Eval Breakdown: Question-by-Question Diagnostic Analysis

This skill guides an agent through conducting a rigorous, evidence-grounded, question-by-question narrative diagnostic audit of an agent-eval benchmark run.

1. Input Sources & Directory Anatomy

An agent-eval results folder (tests/eval/results/{run_id}/ or gs://<bucket>/runs/{run_id}/) contains three primary diagnostic artifacts:

1. eval_summary.json: Aggregated metrics, per-question score distributions, and AutoRater judge verdicts.
2. question_answer_log.md: Multi-turn conversational transcripts including user prompts, agent responses, tool calls, and tool outputs.
3. gemini_analysis.md: Automated executive diagnosis and loss cluster summary.

2. Step-by-Step Diagnostic Workflow

Step 1: Generate the Baseline Matrix Table

Extract individual question scores and judge reasoning using the bundled script:

python3 tools/agent-eval/skills/agent-eval/scripts/parse_eval_summary.py \
  --summary-path tests/eval/results/{run_id}/eval_summary.json

Step 2: Perform the Dialogue Audit Protocol

Inspect question_answer_log.md for each scenario, analyzing across:

1. Turn-by-Turn User Intent: What the user requested in Turn 1, Turn 2, etc.
2. Tool Execution & Traceability: Did the agent emit SQL/API tool calls on each turn where factual assertions were made, or did it answer from empirical dialogue memory?
3. The Memory Reuse vs. Traceability Rubric Clash:
   • Diagnostic Pattern: If tool_use_quality drops to 0.00 on Turn 2 follow-ups, verify if the agent answered correctly from dialogue memory without re-querying the backend. If the rubric expects a tool call on every turn, calibrate the rubric or prompt instructions accordingly.
4. Judge Explanation Grounding: Cross-reference any rubric score $< 1.00$ against the exact model response to verify whether the deduction was a genuine agent failure (Tier 1) or an overly strict judge rubric (Tier 2).

3. Executive Output Template

Conclude the audit with a structured summary table: