AI agents in production fail in complex ways that are hard to diagnose with traditional monitoring.
Develop specialized observability tools that track agent decision paths, API calls, and quality metrics. Provide visualization of agent reasoning and performance bottlenecks.
Start with basic logging and tracing, then add anomaly detection and alerting. Build integrations for popular agent frameworks.
The challenge is keeping pace with rapid changes in agent architectures.