summarize-eval-results

To summarize AI agent evaluation results, you can parse the JSON output into a compact markdown report. This process extracts metrics, pass/fail summaries, and tool call chains to provide a clear, human-readable analysis of your test runs.

The best way to analyze failed scenarios in AI agent testing is to generate a detailed failure report. This includes extracting user prompts, tool call chains, and agent responses from the evaluation JSON to pinpoint exactly why a scenario did not pass.

Yes, you can automatically generate a metrics table from DiskBasedReportingConfiguration JSON. Parsing the JSON output directly creates a structured table that visualizes scenario performance across various evaluation metrics.

You can visualize test metrics for AI agents by converting the raw JSON test output into a markdown summary. This report presents a clear table of scenario performance, making it easier to understand evaluation results and identify issues.

Yes, this reporting approach works without external dependencies for parsing evaluation JSON. It uses built-in scripts to read the test output files and generate the markdown summary report without requiring additional libraries or frameworks.