---
title: Plugin Benchmarking Harness Across Diverse Plan Types
date: 2026-02-10
status: proposal
author: Larry the Laptop Lobster
---

# Plugin Benchmarking Harness Across Diverse Plan Types

## Pitch
Create a benchmark harness that continuously measures plugin quality across a broad matrix of plan domains, complexity levels, and risk profiles so plugin performance is evidence-based, not anecdotal.

## Why
Plugins affect plan quality, but without benchmarking the system cannot identify which plugins are safe, accurate, or robust across contexts.

## Problem

- No consistent evaluation of plugin performance.
- Failures surface late in production plans.
- Plugin quality varies widely across domains.

## Proposed Solution
Implement a benchmarking harness that:

1. Defines standardized test sets of plans by domain and complexity.
2. Runs plugins against these sets under controlled conditions.
3. Scores outputs with objective quality metrics.
4. Publishes coverage and reliability dashboards.

## Benchmark Matrix

Dimensions to cover:

- Domain: infrastructure, software, healthcare, energy, finance
- Complexity: simple, moderate, complex
- Risk: low, medium, high
- Data completeness: sparse, average, rich

## Test Set Design

- Use historical plans plus synthetic edge cases.
- Define “golden outputs” for deterministic tasks.
- Include adversarial inputs for robustness testing.

## Evaluation Metrics

- Accuracy vs known ground truth
- Completeness of outputs
- Consistency across runs
- Failure rate and error types
- Cost and latency impact

## Benchmark Workflow

1. Select plan samples from each matrix cell.
2. Run plugin in isolation with fixed inputs.
3. Compare outputs to baseline and expected structure.
4. Aggregate results into a coverage score.

## Coverage Scoring

Compute a coverage score that rewards breadth and depth:

```
CoverageScore =
  0.40*DomainCoverage +
  0.25*ComplexityCoverage +
  0.20*RiskCoverage +
  0.15*DataCompletenessCoverage
```

## Output Schema

```json
{
  "plugin_id": "plug_551",
  "coverage_score": 0.78,
  "accuracy": 0.84,
  "failure_rate": 0.05,
  "domain_breakdown": {
    "infrastructure": 0.9,
    "healthcare": 0.65
  }
}
```

## Integration Points

- Feeds into plugin hub ranking and discovery.
- Required for runtime plugin safety governance.
- Supports plugin adaptation lifecycle improvements.

## Success Metrics

- Increased plugin reliability across domains.
- Reduced incidence of untested plugin failures.
- Improved user trust in plugin outputs.

## Risks

- High cost to maintain benchmark sets.
- Overfitting plugins to benchmarks.
- Gaps in coverage for emerging domains.

## Future Enhancements

- Continual learning from live production feedback.
- Automated benchmark generation from new plans.
- Plugin performance regression alerts.

## Detailed Implementation Plan

### Phase A — Benchmark Corpus

1. Build scenario matrix by domain and complexity.
2. Define expected contracts and golden outcomes.
3. Add adversarial and noisy input suites.

### Phase B — Runner and Scoring

1. Execute plugins across benchmark suites.
2. Score correctness, robustness, latency, and generalization.
3. Produce composite quality grade with confidence.

### Phase C — Enforcement and Reporting

1. Block production promotion below minimum grade.
2. Publish benchmark reports and trend charts.
3. Trigger re-benchmark on plugin/version changes.

### Validation Checklist

- Coverage breadth across plan domains
- Correlation between benchmark grade and prod outcomes
- Drift detection in plugin quality over time

