Prototype to Production (www.kaggle.com)

A new whitepaper titled "Prototype to Production" lays out a practical operational playbook for moving AI agents from lab experiments into enterprise deployments. Building on prior coverage of evaluation and observability, the authors—Sokratis Kartakis, Gabriela Hernandez Larios, Ran Li, Elia Secchi, and Huang Xia—focus on the trust and engineering controls required for production: robust CI/CD, automated testing and validation, reproducible environments, monitoring and alerting, and scalable infrastructure. The guide is explicitly aimed at AI/ML engineers, DevOps teams, and system architects wrestling with the gap between prototypes and reliable, maintainable agent systems. Technically, the paper emphasizes end-to-end lifecycle patterns for deployment, scaling and productionizing agents, including practical pipelines for model and agent updates, canary/blue‑green rollouts, telemetry-driven observability (metrics, logs, traces), and drift detection for data and behavior. It also calls out Agent2Agent (A2A) interoperability as a core challenge—advocating for clear contracts, messaging standards, fallback modes, and orchestration strategies to manage multi-agent interactions while meeting latency and consistency requirements. Overall, the whitepaper translates architectural principles into operational steps that help teams build trustworthy, scalable, enterprise‑grade agent systems.