Week 4: Enterprise Deployment & Operations (Days 22-30)

Agenda

• Ship the operating layer around agents—not another chat demo

• Connect gateway, Kubernetes, security, cost, observability, QA, DR, integration, portfolio

• Run one integrated dashboard that proves every domain with live metrics

• Practice production habits: generate → test → start → demo → validate → cleanup

Where Week 4 sits in the journey

Weeks 1–3 taught secure agents, production chat, and multi-agent LLMOps. Week 4 answers what happens after code works: how requests enter safely, how workloads run on clusters, how you prove compliance, control spend, see failures, pass QA, survive outages, talk to legacy systems, and present a portfolio to leadership.

Think of Netflix’s control plane, a bank’s API gateway, or a SaaS “status page”—same discipline, applied to AI agent platforms.

Core concept (the insight most tutorials skip)

Production AI is mostly platform engineering. The model is one component; the rest is rate limits, identity, audit trails, cost caps, traces, load tests, failover regions, and ERP sync. If you cannot observe and govern those, you do not have an enterprise agent—you have a prototype with a logo.

Component architecture:

Data flow:

UI action → REST API → RuntimeState update → audit log → /api/dashboard → KPI refresh.