MoonOps: a solo-built multi-agent AI content operation, delivered without spending any of its 250 dollar budget. An orchestrator agent and a cabinet of specialized sub-agents run an agentic production pipeline that must clear a design-quality gate, a security-and-compliance lane, and a human-approval gate before publishing to a live site, video channel, and storefront. Outcomes feed back through closed loops (self-healing skills, performance, revenue, monitoring, and weekly review), and the system is hardened with byte-parity deploys, scoped secrets, schema-validated writes, an audit ledger, and off-box backups.

Governed multi-agent AI operation

MoonOps: the operating model, built not described

A full content operation (website, video channel, and storefront) run by an agentic production pipeline and governed by a cabinet of specialized AI agents with real gates: a design-quality gate, a security & compliance lane, and a human-approval gate before anything ships. Outcomes then loop back into what gets built next, all on a stack that’s hardened by default.

House rule: Stay in Orbit. Every prompt earns its place, or it gets cut.

$0spent of a $250 budget
Soloarchitected & run
8-agentgovernance cabinet
3enforced gates
5feedback loops
Self-healingskill library
3live surfaces
Layer 01

Human oversight

what others call human-in-the-loop

Stay in Orbit

Elliott · operator & principal

Sets strategy and product direction; holds the final publish approval. If the AI starts creating random things, we’ve left orbit; if every prompt supports the mission, we’re in orbit. Oversight is the design, not an afterthought.

Layer 02

Orchestration & governance

Multi-agent system · one agent = one vote · Aria holds design veto

Orchestrator agent

Selene · CEO

Chairs the Council of Equals, routes work to the right specialist, and escalates ties to the human.

Pipeline & MLOps

Lyra · CTO

Growth & SEO

Vega · CMO

Design quality

Aria · CCO

FinOps & monetization

Cassia · CFO

Security & compliance

Vesper · CSO

Product & distribution

Iris · CPO

Site operations

Mara · Director

Layer 03

Agentic production pipeline

Research → generate → render → publish

Step-0 research

Vega market scan

Search demand + SEO before anything is built.

Content generation

affirmation · title · tags

On-brand copy, metadata, and thumbnail.

Procedural render

make_video.py

Video + seamless looped soundscape.

Automated publish

video_upload.py + drip

Scheduled daily uploads via launchd.

Parallel paths

Site CI/CD

ship-site pipeline

Audit → deploy → byte-parity verify (Netlify).

Product build

make_pack.py

Packages downloadable content packs for the storefront.

Layer 04 · guardrails

Governance gates: nothing ships until all three pass

Built in, not bolted on

Guardrail · quality gate

Aria / brand-guardian

Design veto: on-brand and on-craft, or it stops here.

Guardrail · security & compliance

Vesper · CSO

OAuth scopes, platform ToS, secret & credential hygiene.

Human-approval gate

Elliott approves

Explicit human sign-off before anything is published.

Layer 05 · output

Live surfaces

Live today · early / largely pre-revenue

Distribution

Video channel

Long-form ambient video.

Web property

moonops.org

Netlify · self-hosted fonts · hardened headers.

Storefront

Digital store

Downloadable content packs.

Outcomes don’t just ship; they feed back into planning, budget & the agents. See closed-loop control below.
Layer 06 · control

Closed-loop control: the system corrects and improves itself

↻ outcomes re-enter Layers 01 to 03

Self-improvement loop

self-heal

Session digests → skill & agent updates; risky edits proposed, not applied, and all logged.

↩ back to Layer 02

Performance loop

data → next plan

Surface metrics + Step-0 research decide what gets built next.

↩ back to Layer 03

Revenue loop

FinOps (Cassia)

Sales signal → unit economics → budget allocation.

↩ back to Layer 02

Observability loop

run-log + STATUS watch

Monitoring flags failed runs and drift for repair.

↩ back to Layer 01

Retro loop

weekly-review

Cross-workstream read → refreshed top-3 priorities.

↩ back to Layer 01
Feedback re-enters direction, planning & the agents themselves
Cross-cutting disciplines
Hardening

Hardened by default: security, integrity & recovery

Enforced across every surface

Deploy integrity

byte-parity verify

Live site must equal source on every ship.

Self-hosted by default

first-party assets

Self-hosted fonts, hardened headers, no trackers; the one external call is the Spotify embed on the home page.

Secret hygiene

scoped OAuth

Credentials kept out of the repo; least-privilege scopes.

Safe-by-default publishing

draft / posted queue

Nothing auto-publishes without human sign-off.

Data-integrity writes

schema validation

validate_tracker.py runs after every write.

Change auditability

managed blocks + ledger

Every self-heal edit is evidenced and logged.

Sovereign backup & DR

private mirror

State mirrored off-box; restores are drilled, not assumed.

Substrate

Shared infrastructure that underpins every layer

Tool use · MCP

Video platform · Netlify · Storefront · GitHub

Reusable skill library

skills & specialized sub-agents

Persistent memory

CLAUDE.md + memory files

Scheduling · cron

launchd daily cadences

Agents, orchestration & feedback loops Governance gates & hardening Live production surfaces
What it proves

I can architect and govern an end-to-end AI system (production, security, monetization, and distribution) with guardrails, feedback loops, and hardening built in from the start. It ships, monitors itself, learns, and recovers; it doesn’t just run. Early and largely pre-revenue by design, and delivered without spending the $250 budget; the point is the discipline.

It scales straight to a credit union: the same three primitives: capability + governance + adoption.

Fuel wisely. Launch confidently.