# Eval Trending — Metrics Over Time

Track evaluation metrics across multiple runs and versions to visualize improvement trends and detect regressions. This addresses the gap of understanding how agent quality changes over time.

## Prerequisites

- At least 2 evaluation runs in the same evaluation group (same `evaluationId` when created)
- Project endpoint and selected environment available in the selected `.foundry/agent-metadata*.yaml` file

> ⚠️ **Eval-group immutability:** Trend a group only when its evaluator set and thresholds stayed fixed across runs. If either changed, start a new evaluation group and track that history separately.

## Step 1 — Retrieve Evaluation History

Use **`evaluation_get`** to list all evaluation groups:

| Parameter | Required | Description |
|-----------|----------|-------------|
| `projectEndpoint` | ✅ | Azure AI Project endpoint |
| `isRequestForRuns` | | `false` (default) to list evaluation groups |

Then retrieve all runs within the target evaluation group:

| Parameter | Required | Description |
|-----------|----------|-------------|
| `projectEndpoint` | ✅ | Azure AI Project endpoint |
| `evalId` | ✅ | Evaluation group ID |
| `isRequestForRuns` | ✅ | `true` to list runs |

> ⚠️ **Parameter guardrail:** evaluation_get expects `evalId`, not `evaluationId`, even if the runs were grouped earlier with `evaluationId`.

## Step 2 — Build Metrics Timeline

For each run, extract per-evaluator scores and build a timeline:

| Run | Agent Version | Date | Coherence | Fluency | Relevance | Intent Resolution | Task Adherence | Safety |
|-----|--------------|------|-----------|---------|-----------|-------------------|----------------|--------|
| run-001 | v1 | 2025-01-15 | 3.2 | 4.1 | 2.8 | 3.0 | 2.5 | 0.95 |
| run-002 | v2 | 2025-01-22 | 3.8 | 4.3 | 3.5 | 3.7 | 3.2 | 0.97 |
| run-003 | v3 | 2025-02-01 | 4.1 | 4.4 | 4.0 | 4.2 | 3.8 | 0.96 |
| run-004 | v4 | 2025-02-08 | 4.0 | 4.5 | 3.6 | 4.1 | 3.9 | 0.98 |

## Step 3 — Trend Analysis

Calculate trends for each evaluator:

| Evaluator | v1 → v4 Change | Trend | Status |
|-----------|----------------|-------|--------|
| Coherence | +0.8 (+25%) | ↑ Improving | ✅ |
| Fluency | +0.4 (+10%) | ↑ Improving | ✅ |
| Relevance | +0.8 (+29%) | ↑ Improving (dip at v4) | ⚠️ |
| Intent Resolution | +1.1 (+37%) | ↑ Improving | ✅ |
| Task Adherence | +1.4 (+56%) | ↑ Improving | ✅ |
| Safety | +0.03 (+3%) | → Stable | ✅ |

### Detecting Regressions

Flag any evaluator where the latest run scored **lower** than the previous run:

| Evaluator | Previous (v3) | Latest (v4) | Delta | Alert |
|-----------|--------------|-------------|-------|-------|
| Relevance | 4.0 | 3.6 | -0.4 (-10%) | ⚠️ **REGRESSION** |

> ⚠️ **Regression detected:** Relevance dropped 10% from v3 to v4. Investigate prompt changes or dataset drift. See [Eval Regression](eval-regression.md) for automated analysis.

### Trend Visualization (Text-based)

```
Coherence   ████████████████████████████████░░░░░░ 4.0/5.0  ↑ +25%
Fluency     █████████████████████████████████████░░ 4.5/5.0  ↑ +10%
Relevance   ████████████████████████████░░░░░░░░░░ 3.6/5.0  ↑ +29% ⚠️ dip
Intent Res. █████████████████████████████████░░░░░░ 4.1/5.0  ↑ +37%
Task Adh.   ████████████████████████████████░░░░░░░ 3.9/5.0  ↑ +56%
Safety      ████████████████████████████████████████ 0.98     → Stable
```

## Step 4 — Cross-Version Summary

Present an executive summary:

*"Over 4 agent versions (v1→v4), your agent has improved significantly across all quality metrics. The biggest gain is Task Adherence (+56%). However, Relevance showed a 10% regression from v3 to v4 — recommend investigating recent prompt changes. Safety remains stable at 98%."*

## Recommended Thresholds

| Severity | Threshold | Action |
|----------|-----------|--------|
| ✅ Healthy | ≤ 2% drop from previous run | No action needed |
| ⚠️ Warning | 2–5% drop from previous run | Review recent changes |
| 🔴 Regression | > 5% drop from previous run | Block deployment, investigate |
| 🔴 Critical | Below baseline (v1) on any metric | Rollback to last known good version |

## Next Steps

- **Investigate regression** → [Eval Regression](eval-regression.md)
- **Compare specific versions** → [Dataset Comparison](dataset-comparison.md)
- **Set up automated monitoring** → [observe skill CI/CD](../../observe/references/cicd-monitoring.md)
