Marketing Growth Loop — One Pager

2026-07-16 · Source: 120-day Jira (~260 tickets) + 11 marketing repos, read by Claude subagents; roadmap read from pg-agent-gov repo. Items marked "judgment" are Nathan/Claude analysis, not verbatim source.

1Marketing team work patterns

In one sentence: nearly all recurring work is "one event → fan out a full set of tickets across people, channels, and languages". 18 patterns converge into four families; the loop only absorbs this recurring execution — strategy, creativity, and integration are explicitly excluded, which is the organizational upgrade plan itself (Belle → integrator, Chelsea → CRM planner, Jon → high-value content).

FamilyRepresentative patternsTrigger eventWho does it
Product launch assembly line Launch across four lanes (production site / paid LP / PDP+FAQ+manual / SEO), copy production chain (key message → PDP master → ad variants → translation), proofreading tickets, review deployment New product shipsBelle, Jon, Josh, Francisco, Erica
CRM lifecycle highest volume Post-purchase sequence (manually built day 0→N on each shipment, ~two weeks of work), welcome series, abandon cart, app update notifications Shipment / app releaseTravis sets strategy, Chelsea executes
Promotional cycle Monthly promo, Summer Sale audience fan-out (owner / non-owner / upgrade / PA — one ticket each × email/SMS/push), seasonal calendar, flash sale, creator drop (Kiki Wong etc.) Promo date / new videoTravis, Chelsea, Belle
Platform opsparallel track Localization handoff (always the second serial step), Amazon listing ops, page maintenance, CRM platform migration (Yotpo → in-house) Upstream done / changelogErica, Katie, Jay

2Calvin's agent roadmap — five phases, currently at P2

Calvin's model has two orthogonal layers: functional — each team builds and runs its own agents to production ("you build it, you run it"); governance (pg-agent-gov) — five stages converge the entire fleet, run evaluations, and issue graduation certificates ("gov makes it safe to run"). The five stages as recorded in the gov repo:

P0
Chassis
Scaffold
P1
Contracts
(mostly done)
P2
Eval
(in progress)
P3
Govern
(deferred)
P4
Graduate
(0 so far)

Trust ladder: T0 sandbox → T1 shadow → T2 production (eval-gated) → T3 autonomous. Current state: all 12 fleet agents are tier: pending, zero graduated; 4 are actually in production (web / paid-media / content / marketing-pm) but none has been formally signed off.

Why P2 is stuck (judgment): keystone gap = no platform-level definition of "good." Langfuse is connected, but no golden set built, no judge calibrated, eval scores for all 12 agents are null. "cmo-agent directs the entire fleet" and "non-tech staff do 90% via agent" are north-star visions — no mechanism exists in the repo. pg-agent-marketing Phase 0 built 2026-07-16 (contracts + judge + calibration + outcome write-back) is the marketing instance that fills exactly this gap — the same structural problem as ERP's "no one satisfies the graduation evidence mechanism," solved the same way.

3Platform architecture — who lives where

Each layer is a place. Read top-down: people touch only the top layer; everything below is machinery.

People — Dashboard & Slack

marketing team · Google sign-in · no GitHub accounts
ApprovalsLabeling & feedbackStatus boardSlack bot

App plane — Supabase

runtime state · role permissions · every click audited
Commands queueApproval queueEvent logCalibrationCampaign status

AI runtime — local runner

subscription Claude Code · standby + heartbeat
OperatorJudge + oracleRisk gatesPublisherCalibrator

git repos

thick agents keep their own context home · thin capabilities become skills in the operator
operatorcmo-agent (taste)paid-mediacontentdesigncommercemarketing-pmdashboard app

Data hub — BI core (BigQuery)

BI team owns everything that enters · agents read via keyed bi CLI · BI's own sync pulls our results in
bi CLI + team keysmart_* viewsOutcome truthLabel ingestion (pull)

Channels

receive actions · their data replicates into BI on schedule
ExponeaShopifyMeta / Google / Amazon AdsGA4 · App Store

3bThe loop — how one campaign travels

Business eventBI detects: shipment · promo date · anomaly
Plan & fan-outoperator drafts the strategy + candidates
Machine judgerubric + track record · abstains when unsure
Risk gates & routingmoney · claims · irreversible → human
Human approvalads: the plan, once · content: flagged items
approved paths converge
Execute on channelsbatch publish · bid tuning stays inside the approved budget
Measureresults land in BI via the BI team's pipelines
Learnoutcomes join judgments → calibration & lessons
next cycle: machine judges more
Honest note: what exists today is the judgment machinery (operator repo, 44 tests) and marketing-pm; the dashboard, Supabase schema, runner, and BI hookup are design, not yet built.
Humans appear in exactly one box. Everything else is machinery — and the return arrow is the whole point: every measured result makes the next judgment better, so the human box keeps shrinking.