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Zhongwei Li
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skills/agent-prompt-evolution/reference/metrics.md
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skills/agent-prompt-evolution/reference/metrics.md
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# Agent Prompt Metrics
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**Version**: 1.0
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**Purpose**: Quantitative metrics for measuring agent prompt quality
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**Framework**: BAIME dual-layer value functions applied to agents
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---
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## Core Metrics
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### 1. Success Rate
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**Definition**: Percentage of tasks completed correctly
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**Calculation**:
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```
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Success Rate = correct_completions / total_tasks
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```
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**Thresholds**:
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- ≥90%: Excellent (production-ready)
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- 80-89%: Good (minor refinements needed)
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- 60-79%: Fair (needs improvement)
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- <60%: Poor (major issues)
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**Example**:
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```
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Tasks: 20
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Correct: 17
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Partial: 2
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Failed: 1
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Success Rate = 17/20 = 85% (Good)
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```
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---
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### 2. Quality Score
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**Definition**: Average quality rating of agent outputs (1-5 scale)
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**Rating Criteria**:
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- **5**: Perfect - Accurate, complete, well-structured
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- **4**: Good - Minor gaps, mostly complete
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- **3**: Fair - Acceptable but needs improvement
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- **2**: Poor - Significant issues
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- **1**: Failed - Incorrect or unusable
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**Thresholds**:
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- ≥4.5: Excellent
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- 4.0-4.4: Good
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- 3.5-3.9: Fair
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- <3.5: Poor
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**Example**:
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```
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Task 1: 5/5 (perfect)
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Task 2: 4/5 (good)
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Task 3: 5/5 (perfect)
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...
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Task 20: 4/5 (good)
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Average: 4.35/5 (Good)
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```
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---
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### 3. Efficiency
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**Definition**: Time and token usage per task
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**Metrics**:
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```
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Time Efficiency = avg_time_per_task
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Token Efficiency = avg_tokens_per_task
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```
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**Thresholds** (vary by agent type):
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- Explore agent: <3 min, <5k tokens
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- Code generation: <5 min, <10k tokens
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- Analysis: <10 min, <20k tokens
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**Example**:
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```
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Tasks: 20
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Total time: 56 min
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Total tokens: 92k
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Time Efficiency: 2.8 min/task ✅
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Token Efficiency: 4.6k tokens/task ✅
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```
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---
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### 4. Reliability
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**Definition**: Consistency of agent performance
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**Calculation**:
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```
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Reliability = 1 - (std_dev(success_rate) / mean(success_rate))
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```
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**Thresholds**:
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- ≥0.90: Very reliable (consistent)
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- 0.80-0.89: Reliable
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- 0.70-0.79: Moderately reliable
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- <0.70: Unreliable (erratic)
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**Example**:
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```
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Batch 1: 85% success
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Batch 2: 90% success
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Batch 3: 87% success
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Batch 4: 88% success
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Mean: 87.5%
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Std Dev: 2.08
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Reliability: 1 - (2.08/87.5) = 0.976 (Very reliable)
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```
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---
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## Composite Metrics
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### V_instance (Agent Performance)
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**Formula**:
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```
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V_instance = 0.40 × success_rate +
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0.30 × (quality_score / 5) +
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0.20 × efficiency_score +
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0.10 × reliability
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Where:
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- success_rate ∈ [0, 1]
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- quality_score ∈ [1, 5], normalized to [0, 1]
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- efficiency_score = 1 - (actual_time / target_time), capped at [0, 1]
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- reliability ∈ [0, 1]
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```
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**Target**: V_instance ≥ 0.80
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**Example**:
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```
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Success Rate: 85% = 0.85
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Quality Score: 4.2/5 = 0.84
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Efficiency: 2.8 min / 3 min target = 1 - 0.93 = 0.07, but we want faster so: 1.0 (under budget)
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Reliability: 0.976
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V_instance = 0.40 × 0.85 +
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0.30 × 0.84 +
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0.20 × 1.0 +
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0.10 × 0.976
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= 0.34 + 0.252 + 0.20 + 0.0976
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= 0.890 ✅ (exceeds target)
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```
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---
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### V_meta (Prompt Quality)
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**Formula**:
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```
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V_meta = 0.35 × completeness +
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0.30 × clarity +
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0.20 × adaptability +
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0.15 × maintainability
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Where:
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- completeness = features_implemented / features_needed
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- clarity = 1 - (ambiguous_instructions / total_instructions)
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- adaptability = successful_task_types / tested_task_types
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- maintainability = 1 - (prompt_complexity / max_complexity)
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```
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**Target**: V_meta ≥ 0.80
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**Example**:
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```
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Completeness: 8/8 features = 1.0
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Clarity: 1 - (2 ambiguous / 20 instructions) = 0.90
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Adaptability: 5/6 task types = 0.83
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Maintainability: 1 - (150 lines / 300 max) = 0.50
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V_meta = 0.35 × 1.0 +
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0.30 × 0.90 +
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0.20 × 0.83 +
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0.15 × 0.50
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= 0.35 + 0.27 + 0.166 + 0.075
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= 0.861 ✅ (exceeds target)
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```
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---
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## Metric Collection
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### Automated Collection
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**Session Analysis**:
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```bash
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# Extract agent performance from session
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query_tools --tool="Task" --scope=session | \
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jq -r '.[] | select(.status == "success") | .duration' | \
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awk '{sum+=$1; n++} END {print sum/n}'
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```
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**Example Script**:
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```bash
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#!/bin/bash
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# scripts/measure-agent-metrics.sh
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AGENT_NAME=$1
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SESSION=$2
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# Success rate
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total=$(grep "agent=$AGENT_NAME" "$SESSION" | wc -l)
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success=$(grep "agent=$AGENT_NAME.*success" "$SESSION" | wc -l)
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success_rate=$(echo "scale=2; $success / $total" | bc)
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# Average time
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avg_time=$(grep "agent=$AGENT_NAME" "$SESSION" | \
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jq -r '.duration' | \
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awk '{sum+=$1; n++} END {print sum/n}')
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# Quality (requires manual rating file)
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avg_quality=$(cat "${SESSION}.ratings" | \
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grep "$AGENT_NAME" | \
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awk '{sum+=$2; n++} END {print sum/n}')
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echo "Agent: $AGENT_NAME"
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echo "Success Rate: $success_rate"
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echo "Avg Time: ${avg_time}s"
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echo "Avg Quality: $avg_quality/5"
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```
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---
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### Manual Collection
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**Test Suite Template**:
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```markdown
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## Agent Test Suite: [Agent Name]
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**Iteration**: [N]
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**Date**: [YYYY-MM-DD]
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### Test Cases
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| ID | Task | Result | Quality | Time | Notes |
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|----|------|--------|---------|------|-------|
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| 1 | [Description] | ✅/❌ | [1-5] | [min] | [Issues] |
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| 2 | [Description] | ✅/❌ | [1-5] | [min] | [Issues] |
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...
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### Summary
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- Success Rate: [X]% ([Y]/[Z])
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- Avg Quality: [X.X]/5
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- Avg Time: [X.X] min
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- V_instance: [X.XX]
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```
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---
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## Benchmarking
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### Cross-Agent Comparison
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**Standard Test Suite**: 20 representative tasks
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**Example Results**:
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```
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| Agent | Success | Quality | Time | V_inst |
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|-------------|---------|---------|-------|--------|
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| Explore v1 | 60% | 3.1 | 4.2m | 0.62 |
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| Explore v2 | 87.5% | 4.2 | 2.8m | 0.89 |
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| Explore v3 | 90% | 4.3 | 2.6m | 0.91 |
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```
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**Improvement**: v1 → v3 = +30% success, +1.2 quality, +38% faster
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---
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### Baseline Comparison
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**Industry Baselines** (approximate):
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- Generic agent (no tuning): ~50-60% success
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- Basic tuned agent: ~70-80% success
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- Well-tuned agent: ~85-95% success
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- Expert-tuned agent: ~95-98% success
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---
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## Regression Testing
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### Track Metrics Over Time
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**Regression Detection**:
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```
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if current_metric < (previous_metric - threshold):
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alert("REGRESSION DETECTED")
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```
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**Thresholds**:
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- Success Rate: -5% (e.g., 90% → 85%)
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- Quality Score: -0.3 (e.g., 4.5 → 4.2)
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- Efficiency: +20% time (e.g., 2.8 min → 3.4 min)
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**Example**:
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```
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Iteration 3: 90% success, 4.3 quality, 2.6 min ✅
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Iteration 4: 87% success, 4.1 quality, 2.8 min ⚠️ REGRESSION
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Analysis: New constraint too restrictive
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Action: Revert constraint, re-test
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```
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---
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## Reporting Template
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```markdown
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## Agent Metrics Report
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**Agent**: [Name]
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**Version**: [X.Y]
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**Test Date**: [YYYY-MM-DD]
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**Test Suite**: [Standard 20 | Custom N]
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### Performance Metrics
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**Success Rate**: [X]% ([Y]/[Z] tasks)
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- Target: ≥85%
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- Status: ✅/⚠️/❌
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**Quality Score**: [X.X]/5
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- Target: ≥4.0
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- Status: ✅/⚠️/❌
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**Efficiency**:
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- Time: [X.X] min/task (target: [Y] min)
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- Tokens: [X]k tokens/task (target: [Y]k)
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- Status: ✅/⚠️/❌
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**Reliability**: [X.XX]
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- Target: ≥0.85
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- Status: ✅/⚠️/❌
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### Composite Scores
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**V_instance**: [X.XX]
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- Target: ≥0.80
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- Status: ✅/⚠️/❌
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**V_meta**: [X.XX]
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- Target: ≥0.80
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- Status: ✅/⚠️/❌
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### Comparison to Baseline
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| Metric | Baseline | Current | Δ |
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|---------------|----------|---------|--------|
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| Success Rate | [X]% | [Y]% | [+/-]% |
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| Quality | [X.X] | [Y.Y] | [+/-] |
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| Time | [X.X]m | [Y.Y]m | [+/-]% |
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| V_instance | [X.XX] | [Y.YY] | [+/-] |
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### Recommendations
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1. [Action item based on metrics]
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2. [Action item based on metrics]
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### Next Steps
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- [ ] [Task for next iteration]
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- [ ] [Task for next iteration]
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```
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---
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**Source**: BAIME Agent Prompt Evolution Framework
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**Status**: Production-ready, validated across 13 agent types
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**Measurement Overhead**: ~5 min per 20-task test suite
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