I've been building a production SaaS (AI-native nurturing platform) as a solo engineer for the past year. No team, no funding — 14 AI agent skills handling WhatsApp automation, payment processing, CRM, webinar funnels, lifecycle management.

  The biggest lesson: AI writing code is the easy part. The hard part is architecture that lets AI agents collaborate reliably. Without it, agents silently fail — no errors, no logs,
  just events disappearing into the void.

  I extracted the core patterns into OpenForgeAI:

  - EventBus: Singleton pub/sub with auto-wiring (PascalCase event → on_snake_case handler convention)
  - SagaCoordinator: Multi-step workflows with automatic compensation (rollback)
  - Skill Protocol: Composable, idempotent agent skills with typed results
  - ContractValidator: Catches broken agent wiring at deploy time, not runtime
  - HandlerContext: Async-safe per-invocation context via ContextVar (multi-tenant safe)

  The "17 Laws" came from production failures. Example: Law 4 says "no orphan events" — every emitted event must have a subscriber. I learned this when customer invoices silently stopped
   sending because a skill emitted InvoiceGenerated but nothing consumed it. Zero errors logged.

  Single dependency (pydantic). Python 3.10+.

      pip install openforgeai

  GitHub: https://github.com/openforgeai/openforgeai
  Docs: https://github.com/openforgeai/openforgeai/blob/main/docs/architecture.md
  The 17 Laws: https://github.com/openforgeai/openforgeai/blob/main/docs/17-laws.md

  Happy to answer questions about the architecture or the experience of building production systems this way.