AI Agents / Agent evaluation and observability
AI Agents: Representative task set
Sample real task shapes, difficult inputs and failure cases without exposing private data.
Environment and inputs: Framework-independent agent evaluation; OpenTelemetry illustrates trace correlation.
When to use it
Choose an observable task outcome before optimizing a model or prompt. Keep inputs and scoring rules stable enough to compare candidates, and inspect critical failures individually rather than hiding them inside an average quality score.
Check the result
Compare evaluation distribution with production task categories and note missing segments.
Save and compare results
Save the test input, configuration and result. Fix the failing boundary, repeat the same test and compare it with the saved working configuration.
Scope and limits
These exercises have not been tested on your systems. Check feature support and configuration names against the installed release and linked documentation.