Case Study: Cresta achieves multi-service AI agent observability with Langfuse

Cresta speeds agent debugging in seconds with Langfuse

Cresta, an enterprise generative AI platform for contact centers, faced the challenge of gaining observability into their complex, multi-service AI agent pipelines. These pipelines involve numerous steps like intent detection and knowledge retrieval, making it difficult to trace failures, debug issues, and improve model behavior. They needed a solution to trace each step across different services while maintaining strict data sovereignty and multi-tenant isolation for their customers.

Cresta implemented the self-hosted version of Langfuse to organize AI execution into typed trace trees, providing granular observability into each step of their pipelines. The solution enabled cross-service trace propagation and ensured data never left their Kubernetes clusters, fulfilling their sovereignty requirements. As a result, Langfuse significantly sped up the iteration cycle for improving LLM behavior, allowing engineers to test prompt fixes in seconds instead of waiting for production traffic and making observability a prerequisite for their AI agent quality.

Cresta