[ Level 05 ]
Enterprise Scale
Architecture at scale, team standards, GDPR and data protection, cost strategy, model drift, ethics, and templates.
Architecture at scale
How systems, people, keys, and logs multiply as AI automation grows — and the four architecture patterns and central gateway that keep scale manageable.
Updated 2026-10-05
Team workflows and standards
Why standards are quality assurance, not bureaucracy: the five written agreements, ownership, review flows, and onboarding that keep a growing team predictable.
Updated 2026-10-05
Security and data protection (GDPR, audit)
An educational GDPR overview for AI systems: personal data, the five core requirements, provider questions, incident response in four steps, and audit readiness.
Updated 2026-10-05
Cost strategy at scale
From one system's budget to a cost strategy: tag every euro by system, customer, and environment, set cost ceilings, review quarterly, and cap agent loops.
Updated 2026-10-05
Model updates and drift
Planned model migrations vs. silent provider changes and drift: a six-step migration process, version pinning, monitoring alerts, and a monthly golden-set run.
Updated 2026-10-05
Continuous improvement: from measurement to decisions
A five-stage monthly improvement cycle that turns monitoring numbers into decisions — collect, analyze, decide, change one thing, and verify a month later.
Updated 2026-10-05
Responsibility, ethics, and governance
Who may make which decisions: the decisions table, four ethics questions, a lean risk register, and learning from incidents instead of hunting for culprits.
Updated 2026-10-05
Template library: checklists and samples
The handbook's toolbox: four checklists for go-live, prompt changes, monthly reviews, and model changes, plus templates for prompts, decisions, and risks.
Updated 2026-10-05