monitoring

Multiple overlapping bugs with different symptoms, affecting different platforms at different rates, made diagnosis and root-cause analysis extremely difficult. Load balancing changes increased affected traffic unexpectedly, creating contradictory user reports.

76% of developers won't use AI for deployment/monitoring, and 69% avoid it for project planning. High-responsibility, systemic tasks carry too much risk for unverified AI outputs. This reflects both capability and trust gaps.

Bugs that don't appear in local development surface only in production due to differences in data volume, third-party integrations, and real-world user behavior. Without proper logging and monitoring infrastructure, tracking and reproducing these issues becomes time-consuming.

Many teams lack observability tools to monitor and understand system behavior, causing end users to discover issues rather than development teams catching them proactively. Without observability, teams cannot assess the full scope of undiscovered bugs and errors.