Safe and Sound AI is your go-to podcast for staying ahead in predictive and generative AI development. From pre-production design and post-production monitoring to governance and compliance, we deliver bite-sized episodes packed with technical insights and best practices. Designed for data scientists, engineers, trust and safety teams, and business leaders, our focus is to help you deliver and scale AI innovations with safety, trust, and transparency in mind. Safe and Sound AI is brought to you by Fiddler AI.
As AI evolves from single agents into complex, multi-agent systems, the challenge of monitoring and trusting these autonomous collaborators grows. Traditional monitoring tools fall short, unable to interpret the dynamic, unpredictable nature of AI decision-making.
This discussion explores "Agentic Observability," a new approach built to provide deep visibility into an agent's entire operational lifecycle: thought, action, execution, reflection, and alignment.
By understanding the complete reasoning process, this paradigm moves beyond simple monitoring to become a necessary control layer, providing the transparency and trust required to unlock the true potential of sophisticated AI systems.
Learn more:
https://www.fiddler.ai/blog/agentic-observability-developmenthttps://www.fiddler.ai/blog/anatomy-ai-agent
24 Jul 2025
Agentic Observability: The AI Architect's Essential Blueprint
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.
31 May 2025
How to Identify ML Drift Before You Have a Problem
This episode explores machine learning drift, distinguishing between concept and data drift, and details detection methods using statistical tools to maintain model accuracy.
2 Apr 2025
Industry’s Fastest Guardrails Now Native to NVIDIA NeMo
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.
27 Feb 2025
Introducing Fiddler Guardrails: The Fastest in the Industry
In this episode, we explore how Fiddler Guardrails helps organizations keep large language models (LLMs) on track by moderating prompts and responses before they can cause damage. We break down its industry best latency, secure deployment options, and how it works with Fiddler’s AI observability platform to provide the visibility and control to adapt to evolving threats.
Read the article to learn more about how Fiddler Guardrails can help safeguard your LLM Applications.
12 Feb 2025
Should you Observe ML Metrics or Inferences?
In this episode, we explore two key approaches for monitoring AI models: metrics and inference observation. We break down their trade-offs and provide real-world examples from various industries to illustrate the advantages of each model monitoring strategy for driving responsible AI development.
Read the article by Fiddler AI and explore additional resources for more information on how AI observability can help developers build trust into AI services.
12 Dec 2024
Tracking Drift to Monitor LLM Performance
Fiddler AI experts discuss how to monitor Large Language Model performance in production environments via drift monitoring and proactive troubleshooting.
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