Partnership opportunities

Secure your seat

Call to action
Your text goes here. Insert your content, thoughts, or information in this space.
Button

Back to speakers

Pavithra
Rajendran
Machine Learning Lead
Great Ormond Street Hospital
Pavi Rajendran is a Machine Learning Lead with expertise in transforming advanced research into practical, high-impact solutions within complex environments. At GOSH DRIVE, she leads the Machine Learning team, guiding the technical implementation of advanced ML capabilities, including NLP, Computer Vision, Agentic workflows and Causal Machine Learning. With 8+ years of experience, she has a proven track record of successfully transitioning projects from Proof-of-Concept to Deployment, particularly within the healthcare environment. Her core expertise is in directing the implementation of robust, modular solutions that translate complex, unstructured data into meaningful outcomes for improving patient care. She holds a Ph.D. in Computer Science (NLP) and a Master's degree in Advanced Computing (Machine Learning).
Button
02 December 2025 12:30 - 13:00
Panel | Why do agent systems break in production? The gap between design and reality
Agent systems that perform well in controlled environments can behave very differently once they meet real users, changing data and unpredictable workflows. Tool calls fail, context degrades, edge cases multiply and seemingly minor errors compound across multi-step tasks. This panel will examine the gap between designing an agent and operating one reliably in production. We'll unpack where agent systems most commonly break, why failures are difficult to reproduce and how engineering teams can build for recovery, observability and control from the outset. Key takeaways: → Identify the failure modes that emerge only under production conditions. → Understand how context, tools and multi-step workflows create compounding errors. → Design agents that can recover safely when actions fail or outputs become unreliable. → Build observability and evaluation into the system before scaling deployment.