Agenda

Attendees will learn about GPU orchestration, advanced scheduling, autoscaling strategies and architectural decisions that minimize latency and maximize throughput. We’ll also cover storage considerations and practical approaches to deploying models in production. This talk is ideal for engineers and platform teams who want to understand how to operate AI workloads at scale while maintaining reliability and performance across cloud-native environments.

This session is a technical postmortem of a production agent that violated a non-negotiable business constraint without crashing, throwing an error, or missing context. The system appeared successful from the outside, but the outcome was wrong in exactly the way production teams cannot afford: it was helpful, confident, and unsafe.

We will examine why embedding business rules in prompts turns reliability into whack-a-mole. Regression testing becomes guesswork, model changes create unexpected behavior, and critical workflows depend on wording instead of enforceable constraints.

The session then walks through a practical pattern for deterministic control: move business logic back into the application layer, constrain execution with state-driven orchestration, wrap tool calls with validation layers, and test behavior by asserting state changes and tool usage instead of fragile text outputs.

Attendees will leave with a simple principle: let the model handle conversation, but let code enforce the rules.