In this talk, we'll explore the essential techniques for developing generative AI applications that are not only powerful but also reliable and transparent. By leveraging the combined capabilities of PydanticAI and Logfire, developers can create systems that deliver consistent results while maintaining full visibility into their operations.

We'll begin by examining how to create and configure PydanticAI agents, demonstrating how these structured components can form the backbone of sophisticated AI workflows. This foundation will be enhanced through a detailed exploration of Logfire monitoring implementation using MCP servers, providing a robust observability layer for your applications.

The discussion will then shift to evaluation methodologies, offering practical approaches to assess and validate your AI applications' performance and accuracy. We'll delve into the advantages of structured
outputs, showing how they enable more predictable and testable agent responses across various scenarios.

Finally, we'll investigate how real-time insights can transform your troubleshooting process, allowing teams to quickly identify bottlenecks and resolve issues before they impact users. By the end of this
session, you'll have a comprehensive understanding of how these tools and techniques can elevate your generative AI projects to new levels of reliability and observability.