Large Language Models enable workflows to handle tasks that were previously impossible to automate, but they also introduce new challenges around quality, governance, and trust. How do you confidently deploy agentic workflows when outcomes are inherently non-deterministic?

In this session, we demonstrate how Fluxnova's Agentic Subprocess capability can be combined with an open-source evaluation framework to create observable, governed, and continuously improving agentic workflows.

Using a financial services use case, we show how evaluation can be integrated throughout the workflow lifecycle: during development to validate behaviour before deployment, during execution through runtime evaluation and policy checks, and after execution using workflow traces to identify opportunities for improvement.

Attendees will see how Fluxnova combines deterministic workflow orchestration with adaptive AI capabilities, enabling organisations to blend traditional process control with agentic decision-making while maintaining transparency, auditability, and human oversight. The session includes practical implementation patterns and architecture guidance.