Recorded on
with Aishwarya Srinivasan, Arvind Narayanamurthy
Agents are shipping decisions into production faster than enterprises can govern them. Dashboards only tell you what went wrong after it happened. The next phase of adoption will be defined by who operationalizes trust and an AI control plane like Fiddler's is how enterprises govern AI activity as it happens, not after.
Understand how coding agents became part of the software delivery system, and why scaling productivity requires scaling
The limits of post-hoc monitoring, and why LLM-judge costs force sampling that leaves traces as unmonitored
How Fiddler's control plane unifies visibility, policy, cost, and compliance to govern AI activity as it happens.
Agents are shipping decisions into production faster than enterprises can govern them. Dashboards only tell you what went wrong after it happened. The next phase of adoption will be defined by who operationalizes trust and an AI control plane like Fiddler's is how enterprises govern AI activity as it happens, not after.
Solutions Engineering Manager, Fiddler AI · Fiddler AI
Nick Nolan is a Solutions Engineering Manager at Fiddler AI, where he works with enterprises on AI observability, governance, and building more reliable AI systems. He brings deep experience across complex software implementations, privacy, security, GRC, and AI governance, and is also a Fellow of Information Privacy (FIP) with the IAPP.
Hosted by Aishwarya Srinivasan and Arvind Narayanamurthy.
AI Advisor / AI Educator
Aishwarya Srinivasan is the Co-Founder of The Gen Academy and a Developer Relations Lead at Nebius. Her experience spans Fireworks AI, Microsoft for Startups, Google Cloud and IBM, where she worked with developers and businesses on practical AI and machine-learning solutions. She holds a postgraduate degree in Data Science from Columbia University and teaches AI engineering, applied machine learning, generative AI and LLMOps.
Founder @ The Gen Academy | AI Solutions Architect | Ex - Adobe, Microsoft, IBM
Arvind brings a cross-company perspective on AI at scale, built across Microsoft, IBM, and Adobe, where he led data science teams and developed production-grade machine-learning solutions. He has built and deployed enterprise AI systems and focuses on translating AI concepts into practical, production-ready solutions. He holds a Master's from Carnegie Mellon University.
