Production AI agent systems can appear healthy by traditional operational measures while delivering a steadily degrading user experience. Latency remains low, tools execute successfully, databases remain healthy, and dashboards stay green, yet responses become increasingly vague, hesitant, and less useful. The problem is not operational failure, but behavioural degradation.

In this session, you will examine five production incidents from LangGraph-based agent systems that expose the gap between infrastructure health and user experience. You will see how unbounded memory injection reduced response quality without triggering alerts, how semantic caching served confidently incorrect answers, why OAuth refresh races emerged under concurrent streaming workloads, how conventional circuit-breaker strategies increased cloud costs, and how observability broke down across Server-Sent Events. Working from production artifacts including logs, quality metrics, traces, latency distributions, authentication failures, dashboards, and code, you will explore the resulting patterns for memory lifecycle management, cache correctness, streaming-safe authentication, latency-aware failure handling, and observability across protocol boundaries.

What You Will Learn

• How behavioural degradation can emerge even when traditional operational metrics remain healthy

• Production patterns for memory management, cache correctness, authentication, failure handling, and observability

• Practical lessons drawn from real incidents, investigations, and remediation efforts in production agent systems

Who Should Attend

AI Engineers, Platform Engineers, SREs and Reliability Engineers, Staff and Principal Engineers, Software Architects, Technical Leads, and teams operating LangGraph, CrewAI, AutoGen, or custom agent platforms.