Strategy Pattern

The Strategy Pattern enables AI agents to switch between different algorithms or approaches dynamically based on context or performance requirements.

Pattern Overview

This pattern defines a family of algorithms, encapsulates each one, and makes them interchangeable, allowing agents to adapt their behavior at runtime.

Structure

// Strategy trait for different optimization approaches
pub trait OptimizationStrategy {
    fn optimize(&self, data: &[f64]) -> Vec<f64>;
    fn get_name(&self) -> &str;
}
 
// Concrete strategies
pub struct GradientDescentStrategy;
 
impl OptimizationStrategy for GradientDescentStrategy {
    fn optimize(&self, data: &[f64]) -> Vec<f64> {
        // Simplified gradient descent
        data.iter().map(|x| x * 0.9).collect()
    }
 
    fn get_name(&self) -> &str {
        "gradient_descent"
    }
}
 
pub struct GeneticAlgorithmStrategy;
 
impl OptimizationStrategy for GeneticAlgorithmStrategy {
    fn optimize(&self, data: &[f64]) -> Vec<f64> {
        // Simplified genetic algorithm
        data.iter().map(|x| x * 1.1).collect()
    }
 
    fn get_name(&self) -> &str {
        "genetic_algorithm"
    }
}
 
// Context that uses strategies
pub struct OptimizerAgent {
    strategy: Box<dyn OptimizationStrategy>,
}
 
impl OptimizerAgent {
    pub fn new(strategy: Box<dyn OptimizationStrategy>) -> Self {
        Self { strategy }
    }
 
    pub fn set_strategy(&mut self, strategy: Box<dyn OptimizationStrategy>) {
        self.strategy = strategy;
    }
 
    pub fn execute_optimization(&self, data: &[f64]) -> Vec<f64> {
        self.strategy.optimize(data)
    }
}

Usage Example

let mut agent = OptimizerAgent::new(Box::new(GradientDescentStrategy));
let data = vec![1.0, 2.0, 3.0, 4.0];
 
// Use gradient descent
let result1 = agent.execute_optimization(&data);
 
// Switch to genetic algorithm
agent.set_strategy(Box::new(GeneticAlgorithmStrategy));
let result2 = agent.execute_optimization(&data);

Benefits

Flexibility: Switch algorithms at runtime
Testability: Easy to test different strategies
Maintainability: Isolate algorithm-specific code
Extensibility: Add new strategies without modifying existing code

Use Cases

Adaptive learning algorithms in AI agents
Dynamic routing strategies for agent communication
Context-aware decision making
A/B testing different AI approaches

Factory Pattern Observer Pattern