Zendesk's 20 Billion Conversation Problem

Agentic infrastructure: How legacy stacks create the bottleneck to AI autonomy Multi-agent systems fail in production if the infrastructure underneath them isn't designed for autonomous, long-running loops. Token costs compound across chained tasks, latency bottlenecks surface inside orchestration layers, and agents drift from their original intent with no mechanism to catch this drift before downstream damage is done. This panel brings together agentic AI leaders who have moved past first-generation agentic deployments to deconstruct four key drivers of success: Inference Economics: How teams are managing cost-per-task in long-running agentic loops, including where speculative decoding and purpose-built small language models actually reduce spend versus where they add complexity. Physical Infrastructure decisions: Compute density, storage, memory, GPUs, inference processing units Orchestration Design: When a central master agent creates a single point of failure versus when a decentralized mesh or hybrid approach introduces coordination overhead and how to make that call based on your workload. Governance at the Infrastructure Layer: How teams are embedding rate limits, kill switches, and audit trails directly into the infrastructure stack to contain runaway API calls and prevent cascading failures before they reach production users.