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Amit
Nandi
VP, Solutions and Data & AI Architecture
Barclays
With deep expertise in enterprise-scale AI systems, Amit is leading the evolution of infrastructure to support next-generation AI agents where humans interact with autonomous systems through powerful LLM interfaces, and domain-specific models collaborate under agentic control. He will take us through what it really means to operationalize AI at scale from traditional MLOps to LLMOps and now AgentOps—bridging infrastructure, data, models, and user experience to unlock measurable business value.
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01 December 2026 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.