AI Explained: Lessons Learned from Building Agentic Systems
August 14, 2025
10:00AM PT / 1:00PM ET
Registration is now closed. Please check back later for the recording.
Enterprises are moving beyond the hype and are now in the trenches, building and deploying agentic systems. This transition from theory to practice is uncovering a new class of real-world challenges that can derail projects and erode trust. What are the hard-won lessons from the teams on the front lines?
Register for this AI Explained to learn:
- The most common failure points in agentic systems.
- Why traditional testing and evaluations are insufficient for non-deterministic agents and how to shift to a continuous observability mindset.
- Practical strategies for diagnosing failures by tracing the full logic chain — from reasoning and planning to memory access and tool use.
AI Explained is our AMA series featuring experts on the most pressing issues facing AI and ML teams.
Can’t attend live? You should still register! Recordings will be available to all registrants after the event.
Featured Speakers
Jayeeta Putatunda
Director of AI Center of Excellence
at
Fitch Group
Jayeeta is an award-winning Lead Data Scientist and Director at Fitch Ratings, specializing in NLP and Generative AI. Her diverse career path from Economics to Data Science has equipped her with technical expertise and strong business acumen. At Fitch, she develops scalable Generative AI solutions while exploring open-source models. Her achievements include the AI100 award in Generative AI (2024), recognition among Top 25 Visionary Women in FinTech AI, and selection as one of 33 global advisors for NVIDIA's Enterprise Platform Advisor Program. Her research on AI for financial inclusion has been presented at prestigious conferences like ACM's ICAIF and AAAI-25, and she serves as a technical paper reviewer for NeurIPS and COLING.
Krishna Gade
Founder and CEO
at
Fiddler AI
Krishna Gade is the founder and CEO of Fiddler AI, an enterprise AI Observability startup, which focuses on monitoring, explainability, fairness, and responsible AI governance for predictive and generative models. AI Observability is a vital building block and provides visibility and transparency to the entire enterprise AI application stack. An entrepreneur and engineering leader with strong technical experience in creating scalable platforms and delightful consumer products, Krishna previously held senior engineering leadership roles at Facebook, Pinterest, Twitter, and Microsoft.
