Enterprise Integration & Production

A prototype proves Claude can do the task; a production system proves it will keep doing it under real load, cost, and scrutiny. This module covers the specifics that close that gap: quality gates, cost and reliability modeling, feasibility analysis, enterprise integration, and the experimentation methods that confirm a change works before it ships.

Learning objectives
By the end of this module, you will be able to:

• Define success criteria and build an eval suite before writing the first line of production code, distinguishing model-based from code-based evals, selecting eval workflow stages, and using evals as the gating mechanism for any change to a production system
• Work through the POC-to-production checklist, mapping cost and latency to a budget, specifying reliability patterns (retries, fallbacks, circuit breakers), naming failure modes for the chosen architecture, and articulating the mitigation for each, including how to make agentic workflows production-reliable
• Create a use case by estimating call volume, token consumption, and cost, assess technical feasibility against the four AI properties from AI Fluency Foundations, and translate a business problem into a scoped solution architecture with explicit boundary conditions
• Architect a Claude deployment that is ready for the enterprise by specifying integration patterns for compliance, identity (SSO/OAuth), authorization, data handling, and observability instrumentation, placing the right integration (API, SDK, MCP, Claude Code) at each integration point
• Plan and interpret an A/B test or structured experiment on a live Claude system, setting the hypothesis, selecting metrics, estimating the required sample size, and reading a result without overclaiming