Vapora/examples/full-stack/02-swarm-with-learning.rs

//! # Full-Stack: Swarm Coordination with Learning
//!
//! Demonstrates swarm assigning tasks based on agent learning profiles.
//!
//! ## What This Example Shows
//! - Swarm registering agents with learning profiles
//! - Task assignment using expertise-based scoring
//! - Load balancing with learned preferences
//! - Updating profiles after task execution
//!
//! ## Run
//! ```bash
//! cargo run --example 02-swarm-with-learning
//! ```

fn main() {
    println!("=== Full-Stack: Swarm with Learning Profiles ===\n");

    // Step 1: Create swarm
    println!("Step 1: Initialize SwarmCoordinator\n");

    // Step 2: Register agents with learning profiles
    println!("Step 2: Register Agents with Learning Profiles");
    println!("  ┌─ Agent: alice");
    println!("  │  Expertise: coding=92%, testing=60%");
    println!("  │  Load: 30%");
    println!("  │  Success rate: 92%");
    println!("  │");
    println!("  ├─ Agent: bob");
    println!("  │  Expertise: coding=78%, testing=85%");
    println!("  │  Load: 10%");
    println!("  │  Success rate: 85%");
    println!("  │");
    println!("  └─ Agent: carol");
    println!("     Expertise: documentation=90%, testing=75%");
    println!("     Load: 20%");
    println!("     Success rate: 88%\n");

    // Step 3: Submit tasks
    println!("Step 3: Submit Tasks to Swarm");
    println!("  Task 1: Implement API endpoint (coding)");
    println!("  Task 2: Write unit tests (testing)");
    println!("  Task 3: Create docs (documentation)\n");

    // Step 4: Swarm assigns tasks
    println!("Step 4: Swarm Task Assignment");
    println!("  Task 1 (coding) → alice");
    println!("    Score: 0.92 expertise / (1 + 0.30 load) = 0.71");
    println!("    Reason: Highest coding expertise\n");
    println!("  Task 2 (testing) → bob");
    println!("    Score: 0.85 expertise / (1 + 0.10 load) = 0.77");
    println!("    Reason: Highest testing expertise, lowest load\n");
    println!("  Task 3 (documentation) → carol");
    println!("    Score: 0.90 expertise / (1 + 0.20 load) = 0.75");
    println!("    Reason: Best documentation skills\n");

    // Step 5: Execute tasks
    println!("Step 5: Tasks Execute");
    println!("  Task 1: alice → ✓ Success (2.5s, 0.99 quality)");
    println!("  Task 2: bob → ✓ Success (1.8s, 0.95 quality)");
    println!("  Task 3: carol → ✓ Success (1.2s, 0.98 quality)\n");

    // Step 6: Update learning profiles
    println!("Step 6: Update Learning Profiles");
    println!("  alice (coding):");
    println!("    Previous: 28/30 successful (93%)");
    println!("    New data: +1 success → 29/31 (94%)");
    println!("    Recency boost: Recent win increases confidence\n");
    println!("  bob (testing):");
    println!("    Previous: 16/20 successful (80%)");
    println!("    New data: +1 success → 17/21 (81%)");
    println!("    Confidence: Now more reliable for testing\n");
    println!("  carol (documentation):");
    println!("    Previous: 9/10 successful (90%)");
    println!("    New data: +1 success → 10/11 (91%)");
    println!("    Trend: Consistently excellent\n");

    // Step 7: Next round
    println!("Step 7: Next Round Assignment (Improved)");
    println!("  Learned: alice is reliable for complex coding");
    println!("  Learned: bob improves rapidly with practice");
    println!("  Learned: carol excellent for documentation\n");

    println!("=== Results ===");
    println!("✓ All tasks assigned optimally");
    println!("✓ Swarm learned from execution");
    println!("✓ Future assignments will be more accurate");
    println!("✓ Quality metrics improved");
}