Arc 11 · AI Architect
Production AI Architecture and Technical Leadership
Integrate models, data, retrieval, tools, safety, reliability, economics, governance, and organizational ownership into durable platforms.
Exit capability: Lead an AI architecture decision from requirements through operations and governance.
- Mapped lessons
- 7
- Published now
- 1
- Full-arc estimate
- ≈20 hours
- Last edited
- 2026-08-11
Lesson sequence
Live units open into complete labs. Planned units stay visible to show the dependency path, but intentionally have no detail route.
- 01
Design a Multi-Tenant AI Gateway
LiveCreate one policy and observability boundary for provider routing, tenant isolation, budgets, fallback, and release evidence.
Systems labAdvanced145 min estimateArtifact: Gateway architecture and capacity model
- 02
Build, Buy, or Partner for AI Capability
PlannedCompare control, differentiation, switching cost, data rights, reliability, and total operating cost.
Design reviewAdvanced105 min estimate
- 03
Draw the AI Platform Boundaries
PlannedDecide what the platform standardizes and what product teams must continue to own.
Design reviewAdvanced110 min estimate
- 04
The Cost–Quality–Latency Operating Envelope
PlannedUse routing, caching, batching, model choice, and graceful degradation within explicit product constraints.
Systems labAdvanced115 min estimate
- 05
Governance as a Control Plane
PlannedEncode inventory, ownership, evidence, policy, approvals, and exceptions into shipping workflows.
Systems labAdvanced110 min estimate
- 06
Migrate a Production AI Stack Without a Flag Day
PlannedDual-run, shadow, compare, canary, reconcile, and retire old paths with rollback intact.
Design reviewAdvanced120 min estimate
- 07
AI Architect Capstone: Defend the System
PlannedProduce and defend a complete architecture under changing scale, safety, cost, and organizational constraints.
CapstoneAdvanced180 min estimateArtifact: Architecture review packet