Tribloc OS | AI Operations & Workflow Automation

PROBLEM AI-assisted operations need to distinguish proposed actions from authorised actions, handle failures and provide understandable operator workflows.

ARCHITECTURE The documented system combines a TypeScript/Node.js backend, API boundaries, event-driven workflow components and internal n8n orchestration. Testing and operator documentation support staged delivery. Internal deployment details are intentionally omitted.

MY ROLE I set operational requirements and architecture direction, coordinated AI-assisted implementation and made acceptance decisions. Most code was generated or revised with AI coding tools; technical design was also model-assisted.

IMPLEMENTATION Core development and staging paths include workflow lifecycle components, API integration, approval boundaries and failure/retry handling. The supplied case study also documents an infrastructure foundation and operational runbooks.

VALIDATION The October 2026 case study records automated test suites, a scenario-to-test verification report and synthetic staging flows. These are historical repository observations, not a fresh production audit. A staging result does not prove a completed business-runtime deployment.

OUTCOME An implemented development/staging foundation for controlled operations automation. Full business-runtime production cutover remains incomplete; this case study does not imply that every planned workflow or runtime is live.

MEDIA The workflow diagram is a conceptual summary of staged delivery, not a production infrastructure map.

CLIENT RELEVANCE Scoped n8n/Make automation, API integration, failure-path verification, operator documentation and staged rollout planning. Hatchet is part of my broader confirmed toolkit; no unverified Hatchet production deployment is claimed here.