AI App Delivery Top 10: Incomplete Observability
F5, Thursday, June 18th, 2026
AI inference and agent systems need fundamentally different observability than traditional cloud-native apps.
Lori MacVittie argues that incomplete observability is a critical challenge for organizations deploying AI inference and agent-based systems. Traditional monitoring built on averages and periodic dashboards fails for AI workloads that don't follow predictable request paths or repeat workflows consistently.
Modern observability must capture execution lineage, token-level performance data, cost attribution, and agent-declared expectations in near real-time.
Without this visibility, organizations cannot effectively diagnose performance issues or optimize infrastructure costs.