Production AI agent systems can appear healthy by every traditional operational measure while delivering a steadily degrading user experience. Latency remains low, tool execution succeeds, databases remain healthy, and dashboards stay green. Yet users report responses becoming increasingly vague, hesitant, and less useful over time.

The problem is not operational failure. It is behavioural degradation.

Traditional reliability patterns such as circuit breakers, caching, retries, and distributed tracing were designed to detect systems that stop working. They are far less effective at detecting systems that continue functioning while their behaviour deteriorates.

This session examines five production incidents from LangGraph-based agent systems that exposed a gap between infrastructure health and user experience. Attendees will see how unbounded memory injection reduced response quality over multi-turn conversations 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 once requests crossed into Server-Sent Events (SSE).

Rather than treating these as isolated incidents, the session extracts a set of production patterns for operating AI agents. These patterns address concerns that rarely appear in tutorials or benchmarks, including memory lifecycle management, cache correctness, streaming-safe authentication, latency-aware failure handling, and observability across protocol boundaries.

Throughout the session, attendees will work directly from production artifacts, including incident logs, quality metrics, traces, latency distributions, authentication failures, dashboards, and code. Every lesson is grounded in a real failure, the investigation that followed, and the engineering changes required to prevent recurrence.

Attendees will leave with practical patterns, operational metrics, and architectural guardrails that can be applied across agent platforms, including LangGraph, CrewAI, AutoGen, and custom orchestration systems. The focus is on the production realities of keeping AI agents reliable when infrastructure health and user experience no longer tell the same story.

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
• Architects building agent-based systems
• Technical leads responsible for production AI systems
• Teams operating LangGraph, CrewAI, AutoGen, or custom agent platforms