → Claude-native → beads floor) is governed by the
`OrchestrationPort` selector; opt out entirely with `AGENTOPS_ORCHESTRATION=off` →
beads floor, which always works.

## Two traps to avoid

- **Don't workflow-ify a sequential edit-loop.** If each pass must see the prior
  pass's edits (progressive-deepening reapply, audit-fix-rescan), there's no
  concurrency to win — a Workflow wrapper adds a process boundary for nothing.
  *Exception:* it graduates to a `loop-until-budget` Workflow only once each step
  returns **structured output** instead of free-form edits, and you want it
  headless/reproducible.
- **Don't ATM-ify a clean fan-out, and don't Workflow-ify an attach-and-steer
  run.** The Workflow tool is in-process and cannot be tmux-attached; ATM is
  built for exactly the live-steering Workflow can't do. Picking wrong fights the
  tool the whole way.

## Worked examples

**→ Workflow** (deterministic fan-out / synthesize, structured returns):
`council` (N judges → consensus — near-trivial port), the **planning half** of
`rpi`, judge/refutation panels, any "fan out N analyses → triangulate" task.

**→ Stay ATM** (long-lived, attachable, open-ended edits, fluid population):
the `*-with-atm` family (hypothesis research, cross-model review swarms, browser
testing), plus `swarm`/`crank` in full epic-execution mode — they touch the
working tree and need wave-validity gating + human review.

**→ Stay plain skill** (no exploitable parallelism, or knowledge/one-shot):
deliberately one-at-a-time loops (progressive reapply, multi-pass bug hunting);
all reference docs; all single-shot transforms (jargon scrub, README authoring).

## Canonical Workflow template

`.claude/workflows/operating-loop.js` is the worked example — a real Workflow-tool
script using `agent(prompt,{schema})` with JSON schemas, `parallel([thunks])`
barriers (framing-lenses / judges / refutation / slices), `phase()` markers,
budget-scaled `FANOUT`, and bounded re-plan/retry. **Start from it when porting a
Workflow.** It is also the proof that the AgentOps operating loop has *two*
conformant runtimes (skill-driven via `rpi`/`crank`/`swarm`/`council`, and
Workflow-driven via this script) — the basis of the `agentops-core-sdk`
portability thesis. See `operating-loop-workflow` for the install+run path.

## Handoff — after the verdict, invoke the next skill

This skill is the **front door**. It does not build; it routes. Once the shape is
decided, hand off:

| Verdict | Next | What it does |
|---|---|---|
| **plain skill** | `skill-builder` | Scaffold a new `SKILL.md` against the unified template → then `skill-auditor` → `heal-skill`. |
| **Workflow** | `workflow-builder` | Scaffold a new `.claude/workflows/*.js` from the operating-loop.js template. |
| **ATM swarm** | `atm` + [`/using-atm`](../using-atm/SKILL.md) | Stand up + tend an ATM swarm running AgentOps loops (`/rpi`/`/evolve`) over a bead queue. |

State the verdict and the deciding axis in one line, then invoke the chosen
builder. Do not scaffold here.

## Contract note (SDK)

A Workflow is a **composite capability** (an orchestration of sub-capabilities
with typed control flow); a skill is a **leaf**. The portable contract for this —
a `shape: skill|workflow` discriminator, a `StepGraph`, a `control_flow` enum, a
`budget`, and an `OrchestrationPort` *interface* — is net-new SDK work. Port the
**shape, not the engine**: keep concrete orchestrators (Codex subagents, swarm
dispatch, scheduler — BC4/BC5) behind adapters.