Your agent demo wowed the team. Six months later you are still rewriting it for production. It forgets users between sessions, you have no idea why it failed at 3 AM, it cannot handle 10 concurrent requests, and last month's bill was four times the estimate. The prototype-to-production gap is where AI projects die. The agent works fine in a notebook. The problem is everything around it: persistent memory, monitoring, infrastructure that scales without manual work, and cost controls. Together these take months when they should take minutes. In this session I take a prototype agent to a production endpoint live: cross-session memory in a managed vector store, zero-code monitoring of every decision and token count, auto-scaling that handles spikes and drops to zero, and cost patterns with per-conversation budgets and caching. You'll walk away with: • A production-readiness deployment checklist • Infrastructure-as-code templates that work with any framework • A cost model that predicts monthly spend before you ship

Outline: • The Six-Month Gap • Cross-Session Memory with S3 Vectors • Zero-Code Monitoring and Observability • Auto-Scaling and Cost Optimization • The Complete Picture and Resources