Testing Performance

Performance baselines are tracked by storing benchmark results in YAML state files. You define load scenarios with specific thresholds, and the system automatically compares current latency and error rate metrics against historical data to detect regressions.

Automated performance regression detection compares current benchmark results against established baselines. By defining load scenarios and thresholds, the system flags performance degradation across various operation types without requiring manual metric analysis.

You can set up load scenarios by asking the assistant to define specific endpoints, background jobs, or CLI commands to track. The system then stores threshold settings and monitors p99 latency metrics against historical baselines using YAML state management.

Yes, YAML state management is required to store and compare latency and error rate metrics against historical data. The system uses YAML files to maintain performance baselines and track benchmark results across various operation types over time.

Yes, this performance monitoring supports tracking CLI commands and background jobs alongside API endpoints. You can define load scenarios for various operation types, establish baselines, and detect regressions across all supported command types.

The best way to monitor post-deployment performance is to establish baselines and define load scenarios with threshold settings. The system automatically compares current benchmark results against historical data to flag any performance degradation after each deployment.