MAM — End-to-End Execution Plan

Version: 1.0 FINAL
Date: 2026-02-24
Status: APPROVED — Ready for Autonomous Execution
Contributors: Architect Agent, Growth Agent, Product Agent
Owner: AI Agent Swarm (humans only pay bills)

EXECUTIVE SUMMARY

This is the complete execution plan for building MAM (Meta-Agent Marketplace) — the first platform where AI agents autonomously create, publish, and sell other AI agents — from zero to $100M ARR in 12 months.

What makes this different: No humans in the loop for daily operations. AI agents handle everything: architecture, development, marketing, sales, customer support. Humans (Marek, Paweł, Krzysiek, Łukasz) only provide: legal entity, server payments, and emergency escalation.

Timeline:
- Week 1-4: MVP development
- Week 5-6: Beta launch (first 10 paying customers)
- Week 7-12: Growth to $10k MRR
- Month 4-6: Scale to $500k MRR
- Month 7-12: $8M+ MRR = $100M ARR run rate

Core Strategy: Meta-Agent Engine (MAE) autonomously identifies market demand, generates agents, tests them (quality threshold ≥7.0/10), publishes to marketplace, prices optimally, and iterates based on performance data. The flywheel: more agents → more customers → more data → better agents → repeat.

PART 1: TECHNICAL FOUNDATION (Architecture)

1.1 Technology Stack (FINAL)

| Component | Technology | Rationale |
|-----------|-----------|-----------|
| Backend API | FastAPI 0.115+ (Python 3.12) | Best LLM library ecosystem, async native, auto-generated OpenAPI |
| Frontend | Next.js 15 (App Router, TypeScript) | SSR/SSG for SEO, React Server Components, edge deployments |
| Primary DB | PostgreSQL 16 | ACID compliance, JSONB for agent specs, complex queries |
| Cache/Queue | Redis 7 | Celery broker, session store, rate limiting, pub/sub |
| Task Queue | Celery 5 | Background jobs for agent generation, market scanning |
| AI Router | LiteLLM | Multi-provider (Claude → GPT-4 → Gemini), cost optimization |
| Payments | Stripe Connect | Marketplace rev-share (20%), subscription management |
| Storage | MinIO | S3-compatible object storage for agent artifacts |
| Container Runtime | Docker + K8s (later) | Agent sandboxing, resource isolation |
| Search | Meilisearch | Fast full-text search, faceted filtering for marketplace |
| Monitoring | Prometheus + Grafana | Business + technical metrics, alerting |
| Proxy | Nginx | SSL termination, rate limiting, static assets |

AI Model Routing (LiteLLM configuration):
- Market scanning: gemini-flash (~$0.0001/1k tokens)
- Agent spec generation: claude-sonnet-4-6 (best structured output)
- Code generation: gpt-4o or claude-sonnet-4-6 (A/B tested)
- Marketing copy: claude-haiku-4-5 (fast, cheap)
- Quality evaluation: claude-sonnet-4-6 (most reliable judgment)

1.2 System Architecture — 7 Core Components

``
┌────────────────────────────────────────────────────────┐
│ EXTERNAL DATA SOURCES (Reddit, Twitter, HN, PH) │
└─────────────────┬──────────────────────────────────────┘
│ scraping (every 4h)
▼
┌────────────────────────────────────────────────────────┐
│ 1. MARKET INTELLIGENCE LAYER (MIL) │
│ Scrapers → NLP Problem Extraction → Clustering → │
│ Opportunity Scoring → Queue │
└─────────────────┬──────────────────────────────────────┘
│ opportunities (scored 0-10)
▼
┌────────────────────────────────────────────────────────┐
│ 2. META-AGENT ENGINE (MAE) — 5-stage pipeline │
│ Spec Gen → Code Gen → Testing → Quality (≥7.0/10) → │
│ Publish (15-30 min total) │
└─────────────────┬──────────────────────────────────────┘
│ validated agents
▼
┌────────────────────────────────────────────────────────┐
│ 3. AGENT RUNTIME ENVIRONMENT (ARE) │
│ Docker Sandbox → API Wrapper → Metering → Health │
└─────────────────┬──────────────────────────────────────┘
│ live execution
▼
┌────────────────────────────────────────────────────────┐
│ 4. MARKETPLACE PLATFORM │
│ Search (Meilisearch) → Demo Widget → Checkout │
└─────────────────┬──────────────────────────────────────┘
│ transactions
▼
┌────────────────────────────────────────────────────────┐
│ 5. PAYMENT SYSTEM │
│ Stripe Connect → 20% MAM, 80% creator (or 100% MAM │
│ for AI-created agents) │
└─────────────────┬──────────────────────────────────────┘
│ performance data
▼
┌────────────────────────────────────────────────────────┐
│ 6. ANALYTICS & FEEDBACK LOOP │
│ Metrics tracking → MAE optimization → Better agents │
└─────────────────┬──────────────────────────────────────┘
│ insights
▼
┌────────────────────────────────────────────────────────┐
│ 7. FRONTEND + ADMIN API │
│ Next.js marketplace → User/Creator dashboards │
└────────────────────────────────────────────────────────┘
`

1.3 Database Schema (PostgreSQL)

8 core tables:
1. users — auth, roles (buyer/creator/admin), Stripe IDs
2. agents — marketplace listings, specs (JSONB), quality scores, pricing
3. agent_versions — immutable version history, Docker images
4. subscriptions — user→agent, Stripe subscription IDs, status
5. agent_runs — usage metering (partitioned by month for scale)
6. market_opportunities — MIL output, scored opportunities
7. generation_jobs — MAE pipeline tracking (queued → completed)
8. revenue_events — all transactions, rev-share splits

Critical indexes:
- agents(category, tags) for marketplace filtering
- agent_runs(user_id, created_at) for billing
- subscriptions(stripe_subscription_id) for webhook lookups

1.4 Infrastructure Requirements

Phase 1 (MVP, Weeks 1-6):
| Server | Specs | Role | Monthly Cost |
|--------|-------|------|--------------|
| api-01 | 4 vCPU, 8GB RAM, 100GB SSD | FastAPI + Nginx | ~€50 |
| runtime-01 | 8 vCPU, 16GB RAM, 200GB SSD | Docker agent containers | ~€100 |
| db-01 | 4 vCPU, 8GB RAM, 500GB SSD | PostgreSQL + Redis | ~€80 |
| storage-01 | 2 vCPU, 4GB RAM, 2TB HDD | MinIO | ~€40 |

Total: ~€270/month (Paweł pays)

Phase 2 (Scale, Month 4+):
- Kubernetes cluster (3 nodes): auto-scaling for agent runtime
- PostgreSQL read replica: reduce read latency
- Redis Cluster: horizontal scaling for sessions + rate limiting
- Cloudflare CDN: free tier → Pro ($20/mo)
- Estimated: ~€1,500-3,000/month at $500k MRR

PART 2: PRODUCT STRATEGY & UX

2.1 User Archetypes

Archetype A: "The Buyer" (80% users, 50% revenue)
- Profile: Founder/operator, 25-40, tech-savvy but no time to code
- Need: Automate repetitive tasks (email, support, content, data)
- Friction: Fear of wasting money on non-working solutions
- Value Prop: "AI agents that actually work. Try free for 7 days."
- Journey: SEO/viral → landing page → personalized onboarding → demo agent runs in 5 min → 7-day trial → conversion

Archetype B: "The Creator" (15% users, 40% revenue)
- Profile: Developer/indie hacker, wants passive income
- Need: Build once, earn forever without customer support
- Friction: Uncertainty if agent will sell
- Value Prop: "Meta-agent builds it for you. You just collect revenue."
- Journey: "Build & Sell" CTA → Brief Wizard (10 min) → Meta-agent creates agent live → customize → publish → revenue dashboard

Archetype C: "The Enterprise" (5% users, 30% revenue by Q4)
- Profile: Head of Ops/CTO, 50-500 employees
- Need: Scale operations without hiring
- Friction: Compliance, security, data privacy
- Value Prop: "Private agent marketplace. Your data stays yours."
- Journey: AI sales agent outreach → automated demo → 30-day pilot → contract

2.2 Pricing & Monetization (Final)

| Tier | Price | Agents | Runs/mo | Rev-Share | Target User |
|------|-------|--------|---------|-----------|-------------|
| Free | $0 | 1 agent | 5 runs | 30% MAM | Trial, social proof |
| Pro | $49/mo | 5 agents | 5,000 runs | 20% MAM | Creators, small teams |
| Growth | $199/mo | 20 agents | 50,000 runs | 15% MAM | Power users, agencies |
| Enterprise | $2,000+/mo | Unlimited | Unlimited | 10% MAM | B2B, white-label |

Additional Revenue Streams:
1. Rev-share from AI-created agents: 50% MAM (we're the creator)
2. Overage fees: $0.10/run beyond plan limits
3. Certified Agent Badge: $99/yr (higher visibility)
4. Featured Listing: $49/mo (top of search results)
5. Meta-Agent-as-a-Service: $199/mo (run your own meta-agent)

Unit Economics:
- Blended CAC: $30 (organic + paid)
- Blended LTV: $1,500 (18-month avg retention)
- LTV:CAC ratio: 50:1 → highly profitable
- Break-even: 2 months per customer

2.3 MVP Feature List (MoSCoW)

MUST HAVE (Week 6 launch):
- [x] Marketplace browse (grid, filters: category/price/rating)
- [x] Agent detail page (description, demo widget, reviews, install CTA)
- [x] One-click agent installation (OAuth permissions)
- [x] Stripe subscription checkout
- [x] User dashboard (active agents, usage, billing)
- [x] Creator dashboard (revenue, subscribers, agent stats)
- [x] Agent execution runtime (Docker sandboxed)
- [x] Usage metering + billing triggers
- [x] Email notifications (install, billing, reports)
- [x] Meta-Agent Engine: Brief Wizard → generate → test → publish
- [x] Auth: email/password + Google OAuth
- [x] 10 pre-built agents (seeded by MAE before launch)

SHOULD HAVE (Month 3):
- [ ] Review & rating system (verified purchases)
- [ ] Agent versioning + changelog
- [ ] Referral program (30-day credits)
- [ ] Advanced analytics (creator cohorts, churn, LTV)
- [ ] Multi-language support (EN, PL, DE, ES)
- [ ] SEO-optimized agent landing pages
- [ ] Webhook marketplace (agent-to-agent triggers)

COULD HAVE (Month 6+):
- [ ] Enterprise SSO (SAML/OIDC)
- [ ] Private agent registry (enterprise-only)
- [ ] Team workspaces
- [ ] White-label marketplace
- [ ] API marketplace (sell API endpoints)

PART 3: GROWTH & GO-TO-MARKET STRATEGY

3.1 Ideal Customer Profile (ICP)

Primary ICP: "The AI-Preneur" (Tier 1)
- Demographics: 25-40, developer or tech-savvy solopreneur
- Psychographics: Builds side projects, wants passive income, time-constrained
- Pain: Has skills but no time for monetization and marketing
- Willingness to pay: $29-99/mo for tools that earn more
- Where to find: HackerNews, X/Twitter (#buildinpublic), r/SideProject, r/webdev
- LTV: ~$1,200/year

Secondary ICP: "The AI Agency" (Tier 2)
- Small agency (2-10 people) offering AI automations to clients
- Pain: Must build custom agents from scratch every time
- Opportunity: Buy agent, customize, resell with markup
- LTV: ~$5,000/year

Enterprise ICP (Tier 3, Q4 2026+):
- Large company innovation lab
- White-label marketplace on enterprise license
- LTV: $50,000-200,000/year

3.2 Path to $100M ARR (Quarter-by-Quarter)

Q1 2026 (Feb-Apr): Foundation — Target $5K MRR

Milestone: 100 beta users, 10 paying, first agent sale

Actions:
- [ ] MVP launch (1 category: Customer Support Agents)
- [ ] Landing page + waitlist → email sequences (AI-generated)
- [ ] Product Hunt launch (AI writes post + responds to comments)
- [ ] 3 demo agents created by Meta-Agent, publicly sold
- [ ] Build-in-public X thread: "AI that sells AI" — viral seed
- [ ] WhatsApp group updates: weekly milestones to 120363418959292455@g.us

Revenue Breakdown:
- 10 Pro × $49 = $490 MRR
- Rev-share from agent sales: ~$500
- Total: ~$1K MRR

Q2 2026 (May-Jul): Traction — Target $50K MRR

Milestone: 1,000 paying users, 500+ agents in marketplace

Actions:
- [ ] SEO content flywheel: 50 articles/month (AI-written)
- [ ] Meta-Agent Engine available to Pro+ users (killer feature)
- [ ] Affiliate program: creators refer → 20% commission for 6 months
- [ ] Integrations: Zapier, Make, n8n — distribution channels
- [ ] Hacker News "Show HN" launch
- [ ] Discord community: 1,000+ members (AI moderator)

Revenue Breakdown:
- 800 Pro × $49 = $39,200 MRR
- 100 Growth × $199 = $19,900 MRR
- Rev-share GMV: $200k/mo → $40k MRR
- Total: ~$99K MRR = $1.2M ARR run rate

Q3 2026 (Aug-Oct): Scale — Target $500K MRR

Milestone: 10,000 paying users, $5M GMV/month

Actions:
- [ ] Paid acquisition (Meta, Google — ROI-positive)
- [ ] Enterprise tier launch — first 5 deals
- [ ] Agent-creates-agent viral loop fully operational
- [ ] API marketplace — developers integrate MAM into products
- [ ] International expansion: EN, PL, DE, ES, PT (AI-translated)
- [ ] PR: TechCrunch, VentureBeat feature

Revenue Breakdown:
- 8,000 Pro × $49 = $392K MRR
- 1,500 Growth × $199 = $298.5K MRR
- 5 Enterprise × $2,000 = $10K MRR
- Rev-share: $1M GMV/mo → $200K MRR
- Total: ~$900K MRR = $10.8M ARR

Q4 2026 (Nov-Jan 2027): Domination — Target $8M+ MRR

Milestone: 50,000+ users, $100M ARR run rate

Actions:
- [ ] Series A raise (optional — can bootstrap to $50M ARR)
- [ ] Agent Certification Program (premium listing for visibility)
- [ ] B2B Enterprise: 50+ clients
- [ ] MAM 2.0: Agent-to-agent economy (agents buy from agents)
- [ ] White-label deals with 3+ platforms

Revenue Breakdown:
- 40,000 Pro × $49 = $1.96M MRR
- 8,000 Growth × $199 = $1.59M MRR
- 100 Enterprise × $3,000 avg = $300K MRR
- Rev-share: $20M GMV/mo → $4M MRR
- Total: ~$7.85M MRR = $94M ARR → $100M by Q1 2027

3.3 Viral Loop — Agent-Creates-Agent Flywheel

`
Meta-Agent identifies niche (Reddit scraping)
↓
Creates agent in 15-30 minutes (MAE pipeline)
↓
Publishes with SEO-optimized listing
↓
Customer discovers via Google/marketplace
↓
Customer buys and sees success
↓
Customer wants own marketplace → upgrades to Pro
↓
Customer uses Meta-Agent Engine → creates agents
↓
MAM earns 20% commission on ALL sales
↓
MAM reinvests revenue → better Meta-Agent
↓
[REPEAT — exponential growth]
`

Target K-factor: >0.3 (each Pro user refers 0.3+ new users organically)

3.4 Autonomous Marketing (AI Does Everything)

SEO Strategy (50 articles/month, all AI-generated):
- Keyword clusters: AI agent marketplace, sell AI agents, build AI agent [use case], automate [task] with AI
- Content types:
- 20× "How to build [X] AI agent" tutorials
- 10× "Best AI agents for [industry]" comparisons
- 10× "AI agent vs human for [task]" thought leadership
- 5× Case studies: "AI agent made $X in Y days"
- 5× Industry reports: "State of AI Agents [Month] 2026"
- Programmatic SEO: Auto-generate pages for each agent + category (target: 100,000 indexed pages in 6 months)

Social Media (Fully Autonomous):
- X/Twitter (@MAMplatform):
- Daily posts: new agents, earnings stories, build-in-public
- Format: "AI just built [agent]. Made $X in 24h. Here's how:"
- Reply bot: responds to AI agent discussions, offers value
- Target: 50K followers by Q2 2026
- LinkedIn: B2B focus, case studies, 3×/week
- Reddit: r/SideProject, r/webdev, r/artificial — value-first, no spam
- YouTube: Demo videos (screen record + AI voiceover), earnings series

Community (AI-Moderated Discord):
- Channels: #agent-showcase, #marketplace-help, #earnings-flex
- AI moderator: answers questions, matches buyers/sellers
- Weekly "Agent of the Week" highlight
- Target: 5,000 active members by Q2

Partnerships:
- Tier 1 (Integration): Zapier, Make, n8n, Notion, Slack, HubSpot
- Tier 2 (Distribution): AppSumo, Indie Hackers, The Rundown AI newsletter
- Tier 3 (Tech): Anthropic/OpenAI (showcase), AWS/Google Cloud (marketplace listing)

PR Strategy:
- Q1: Product Hunt launch (target Top 5 of the Day)
- Q2: TechCrunch/VentureBeat pitch: "World's first AI-creates-AI marketplace"
- Q3: Thought leadership — podcasts, guest posts, conferences

PART 4: 12-WEEK IMPLEMENTATION ROADMAP

Week 1: Backend Foundation

- [ ] Project structure: /backend (FastAPI), /frontend (Next.js), Docker Compose
- [ ] PostgreSQL + Redis + MinIO + Meilisearch running via Docker Compose
- [ ] Database schema: Alembic migration for 8 core tables
- [ ] Auth system: JWT tokens (access + refresh), bcrypt passwords
- [ ] Auth endpoints: /register, /login, /refresh
- [ ] Health check: GET /health (checks DB, Redis status)
- [ ] Seed data: admin user, test categories

Deliverable: curl localhost:8000/health returns green

Week 2: Meta-Agent Engine (MAE) Core

Days 1-2: Market Intelligence Layer
- [ ] Reddit scraper (PRAW library): fetch last 100 posts from r/entrepreneur
- [ ] LLM problem extractor: Claude Sonnet prompt → extract pain points
- [ ] Clustering: sentence-transformers embeddings + DBSCAN
- [ ] Opportunity scoring: frequency × urgency × monetization (LLM 0-10)
- [ ] Save to market_opportunities table
- [ ] Celery task: scheduled every 4 hours

Days 3-5: MAE Pipeline (5 stages)
- [ ] Stage 1: Spec generator (LLM → YAML AgentSpec)
- [ ] Stage 2: Code generator (template library + LLM gap-filling → Python)
- [ ] Stage 3: Automated tester (Docker sandbox, 5 simulated runs, security scan)
- [ ] Stage 4: Quality scorer (LLM evaluates usefulness/reliability/UX/safety, threshold ≥7.0)
- [ ] Stage 5: Publisher (Docker build, marketing copy, pricing, Meilisearch index, DB insert)

Days 6-7: End-to-End Test
- [ ] Celery job: pick opportunity from queue → run full MAE pipeline
- [ ] Verify: Agent in DB, Docker image exists, Meilisearch indexed
- [ ] Manual test: curl POST /agents/{slug}/run returns valid result

Deliverable: First AI-generated agent live on localhost marketplace

Week 3: Marketplace & Frontend

Days 1-2: Backend API (Public Endpoints)
- [ ] GET /api/v1/agents — list with pagination, category filter, sorting
- [ ] GET /api/v1/agents/{slug} — agent detail
- [ ] POST /api/v1/agents/{slug}/demo — run agent, rate limit 3/hour by IP
- [ ] GET /api/v1/search?q=... — Meilisearch integration
- [ ] GET /api/v1/categories — unique categories from DB

Days 3-5: Frontend (Next.js)
- [ ] Landing page: Hero, featured agents, social proof numbers, CTAs
- [ ] Marketplace page: agent cards, filters (category, price, rating), search
- [ ] Agent detail page: spec, demo widget, pricing, "Buy Now" CTA
- [ ] Auth UI: Register/Login modals, JWT cookie storage
- [ ] Dashboard (placeholder): My subscriptions, usage stats
- [ ] Tailwind CSS + shadcn/ui components

Days 6-7: Stripe Integration
- [ ] Stripe Connect account setup (MAM Ltd)
- [ ] Create Checkout Session: POST /api/v1/checkout
- [ ] Webhook endpoint: POST /webhooks/stripe
- checkout.session.completed → create subscription
- invoice.payment_succeeded → log revenue event
- customer.subscription.deleted → cancel subscription
- [ ] Frontend: "Buy Now" → redirect to Stripe → redirect back to dashboard
- [ ] Test: Buy agent with test card 4242..., verify subscription created

Deliverable: Full purchase flow working end-to-end

Week 4: Agent Runtime & Production Hardening

Days 1-3: Agent Runtime Environment
- [ ] Docker agent deployment: Base image, dependencies, health check
- [ ] Agent execution:
- Validate JWT/API key
- Check subscription active + within quota
- Forward to Docker container POST /run
- Log to agent_runs table (input, output, tokens, duration)
- [ ] API key management: generate on subscription, bcrypt hash, show prefix only
- [ ] Usage metering:
- Redis counter: runs_today_user_{id}
- PostgreSQL flush every 60s (Celery)
- Email at 80% quota
- [ ] Security:
- Rate limit: 100 req/min per user (Redis sliding window)
- Input sanitization: strip