AI Paved Roads

The first LLM tools proved value but did not compound: each had bespoke deployment, no observability and architecture decisions locked inside one repo. The goal became paved roads, making the right way the easy way, for humans and for AI coding agents.

1. A company-wide workflow-automation platform: n8n on Kubernetes in queue mode, with a second fully isolated instance for the operations team on its own tag-triggered deploy pipeline.
2. LLM observability as a platform: a Langfuse deployment plus an installable tracing package, so any agent gets tracing by adding a dependency rather than writing integration code.
3. A paved-road agent template: FastAPI on Lambda with structured logging, health endpoints and CI that scales to a multi-module monorepo, where adding a new agent means copying the template.
4. A boilerplate for AI staff tools with enforced architecture: only the engine layer may touch the LLM SDK, and every folder carries its own rules file with ownership boundaries.
5. A repeatable deployment signature proven on internal dashboards taken from prototype to Kubernetes in two-day sprints, each app aligned to a named reference pattern rather than improvised.

The platform treats scaffolding as a product with users: templates ship with extension documentation, CI that scales to many modules, and explicit architectural boundaries, so downstream teams and AI coding agents inherit judgment rather than files. Those boundaries held when the boilerplate seeded a flagship multi-agent system that reached production in about three months.