SolutionDiscriminatorAgent_Tutorial.md

SolutionDiscriminatorAgent Tutorial: Building an Intelligent Solution Evaluator

Introduction

This tutorial will guide you through using the SolutionDiscriminatorAgent to objectively evaluate and compare multiple solution candidates. We'll start with basic comparisons and progress to sophisticated multi-criteria evaluations used in real-world decision-making.

By the end of this tutorial, you'll be able to:

• Evaluate multiple solutions using LLM-based scoring
• Define custom evaluation criteria for your specific needs
• Provide context to guide evaluations
• Integrate solution discrimination into your development workflow
• Make data-driven decisions when choosing between alternatives

Prerequisites

• PHP 8.1 or higher
• Composer
• Claude API key (Anthropic)
• Basic understanding of PHP and decision-making processes

Table of Contents

1. Getting Started
2. Your First Evaluation
3. Understanding Criteria
4. Working with Context
5. Comparing Algorithms
6. Evaluating Design Decisions
7. Code Review Use Cases
8. Integration Patterns
9. Best Practices

Getting Started

Installation

Ensure you have the claude-php-agent package installed:

composer require your-org/claude-php-agent

Basic Setup

Create a simple script to start evaluating solutions:

<?php
require_once 'vendor/autoload.php';

use ClaudeAgents\Agents\SolutionDiscriminatorAgent;
use ClaudePhp\ClaudePhp;

// Initialize the Claude client
$client = new ClaudePhp(apiKey: getenv('ANTHROPIC_API_KEY'));

// Create the discriminator agent
$discriminator = new SolutionDiscriminatorAgent($client, [
    'name' => 'tutorial_discriminator',
]);

echo "Solution Discriminator ready!\n";

Your First Evaluation

Let's start with a simple example: choosing between two approaches for caching.

Step 1: Define Your Solutions

$cachingSolutions = [
    [
        'id' => 'redis',
        'name' => 'Redis',
        'type' => 'In-memory data store',
        'description' => 'Fast, supports complex data structures',
        'pros' => 'Very fast, rich features, clustering support',
        'cons' => 'Requires separate server, memory-intensive',
    ],
    [
        'id' => 'memcached',
        'name' => 'Memcached',
        'type' => 'In-memory cache',
        'description' => 'Simple, fast key-value cache',
        'pros' => 'Simple, very fast, minimal overhead',
        'cons' => 'Limited data types, no persistence',
    ],
];

Step 2: Evaluate the Solutions

$evaluations = $discriminator->evaluateSolutions($cachingSolutions);

// Display results
foreach ($evaluations as $eval) {
    echo "\n{$eval['solution_id']}:\n";
    echo "  Total Score: " . number_format($eval['total_score'], 3) . "\n";
    echo "  Criteria:\n";
    foreach ($eval['scores'] as $criterion => $score) {
        echo "    - {$criterion}: " . number_format($score, 3) . "\n";
    }
}

Output:

redis:
  Total Score: 0.833
  Criteria:
    - correctness: 0.850
    - completeness: 0.900
    - quality: 0.750

memcached:
  Total Score: 0.767
  Criteria:
    - correctness: 0.800
    - completeness: 0.700
    - quality: 0.800

Step 3: Identify the Best Solution

// Sort by score
usort($evaluations, fn($a, $b) => $b['total_score'] <=> $a['total_score']);

$best = $evaluations[0];
echo "\nBest Solution: {$best['solution_id']}\n";
echo "Score: " . number_format($best['total_score'], 3) . "\n";

Understanding Criteria

Criteria are the dimensions along which solutions are evaluated. Choosing the right criteria is crucial for getting meaningful results.

Default Criteria

By default, the agent uses:

• correctness: How accurate and correct the solution is
• completeness: Whether it fully addresses the problem
• quality: Overall implementation quality

Defining Custom Criteria

Let's evaluate database query optimization strategies with domain-specific criteria:

$queryOptimizations = [
    [
        'id' => 'add_index',
        'strategy' => 'Add Database Index',
        'effort' => 'Low',
        'impact' => 'High',
        'risk' => 'Very Low',
    ],
    [
        'id' => 'denormalize',
        'strategy' => 'Denormalize Tables',
        'effort' => 'High',
        'impact' => 'Very High',
        'risk' => 'Medium',
    ],
    [
        'id' => 'query_rewrite',
        'strategy' => 'Rewrite Query',
        'effort' => 'Medium',
        'impact' => 'Medium',
        'risk' => 'Low',
    ],
];

// Custom criteria for optimization decisions
$optimizationEvaluator = new SolutionDiscriminatorAgent($client, [
    'criteria' => [
        'implementation_effort',
        'performance_impact',
        'risk_level',
        'maintainability',
    ],
]);

$evaluations = $optimizationEvaluator->evaluateSolutions($queryOptimizations);

Choosing Good Criteria

Good criteria are:

• Specific and measurable
• Relevant to your decision
• Independent of each other
• Clear in meaning

Examples:

// Good - specific, relevant
$criteria = ['time_complexity', 'space_complexity', 'code_readability'];

// Good - for architecture decisions
$criteria = ['scalability', 'deployment_complexity', 'team_familiarity'];

// Less good - too vague
$criteria = ['good', 'better', 'best'];

// Less good - overlapping
$criteria = ['speed', 'performance', 'fast'];  // Too similar

Working with Context

Context helps guide the evaluation by providing situational information.

Without Context

$solutions = [
    ['id' => 'microservices', 'description' => 'Independent services'],
    ['id' => 'monolith', 'description' => 'Single application'],
];

// Generic evaluation
$evaluations = $discriminator->evaluateSolutions($solutions);

With Context

// Startup context - different priorities
$startupContext = "Early-stage startup with 3 developers, " .
                  "need to move fast and iterate quickly, " .
                  "limited operations expertise";

$startupEval = $discriminator->evaluateSolutions($solutions, $startupContext);

// Enterprise context - different priorities
$enterpriseContext = "Large enterprise with 50+ developers, " .
                     "high traffic requiring horizontal scaling, " .
                     "dedicated DevOps team";

$enterpriseEval = $discriminator->evaluateSolutions($solutions, $enterpriseContext);

The same solutions will likely score differently with different contexts!

Crafting Effective Context

Include relevant details:

$context = implode(', ', [
    'PHP 8.2 application',
    'MySQL 8.0 database',
    'Team of 5 developers',
    'E-commerce platform',
    '100k monthly active users',
    'High read volume (90%)',
    'Peak traffic during sales events',
    'Budget constraints',
    '6-month timeline',
]);

$evaluations = $discriminator->evaluateSolutions($solutions, $context);

Comparing Algorithms

One of the most common uses is comparing algorithm implementations.

Example: Sorting Algorithms

$sortingAlgorithms = [
    [
        'id' => 'bubble_sort',
        'name' => 'Bubble Sort',
        'time_complexity' => 'O(n²)',
        'space_complexity' => 'O(1)',
        'stable' => true,
        'description' => 'Simple comparison-based sort',
        'code_lines' => 15,
        'best_case' => 'O(n)',
        'use_case' => 'Small datasets, educational purposes',
    ],
    [
        'id' => 'quick_sort',
        'name' => 'Quick Sort',
        'time_complexity' => 'O(n log n) average',
        'space_complexity' => 'O(log n)',
        'stable' => false,
        'description' => 'Efficient divide-and-conquer sort',
        'code_lines' => 20,
        'best_case' => 'O(n log n)',
        'use_case' => 'General purpose, large datasets',
    ],
    [
        'id' => 'merge_sort',
        'name' => 'Merge Sort',
        'time_complexity' => 'O(n log n)',
        'space_complexity' => 'O(n)',
        'stable' => true,
        'description' => 'Predictable divide-and-conquer sort',
        'code_lines' => 25,
        'best_case' => 'O(n log n)',
        'use_case' => 'When stability is required',
    ],
];

$algorithmEvaluator = new SolutionDiscriminatorAgent($client, [
    'criteria' => [
        'time_efficiency',
        'space_efficiency',
        'implementation_simplicity',
        'practical_utility',
    ],
]);

$context = "Need to sort user-generated data (1000-10000 items), " .
           "stability is important, memory is limited";

$evaluations = $algorithmEvaluator->evaluateSolutions($sortingAlgorithms, $context);

// Rank them
usort($evaluations, fn($a, $b) => $b['total_score'] <=> $a['total_score']);

echo "Rankings for your use case:\n";
foreach ($evaluations as $i => $eval) {
    $rank = $i + 1;
    echo "{$rank}. {$eval['solution_id']}: " . 
         number_format($eval['total_score'], 3) . "\n";
}

Example: Search Algorithms

$searchAlgorithms = [
    [
        'id' => 'linear_search',
        'name' => 'Linear Search',
        'complexity' => 'O(n)',
        'requirements' => 'None',
        'description' => 'Check each element sequentially',
    ],
    [
        'id' => 'binary_search',
        'name' => 'Binary Search',
        'complexity' => 'O(log n)',
        'requirements' => 'Sorted array',
        'description' => 'Divide and conquer on sorted data',
    ],
    [
        'id' => 'hash_lookup',
        'name' => 'Hash Table Lookup',
        'complexity' => 'O(1) average',
        'requirements' => 'Extra memory for hash table',
        'description' => 'Direct access via hash function',
    ],
];

$context = "Frequent searches in dataset of 10,000 items, " .
           "data rarely changes, memory available";

$evaluations = $discriminator->evaluateSolutions($searchAlgorithms, $context);

Evaluating Design Decisions

Design decisions are perfect candidates for objective evaluation.

Example: API Design

$apiDesigns = [
    [
        'id' => 'rest',
        'name' => 'REST API',
        'approach' => 'Resource-oriented with HTTP methods',
        'pros' => [
            'Widely understood',
            'Great tooling',
            'HTTP caching',
            'Stateless',
        ],
        'cons' => [
            'Over-fetching/under-fetching',
            'Multiple round trips',
            'Versioning challenges',
        ],
        'learning_curve' => 'Low',
        'community' => 'Very large',
    ],
    [
        'id' => 'graphql',
        'name' => 'GraphQL',
        'approach' => 'Query language for APIs',
        'pros' => [
            'Flexible queries',
            'Single endpoint',
            'Strong typing',
            'No over-fetching',
        ],
        'cons' => [
            'Complexity',
            'Caching challenges',
            'File uploads',
        ],
        'learning_curve' => 'Medium',
        'community' => 'Large',
    ],
    [
        'id' => 'grpc',
        'name' => 'gRPC',
        'approach' => 'Binary protocol with Protocol Buffers',
        'pros' => [
            'Very fast',
            'Streaming support',
            'Strong contracts',
            'Cross-language',
        ],
        'cons' => [
            'Not browser-friendly',
            'Steeper learning curve',
            'Debugging harder',
        ],
        'learning_curve' => 'High',
        'community' => 'Growing',
    ],
];

$apiEvaluator = new SolutionDiscriminatorAgent($client, [
    'criteria' => [
        'developer_experience',
        'performance',
        'client_compatibility',
        'ecosystem_maturity',
    ],
]);

$context = "Mobile app backend, need real-time updates, " .
           "team familiar with REST, supporting iOS and Android";

$evaluations = $apiEvaluator->evaluateSolutions($apiDesigns, $context);

// Display with visual bars
foreach ($evaluations as $eval) {
    echo "\n{$eval['solution_id']}:\n";
    foreach ($eval['scores'] as $criterion => $score) {
        $bar = str_repeat('█', (int)($score * 20));
        $percentage = number_format($score * 100, 1);
        echo "  {$criterion}: {$bar} {$percentage}%\n";
    }
    echo "  TOTAL: " . number_format($eval['total_score'], 3) . "\n";
}

Example: Database Schema Design

$schemaDesigns = [
    [
        'id' => 'normalized_3nf',
        'name' => '3rd Normal Form',
        'structure' => 'Fully normalized with separate tables',
        'tables' => 8,
        'joins_typical_query' => 4,
        'data_redundancy' => 'Minimal',
        'write_performance' => 'Excellent',
        'read_performance' => 'Moderate',
        'maintenance' => 'Easy',
    ],
    [
        'id' => 'denormalized',
        'name' => 'Denormalized',
        'structure' => 'Combined tables with redundancy',
        'tables' => 3,
        'joins_typical_query' => 1,
        'data_redundancy' => 'High',
        'write_performance' => 'Poor',
        'read_performance' => 'Excellent',
        'maintenance' => 'Difficult',
    ],
    [
        'id' => 'hybrid',
        'name' => 'Hybrid with Views',
        'structure' => 'Normalized base + materialized views',
        'tables' => 8,
        'joins_typical_query' => 0,
        'data_redundancy' => 'Controlled',
        'write_performance' => 'Good',
        'read_performance' => 'Excellent',
        'maintenance' => 'Moderate',
    ],
];

$schemaEvaluator = new SolutionDiscriminatorAgent($client, [
    'criteria' => [
        'read_performance',
        'write_performance',
        'maintainability',
        'data_integrity',
    ],
]);

$context = "E-commerce platform, 90% reads, 10% writes, " .
           "complex product catalog, frequent reporting queries";

$evaluations = $schemaEvaluator->evaluateSolutions($schemaDesigns, $context);

Code Review Use Cases

Use the discriminator to make code review decisions.

Example: Refactoring Strategies

class RefactoringDecision
{
    private SolutionDiscriminatorAgent $discriminator;
    
    public function __construct(SolutionDiscriminatorAgent $discriminator)
    {
        $this->discriminator = $discriminator;
    }
    
    public function evaluateRefactoringOptions(
        string $currentCode,
        array $options,
        array $constraints
    ): array {
        $context = $this->buildContext($currentCode, $constraints);
        
        $evaluations = $this->discriminator->evaluateSolutions($options, $context);
        
        // Sort by score
        usort($evaluations, fn($a, $b) => $b['total_score'] <=> $a['total_score']);
        
        return $evaluations;
    }
    
    private function buildContext(string $currentCode, array $constraints): string
    {
        return implode(', ', [
            "Current code: {$currentCode} lines",
            "Test coverage: {$constraints['test_coverage']}%",
            "Team size: {$constraints['team_size']}",
            "Timeline: {$constraints['timeline']}",
            "Risk tolerance: {$constraints['risk_tolerance']}",
        ]);
    }
}

// Usage
$refactoringOptions = [
    [
        'id' => 'extract_class',
        'technique' => 'Extract Class',
        'description' => 'Split large class into focused classes',
        'estimated_effort' => '3 days',
        'risk' => 'Medium',
        'benefit' => 'High maintainability',
        'breaking_changes' => false,
    ],
    [
        'id' => 'introduce_interface',
        'technique' => 'Extract Interface',
        'description' => 'Create interface for dependency injection',
        'estimated_effort' => '1 day',
        'risk' => 'Low',
        'benefit' => 'Better testability',
        'breaking_changes' => false,
    ],
    [
        'id' => 'complete_rewrite',
        'technique' => 'Complete Rewrite',
        'description' => 'Rewrite from scratch with modern patterns',
        'estimated_effort' => '2 weeks',
        'risk' => 'High',
        'benefit' => 'Clean architecture',
        'breaking_changes' => true,
    ],
];

$constraints = [
    'test_coverage' => 45,
    'team_size' => 3,
    'timeline' => '1 week',
    'risk_tolerance' => 'low',
];

$evaluator = new RefactoringDecision($discriminator);
$evaluations = $evaluator->evaluateRefactoringOptions(
    '500',
    $refactoringOptions,
    $constraints
);

echo "Recommended refactoring: {$evaluations[0]['solution_id']}\n";

Example: Library Selection

$loggingLibraries = [
    [
        'id' => 'monolog',
        'name' => 'Monolog',
        'stars' => 20500,
        'downloads' => '500M+',
        'last_update' => '2 weeks ago',
        'php_version' => '>=7.2',
        'features' => ['handlers', 'formatters', 'processors'],
        'license' => 'MIT',
    ],
    [
        'id' => 'log4php',
        'name' => 'Apache Log4php',
        'stars' => 450,
        'downloads' => '5M+',
        'last_update' => '3 years ago',
        'php_version' => '>=5.2.7',
        'features' => ['appenders', 'layouts', 'filters'],
        'license' => 'Apache 2.0',
    ],
    [
        'id' => 'klogger',
        'name' => 'KLogger',
        'stars' => 1000,
        'downloads' => '10M+',
        'last_update' => '1 year ago',
        'php_version' => '>=5.3',
        'features' => ['simple', 'PSR-3'],
        'license' => 'MIT',
    ],
];

$libraryEvaluator = new SolutionDiscriminatorAgent($client, [
    'criteria' => [
        'community_support',
        'maintenance_activity',
        'feature_richness',
        'ease_of_use',
    ],
]);

$context = "Modern PHP 8.2 application, need structured logging, " .
           "planning to use for years, team prefers PSR standards";

$evaluations = $libraryEvaluator->evaluateSolutions($loggingLibraries, $context);

Integration Patterns

Pattern 1: Decision Pipeline

Chain multiple evaluations together:

class DesignDecisionPipeline
{
    private array $evaluators = [];
    
    public function addStage(string $name, SolutionDiscriminatorAgent $evaluator): self
    {
        $this->evaluators[$name] = $evaluator;
        return $this;
    }
    
    public function evaluate(array $solutions): array
    {
        $results = [];
        
        foreach ($this->evaluators as $stage => $evaluator) {
            $evaluations = $evaluator->evaluateSolutions($solutions);
            $results[$stage] = $evaluations;
            
            // Filter to top 3 for next stage
            usort($evaluations, fn($a, $b) => $b['total_score'] <=> $a['total_score']);
            $solutions = array_slice($evaluations, 0, 3);
        }
        
        return $results;
    }
}

// Usage
$pipeline = new DesignDecisionPipeline();

$pipeline
    ->addStage('technical', new SolutionDiscriminatorAgent($client, [
        'criteria' => ['performance', 'scalability', 'security'],
    ]))
    ->addStage('business', new SolutionDiscriminatorAgent($client, [
        'criteria' => ['cost', 'time_to_market', 'team_expertise'],
    ]))
    ->addStage('risk', new SolutionDiscriminatorAgent($client, [
        'criteria' => ['implementation_risk', 'maintenance_risk', 'vendor_lock_in'],
    ]));

$results = $pipeline->evaluate($solutions);

Pattern 2: Weighted Criteria

Implement custom weighting:

class WeightedEvaluator
{
    private SolutionDiscriminatorAgent $discriminator;
    private array $weights;
    
    public function __construct(
        SolutionDiscriminatorAgent $discriminator,
        array $weights
    ) {
        $this->discriminator = $discriminator;
        $this->weights = $weights;
    }
    
    public function evaluate(array $solutions): array
    {
        $evaluations = $this->discriminator->evaluateSolutions($solutions);
        
        // Apply weights
        foreach ($evaluations as &$eval) {
            $weightedScore = 0;
            $totalWeight = array_sum($this->weights);
            
            foreach ($eval['scores'] as $criterion => $score) {
                $weight = $this->weights[$criterion] ?? 1;
                $weightedScore += $score * $weight;
            }
            
            $eval['weighted_score'] = $weightedScore / $totalWeight;
        }
        
        return $evaluations;
    }
}

// Usage
$weights = [
    'performance' => 3,      // 3x importance
    'maintainability' => 2,  // 2x importance
    'simplicity' => 1,       // 1x importance (baseline)
];

$weightedEvaluator = new WeightedEvaluator($discriminator, $weights);
$evaluations = $weightedEvaluator->evaluate($solutions);

Pattern 3: Evaluation Cache

Cache evaluations to avoid redundant API calls:

class CachedEvaluator
{
    private SolutionDiscriminatorAgent $discriminator;
    private array $cache = [];
    
    public function evaluateSolutions(array $solutions, ?string $context = null): array
    {
        $cacheKey = $this->generateCacheKey($solutions, $context);
        
        if (isset($this->cache[$cacheKey])) {
            echo "Using cached evaluation\n";
            return $this->cache[$cacheKey];
        }
        
        $evaluations = $this->discriminator->evaluateSolutions($solutions, $context);
        $this->cache[$cacheKey] = $evaluations;
        
        return $evaluations;
    }
    
    private function generateCacheKey(array $solutions, ?string $context): string
    {
        return md5(json_encode($solutions) . $context);
    }
}

Best Practices

1. Start Broad, Then Narrow

// Phase 1: Broad evaluation
$broadEvaluator = new SolutionDiscriminatorAgent($client, [
    'criteria' => ['overall_fit', 'feasibility'],
]);

$broadEval = $broadEvaluator->evaluateSolutions($allSolutions);

// Take top 3
usort($broadEval, fn($a, $b) => $b['total_score'] <=> $a['total_score']);
$topSolutions = array_slice($broadEval, 0, 3);

// Phase 2: Detailed evaluation
$detailedEvaluator = new SolutionDiscriminatorAgent($client, [
    'criteria' => ['performance', 'maintainability', 'security', 'scalability'],
]);

$detailedEval = $detailedEvaluator->evaluateSolutions($topSolutions);

2. Document Your Decisions

class DocumentedEvaluation
{
    public function evaluate(
        array $solutions,
        string $context,
        SolutionDiscriminatorAgent $discriminator
    ): array {
        $evaluations = $discriminator->evaluateSolutions($solutions, $context);
        
        // Create decision log
        $log = [
            'timestamp' => date('c'),
            'context' => $context,
            'criteria' => $discriminator->getCriteria(),
            'solutions_evaluated' => count($solutions),
            'evaluations' => $evaluations,
            'decision' => $this->selectBest($evaluations),
        ];
        
        file_put_contents(
            'decisions.jsonl',
            json_encode($log) . "\n",
            FILE_APPEND
        );
        
        return $evaluations;
    }
    
    private function selectBest(array $evaluations): array
    {
        usort($evaluations, fn($a, $b) => $b['total_score'] <=> $a['total_score']);
        return $evaluations[0];
    }
}

3. Validate Results

function validateEvaluation(array $evaluations): bool
{
    // Check for suspicious patterns
    
    // 1. All scores too similar
    $scores = array_column($evaluations, 'total_score');
    $variance = variance($scores);
    
    if ($variance < 0.01) {
        echo "Warning: Very low variance in scores\n";
        echo "Consider: More specific criteria or richer solution details\n";
        return false;
    }
    
    // 2. Extreme scores
    foreach ($evaluations as $eval) {
        if ($eval['total_score'] < 0.2 || $eval['total_score'] > 0.95) {
            echo "Warning: Extreme score detected\n";
            return false;
        }
    }
    
    return true;
}

function variance(array $values): float
{
    $mean = array_sum($values) / count($values);
    $squaredDiffs = array_map(fn($v) => ($v - $mean) ** 2, $values);
    return array_sum($squaredDiffs) / count($values);
}

4. Combine with Human Review

function hybridEvaluation(
    array $solutions,
    SolutionDiscriminatorAgent $discriminator
): array {
    // AI evaluation
    $aiEvaluations = $discriminator->evaluateSolutions($solutions);
    
    // Get top 3
    usort($aiEvaluations, fn($a, $b) => $b['total_score'] <=> $a['total_score']);
    $finalists = array_slice($aiEvaluations, 0, 3);
    
    // Present to humans for final decision
    echo "AI has narrowed down to 3 finalists:\n\n";
    foreach ($finalists as $i => $eval) {
        echo ($i + 1) . ". {$eval['solution_id']} - ";
        echo "Score: " . number_format($eval['total_score'], 3) . "\n";
        
        echo "   Top strength: " . array_key_first($eval['scores']) . "\n";
    }
    
    echo "\nPlease select final winner (1-3): ";
    $choice = (int)trim(fgets(STDIN));
    
    return $finalists[$choice - 1];
}

5. Monitor and Improve

class EvaluationMetrics
{
    private array $history = [];
    
    public function recordEvaluation(array $evaluations, ?string $selectedId = null): void
    {
        $this->history[] = [
            'timestamp' => time(),
            'evaluations' => $evaluations,
            'selected' => $selectedId,
        ];
    }
    
    public function getInsights(): array
    {
        $totalEvaluations = count($this->history);
        $avgScores = [];
        
        foreach ($this->history as $record) {
            foreach ($record['evaluations'] as $eval) {
                $id = $eval['solution_id'];
                if (!isset($avgScores[$id])) {
                    $avgScores[$id] = ['sum' => 0, 'count' => 0];
                }
                $avgScores[$id]['sum'] += $eval['total_score'];
                $avgScores[$id]['count']++;
            }
        }
        
        $insights = [];
        foreach ($avgScores as $id => $data) {
            $insights[$id] = $data['sum'] / $data['count'];
        }
        
        arsort($insights);
        
        return [
            'total_evaluations' => $totalEvaluations,
            'average_scores' => $insights,
        ];
    }
}

Conclusion

You now have a comprehensive understanding of the SolutionDiscriminatorAgent! You've learned:

✅ How to evaluate multiple solutions objectively
✅ Defining and using custom evaluation criteria
✅ Providing context to guide evaluations
✅ Comparing algorithms and design decisions
✅ Integrating evaluation into code review processes
✅ Advanced patterns and best practices

Next Steps

• Review the SolutionDiscriminatorAgent API Documentation
• Check out the working example
• Try evaluating solutions in your own projects
• Experiment with different criteria for your specific use cases
• Integrate with your existing decision-making workflows

Additional Resources

• SolutionDiscriminatorAgent.md - Complete API reference
• solution_discriminator_example.php - Working examples
• MakerAgent.md - Integration with MAKER framework
• Claude API Documentation

Happy evaluating! 🎯