Nikhil Thorat

This session is repeated. Measuring the effectiveness of domain-specific AI agents requires specialized evaluation frameworks that go beyond standard LLM benchmarks. This session explores methodologies for assessing agent quality across specialized knowledge domains, tailored workflows, and task-specific objectives. We'll demonstrate practical approaches to designing robust LLM judges that align with your business goals and provide meaningful insights into agent capabilities and limitations. Key session takeaways include: - Tools for creating domain-relevant evaluation datasets and benchmarks that accurately reflect real-world use cases - Approach for creating LLM judges to measure domain-specific metrics - Strategies for interpreting those results to drive iterative improvement in agent performance Join us to learn how proper evaluation methodologies can transform your domain-specific agents from experimental tools to trusted enterprise solutions with measurable business value.