Evaluation driven development for Multi agent enterprise AI platform

About this session

Evaluation-Driven Development for Enterprise AI Agents

Building an AI agent is relatively easy. Knowing whether it is reliable enough for real users—and whether the next release made it better or worse—is much harder.

As enterprise AI moves from prototypes to production, traditional software testing is no longer sufficient. Agent behavior can change with a new prompt, model, retrieval strategy, tool, knowledge source, or orchestration decision. Teams need a development discipline where evaluation is continuous, measurable, and built into the lifecycle rather than performed as a final validation step.

This session presents a practical approach to Evaluation-Driven Development (EDD) for enterprise AI agents, grounded in lessons from building and operationalizing agentic systems in a regulated enterprise environment.

We will explore how real user questions and feedback can be transformed into representative evaluation datasets; how to evaluate retrieval, response quality, tool use, routing, and end-to-end agent behavior; and how automated regression testing can identify failures as systems evolve. We will also examine where automated metrics fall short and business or human validation remains essential.

The goal is to move beyond “the demo works” toward an engineering model in which every meaningful change can answer a more important question: Did we actually make the AI system better?

Attendees will leave with a practical blueprint for building evaluation into the development and release lifecycle of production AI agents.

Speaker

Key takeaways

  • How to turn real user behavior into an AI evaluation strategy
  • How to build continuous regression testing for agents
  • How to use evaluation as an engineering discipline—not a final QA step

Related sessions