From Demo to Deployed: Building Production Systems with Claude Code

Overview:

A working session on what changes when Claude Code moves from prototype to production. We cover the architectural decisions, evaluation harnesses, observability, cost guardrails, and human-in-the-loop patterns that separate a polished demo from a deployed system. Examples are drawn from production AI products currently running in industrial manufacturing, property management, and voice-based customer operations.

Why you should Attend:

You shipped the demo. Leadership is asking when it goes live. The path from "it works on my laptop" to "it serves real users without falling over" is harder than the demo made it look, and the cost of getting it wrong shows up in support tickets, security incidents, and erased trust.

Areas Covered in the Session:

• The demo-to-deployment gap and where Claude Code projects most often stall
• Architecting for failure: retry logic, fallbacks, and graceful degradation
• Cost and rate-limit guardrails to build before they bite
• Evaluation harnesses you will actually maintain over time
• Observability for agent systems: logging, tracing, and what to monitor
• Human-in-the-loop patterns for high-stakes outputs
• Versioning prompts, tools, and agent definitions in source control
• Security boundaries, credential hygiene, and least-privilege tool access
• Rollout patterns: shadow mode, canary releases, and progressive exposure
• What to instrument from day one so you can debug at 2 AM

Who Will Benefit:

• Engineering Managers
• Staff Engineers
• ML Platform Leads
• AI Engineers
• ML Engineers
• DevOps and Platform Engineers
• Product Engineers
• CTOs
• VPs of Engineering
• Heads of AI