In this episode of Safe and Sound AI, we dive into the challenge of moving AI agents from impressive demos to robust, production-ready systems. We break down the principles of Agentic Observability, explaining how this essential "blueprint" provides the clarity needed to overcome the "black box" problem during both development and production.

Learn practical methods for monitoring key signals like tool usage and planning, discover how to diagnose the root causes of agent failures, and explore strategies for ensuring your agent delivers real-world value.

In this episode of Safe and Sound AI, we dive into the challenge of drift in machine learning models. We break down the key differences between concept and data drift (including feature and label drift), explaining how each affects ML model performance over time. Learn practical detection methods using statistical tools, discover how to identify root causes, and explore strategies for maintaining model accuracy.

Read the article by Fiddler AI and explore additional resources on how AI Observability can help build trust into LLMs and ML models.

In this episode, we discuss the new integration between Fiddler Guardrails with NVIDIA Nemo Guardrails, pairing the industry's fastest guardrails with your secure environment. We explore the setup process, practical implications, and the role of the Fiddler Trust Service in providing guardrails, monitoring and custom metrics. Plus, we highlight the free trial opportunity to experience Fiddler Guardrails firsthand.

Read the article to learn more, or sign up for the Fiddler Guardrails free trial to test the integration for yourself.