Automation Shape Routing

Automation Shape Routing — Workflow vs ATM vs Skill

HEADLINE TRAP — don't orchestrate a one-shot task. Before standing up ANY ATM swarm or Workflow, ask: is this a reusable automation, or a single deliverable I just need produced once? A one-off task ("generate 9 content ideas", "draft this section", "summarize these files") is not an automation — orchestrating it stands up machinery that costs more time than the task. The verdict for a one-shot is shape 0 below: do it inline, or fan out 2–3 in-session Agent subagents. Real failure (2026-06-15): an operator pointed a heavy ATM-codex swarm at a ~9-idea content task; it wedged on codex boot and cost more than doing it inline would have. In-session Agent fan-out later worked in one pass. If you are reaching for ATM or a Workflow, confirm you are not here.

The other 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

The decision rule (axes)

Litmus zero — reusable automation, or a one-shot? Ask this FIRST. Are you building something that will be re-run, or just producing one deliverable? One-shot → shape 0: do not route. Do the task inline, or fan out 2–3 in-session Agent subagents. Don't stand up ATM or a Workflow for a single deliverable. Only continue to the axes below if the answer is "reusable automation."

Then ask in order:

1. Is there real orchestration at all? (fan-out / barrier / multi-stage, OR a loop with parallelism to exploit) — if no → shape 0 (inline / Agent fan-out) for a one-off, or plain skill if it's a reusable procedure. 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.

Cost-check on axes 2–3 (before committing to fan-out). Parallel buys independence / fresh eyes, not wall-clock at small N — a measured 3-way fan-out tied a single sequential agent (191s vs 180s) and cost ~2.7× the tokens, because the synthesis barrier eats the parallel gain. So at small N, parallel is a tax unless you actually want independent verification. If you just want the answer once, the cheapest correct shape is shape 0 — often a single inline pass. (Full evidence under "Spike-validated nuances" below.)

One-line litmus:

one-shot deliverable, not reusable → shape 0 (inline / in-session Agent fan-out) deterministic DAG + structured JSON + no human-attach + headless-wanted → Workflow long-lived + attachable + open-ended file edits / fluid population → ATM reusable procedure, no fan-out, or hard-sequential edit loop → plain skill

Zeroth question, before the three axes: is this an automation at all, or a constraint — a "must never regress" rule promoted from a learning? A constraint is not a process to run; it is a check that blocks. Shape = gate: a warn-only script under scripts/ + a bats case, flipped to blocking after a soak (the ratchet ladder's rungs 3-4). Route it through operationalize (its gate route target), not through the three shapes below.

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

.codex/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:

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.