Learn how to take agentic applications from lab experiments to production-grade deployments using AWS Copilot. This workshop shows how to automate infrastructure provisioning, CI/CD pipelines, and integrate standards like the Model Context Protocol (MCP) to extend agents beyond chat into real-world tasks. By the end, you’ll know how to create resilient, scalable, and context-aware AI agents that can truly operate in enterprise environments, while freeing developers to focus on logic instead of infrastructure firefighting.

Agentic systems are non-deterministic—making them harder to debug with traditional logs. This workshop takes you deep into telemetry: instrumentation, observability pipelines, and analysis techniques that capture reasoning loops, tool failures, and system context. You’ll walk away with hands-on methods to turn raw signals into actionable insights, ensuring your autonomous agents remain reliable and explainable in production, even when facing unpredictable environments.

Building machine learning models is only a small part of the challenge in heavy-industry environments. The real complexity lies in deploying, scaling, and operating ML systems that must work reliably on the shop floor—often under strict safety, latency, and reliability constraints. This session walks through the end-to-end journey of building production-grade ML systems for heavy industry, covering data acquisition from industrial systems, model development, validation, and deployment into real-world decision workflows. It will highlight how ML models are integrated with existing operational technology (OT) systems, how predictions translate into actionable shop-floor decisions, and how teams handle issues like data drift, model monitoring, explainability, and human-in-the-loop controls. Attendees will gain practical insights into ML system design, MLOps, and decision engineering in industrial settings, along with lessons learned from taking models out of notebooks and into mission-critical production environments.