Builder Pattern
The Builder Pattern constructs complex AI agent configurations step by step, allowing for flexible and readable agent initialization.
Pattern Overview
This pattern separates the construction of complex AI agents from their representation, enabling the same construction process to create different types of agents.
Structure
// Agent configuration structure
#[derive(Debug, Clone)]
pub struct AgentConfig {
pub name: String,
pub capabilities: Vec<String>,
pub memory_size: usize,
pub learning_rate: f64,
pub max_retries: u32,
}
// Builder implementation
pub struct AgentBuilder {
config: AgentConfig,
}
impl AgentBuilder {
pub fn new(name: &str) -> Self {
Self {
config: AgentConfig {
name: name.to_string(),
capabilities: Vec::new(),
memory_size: 1024,
learning_rate: 0.01,
max_retries: 3,
},
}
}
pub fn with_capability(mut self, capability: &str) -> Self {
self.config.capabilities.push(capability.to_string());
self
}
pub fn with_memory_size(mut self, size: usize) -> Self {
self.config.memory_size = size;
self
}
pub fn with_learning_rate(mut self, rate: f64) -> Self {
self.config.learning_rate = rate;
self
}
pub fn build(self) -> AgentConfig {
self.config
}
}Usage Example
let agent_config = AgentBuilder::new("ProcessMiningAgent")
.with_capability("data_analysis")
.with_capability("pattern_recognition")
.with_memory_size(2048)
.with_learning_rate(0.05)
.build();Benefits
- Flexibility: Configure agents with different combinations of features
- Readability: Clear, fluent API for agent construction
- Validation: Ensure agent configurations are valid before creation
- Extensibility: Easy to add new configuration options
Use Cases
- Creating specialized AI agents with different capabilities
- Building agent templates for different business domains
- Configuring agent behavior for specific environments
- Setting up agent hierarchies with varying parameters