From AI Demo to Production: How to Ship Quality Agentic Applications

Shipping AI applications from demos to production requires operational rigor, not just better prompts. AI systems have non-deterministic failure modes, unlike traditional software. To ensure quality, a different quality model is needed, combining practices from classic software engineering and machine learning. This includes type checks, unit tests, integration tests, CI/CD, structured logging, service observability, release discipline, datasets, offline evaluation, online evaluation, model comparison, data quality checks, and drift monitoring.