Sharmila Chockalingam

AI agents can look impressive in a demo but behave inconsistently in production. The real challenge is knowing how to improve quality systematically: defining what success looks like, testing agent behavior, identifying failure modes, and making the right changes to prompts, models, tools, and orchestration. Using Claude in Microsoft Foundry, we’ll walk through how teams can define success criteria, compare outputs, test agent behavior, diagnose failures, and iterate through the Foundry control plane. You’ll leave with a repeatable approach for moving agents from experimentation to production with greater confidence while meeting enterprise expectations for data control, governance, procurement, and cloud alignment.