<!-- START doctoc generated TOC please keep comment here to allow auto update -->
<!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE -->
**Table of Contents**

- [Ultrathink: Autonomous Intelligence System](#ultrathink-autonomous-intelligence-system)
  - [What is Ultrathink?](#what-is-ultrathink)
    - [Why Autonomous AI?](#why-autonomous-ai)
  - [Core Architecture](#core-architecture)
    - [The Autonomy Loop](#the-autonomy-loop)
    - [Agent Specialization](#agent-specialization)
      - [1. Analysis Agent](#1-analysis-agent)
      - [2. Planning Agent](#2-planning-agent)
      - [3. Generation Agent](#3-generation-agent)
      - [4. Validation Agent](#4-validation-agent)
      - [5. Optimization Agent](#5-optimization-agent)
  - [WIP (Work In Progress) Synchronization](#wip-work-in-progress-synchronization)
    - [WIP State Machine](#wip-state-machine)
    - [WIP Entry Structure](#wip-entry-structure)
    - [WIP Synchronization Protocol](#wip-synchronization-protocol)
    - [Monitoring WIP](#monitoring-wip)
  - [Adaptive Decision Making](#adaptive-decision-making)
    - [Decision Framework](#decision-framework)
    - [Examples of Adaptive Decisions](#examples-of-adaptive-decisions)
      - [1. Template Selection](#1-template-selection)
      - [2. Conflict Resolution](#2-conflict-resolution)
      - [3. Performance Optimization](#3-performance-optimization)
  - [Self-Healing and Error Recovery](#self-healing-and-error-recovery)
    - [Failure Detection](#failure-detection)
    - [Automatic Recovery](#automatic-recovery)
  - [Learning and Improvement](#learning-and-improvement)
    - [Learning Mechanism](#learning-mechanism)
    - [Feedback Loop](#feedback-loop)
  - [Monitoring and Observability](#monitoring-and-observability)
    - [Core Metrics](#core-metrics)
    - [Dashboard Access](#dashboard-access)
    - [Example Dashboard Output](#example-dashboard-output)
  - [Use Cases](#use-cases)
    - [1. Complex Ontology Generation](#1-complex-ontology-generation)
    - [2. Multi-Language Generation](#2-multi-language-generation)
    - [3. Incremental Generation](#3-incremental-generation)
  - [Best Practices](#best-practices)
  - [Troubleshooting](#troubleshooting)
    - [Ultrathink Takes Too Long](#ultrathink-takes-too-long)
    - [Memory Usage Growing](#memory-usage-growing)
    - [WIP Synchronization Failures](#wip-synchronization-failures)
  - [Next Steps](#next-steps)
  - [Resources](#resources)

<!-- END doctoc generated TOC please keep comment here to allow auto update -->

# Ultrathink: Autonomous Intelligence System

**Goal:** Understand how ggen's autonomous AI system processes complex code generation tasks independently with intelligent decision-making and adaptive behavior.

**What you'll learn:** Autonomous task processing, WIP synchronization, intelligent resource allocation, and monitoring autonomous agents.

---

## What is Ultrathink?

Ultrathink is ggen's autonomous intelligence system that coordinates multiple AI agents to handle complex code generation tasks without human intervention. It goes beyond template rendering by providing:

1. **Autonomous Task Processing** - Break complex goals into subtasks and execute them intelligently
2. **Adaptive Decision Making** - Choose optimal strategies based on current context
3. **Multi-Agent Coordination** - Orchestrate multiple specialized AI agents
4. **Self-Healing** - Detect and recover from failures automatically
5. **Learning & Improvement** - Improve generation quality over time

### Why Autonomous AI?

**Traditional Code Generation:**
```
User defines → Template executes → Output generated
(Static)       (Fixed logic)       (One path)
```

**Autonomous Generation (Ultrathink):**
```
User defines → AI analyzes → Agents discuss → Optimal solution
              → Adapts       → Self-corrects   → Executes
```

**Real-World Examples:**

- **Complex Ontology Analysis** - Automatically discover patterns and optimal code structure
- **Multi-Language Generation** - Intelligently target multiple platforms with adapted code
- **Conflict Resolution** - When templates conflict, AI negotiates optimal solution
- **Performance Optimization** - Autonomously optimize generated code for target environment
- **Quality Assurance** - Self-verify output meets quality standards

---

## Core Architecture

### The Autonomy Loop

```
┌─────────────────────────────────────────────┐
│  1. Goal Reception                          │
│     User request: "Generate blog API"       │
└────────────────┬────────────────────────────┘
                 │
┌────────────────▼────────────────────────────┐
│  2. Understanding                           │
│     Parse request → Identify requirements   │
│     Extract constraints → Analyze context   │
└────────────────┬────────────────────────────┘
                 │
┌────────────────▼────────────────────────────┐
│  3. Planning                                │
│     Break into subtasks → Order tasks       │
│     Estimate resources → Allocate agents    │
└────────────────┬────────────────────────────┘
                 │
┌────────────────▼────────────────────────────┐
│  4. Agent Dispatch                          │
│     Assign tasks to agents → Monitor        │
│     Coordinate results → Handle conflicts   │
└────────────────┬────────────────────────────┘
                 │
┌────────────────▼────────────────────────────┐
│  5. Verification                           │
│     Check output correctness → Validate     │
│     Test generated code → Quality check     │
└────────────────┬────────────────────────────┘
                 │
┌────────────────▼────────────────────────────┐
│  6. Optimization                            │
│     Analyze performance → Improve           │
│     Learn patterns → Update strategies      │
└─────────────────────────────────────────────┘
```

### Agent Specialization

Ultrathink coordinates five specialized agent types:

#### 1. Analysis Agent

**Responsible for:** Understanding requirements and constraints

```rust
pub struct AnalysisAgent {
    pub role: "Analyzer",
    pub capabilities: vec![
        "Parse RDF ontologies",
        "Identify patterns in schemas",
        "Extract constraints",
        "Detect relationships",
        "Recommend optimizations"
    ],
}

// Example output
pub struct Analysis {
    pub entity_count: 42,
    pub relationship_types: 8,
    pub has_cycles: false,
    pub patterns_detected: vec![
        Pattern::HierarchyPattern,
        Pattern::AggregationPattern,
        Pattern::PolymorphismPattern,
    ],
    pub recommended_templates: vec![
        "rust-models",
        "graphql-schema",
        "validation-rules"
    ],
}
```

#### 2. Planning Agent

**Responsible for:** Breaking work into optimal subtasks

```rust
pub struct PlanningAgent {
    pub role: "Planner",
    pub capabilities: vec![
        "Decompose tasks",
        "Estimate complexity",
        "Optimize order",
        "Allocate resources",
        "Manage dependencies"
    ],
}

// Example output
pub struct ExecutionPlan {
    pub tasks: vec![
        Task {
            id: "extract-classes",
            priority: 1,
            depends_on: vec![],
            estimated_duration: Duration::from_millis(100),
            agent_type: "analysis",
        },
        Task {
            id: "validate-ontology",
            priority: 2,
            depends_on: vec!["extract-classes"],
            estimated_duration: Duration::from_millis(50),
            agent_type: "validation",
        },
        Task {
            id: "generate-rust",
            priority: 3,
            depends_on: vec!["validate-ontology"],
            estimated_duration: Duration::from_millis(200),
            agent_type: "generation",
        },
    ],
    pub total_estimated: Duration::from_millis(350),
    pub parallelizable: vec!["extract-classes", "analysis"],
}
```

#### 3. Generation Agent

**Responsible for:** Creating code using templates and AI

```rust
pub struct GenerationAgent {
    pub role: "Generator",
    pub capabilities: vec![
        "Render templates",
        "Apply transformations",
        "Optimize code",
        "Handle multiple languages",
        "Inject custom logic"
    ],
}

// Example output
pub struct GenerationResult {
    pub files_generated: 12,
    pub lines_of_code: 3_847,
    pub quality_score: 0.94,
    pub optimizations_applied: vec![
        "Removed unused imports",
        "Simplified type definitions",
        "Added documentation",
    ],
}
```

#### 4. Validation Agent

**Responsible for:** Ensuring output correctness and quality

```rust
pub struct ValidationAgent {
    pub role: "Validator",
    pub capabilities: vec![
        "Type checking",
        "Syntax validation",
        "Semantic checking",
        "Performance analysis",
        "Security scanning"
    ],
}

// Example output
pub struct ValidationResult {
    pub syntax_valid: true,
    pub type_correct: true,
    pub semantic_correct: true,
    pub issues: vec![],
    pub warnings: vec![
        "Missing documentation on 5 public APIs",
    ],
    pub security_score: 98,
    pub performance_score: 92,
}
```

#### 5. Optimization Agent

**Responsible for:** Improving output and learning patterns

```rust
pub struct OptimizationAgent {
    pub role: "Optimizer",
    pub capabilities: vec![
        "Analyze performance",
        "Identify bottlenecks",
        "Suggest improvements",
        "Learn from results",
        "Update strategies"
    ],
}

// Example output
pub struct OptimizationSuggestions {
    pub performance_improvements: vec![
        Suggestion {
            area: "Type System",
            current: "12 runtime type checks",
            suggested: "Use type-level proof",
            potential_gain: "30% faster",
        },
    ],
    pub quality_improvements: vec![
        Suggestion {
            area: "Error Handling",
            current: "String errors",
            suggested: "Structured error types",
            potential_gain: "Better debugging",
        },
    ],
}
```

---

## WIP (Work In Progress) Synchronization

Ultrathink uses a sophisticated WIP system to track and coordinate ongoing work:

### WIP State Machine

```
         ┌─────────────┐
         │   Created   │
         └──────┬──────┘
                │
        ┌───────▼────────┐
        │  Acknowledged  │
        └────────┬───────┘
                 │
        ┌────────▼──────────┐
        │  In Progress      │
        │  ├─ Subtask 1 ✓   │
        │  ├─ Subtask 2 ⟳  │
        │  └─ Subtask 3 ⏳   │
        └────────┬──────────┘
                 │
        ┌────────▼──────────┐
        │  Verification     │
        │  (Quality Checks) │
        └────────┬──────────┘
                 │
        ┌────────▼──────────┐
        │  Optimization     │
        │  (Performance)    │
        └────────┬──────────┘
                 │
        ┌────────▼──────────┐
        │  Ready            │
        │  (Can Finalize)   │
        └─────────────────────┘
```

### WIP Entry Structure

```rust
pub struct WorkInProgress {
    pub id: UUID,
    pub goal: String,
    pub state: WipState,
    pub created_at: SystemTime,
    pub updated_at: SystemTime,

    pub subtasks: Vec<Subtask>,
    pub current_agent: Option<AgentId>,
    pub next_agent: Option<AgentId>,

    pub context: Context,
    pub metadata: Metadata,
    pub progress: f64, // 0.0-1.0
}

pub struct Subtask {
    pub id: UUID,
    pub name: String,
    pub state: SubtaskState,
    pub assigned_to: Option<AgentId>,
    pub progress: f64,
    pub result: Option<SubtaskResult>,
    pub error: Option<String>,
}
```

### WIP Synchronization Protocol

```
Agent A              WIP Tracker         Agent B
   │                    │                   │
   ├──Report Progress──>│                   │
   │                    ├──Notify Status──>│
   │                    │                   │
   │                    │<──Request Data──<│
   │                    ├──Send Data──────>│
   │                    │                   │
   ├──Complete Task──>│                   │
   │                    ├──Mark Done──────>│
   │                    │                   │
   │                    ├──Dispatch Next──>│
   │                    │                   │
```

### Monitoring WIP

```rust
use ggen_ultrathink::wip::WipTracker;

// Track a generation task
let wip = WipTracker::create()
    .goal("Generate Rust API from blog ontology")
    .subtask("Parse ontology")
    .subtask("Analyze schema")
    .subtask("Generate code")
    .subtask("Validate output");

// Monitor progress
let progress = wip.progress(); // 0.0-1.0

// Get current state
let state = wip.current_state();
println!("Current agent: {:?}", state.current_agent);
println!("Next agent: {:?}", state.next_agent);

// Access subtask results
for subtask in wip.subtasks() {
    match subtask.state {
        SubtaskState::Completed(result) => {
            println!("✅ {}: {:?}", subtask.name, result);
        },
        SubtaskState::InProgress => {
            println!("⟳ {}: {}%", subtask.name, subtask.progress * 100.0);
        },
        SubtaskState::Pending => {
            println!("⏳ {}: Waiting", subtask.name);
        },
        SubtaskState::Failed(error) => {
            println!("❌ {}: {}", subtask.name, error);
        },
    }
}
```

---

## Adaptive Decision Making

Ultrathink makes intelligent decisions based on context:

### Decision Framework

```rust
pub trait DecisionMaker {
    fn evaluate_options(&self, options: Vec<Option>) -> Ranking;
    fn select_best(&self, ranking: Ranking) -> Option;
    fn adjust_for_constraints(&mut self, constraints: Constraints);
}

pub struct Option {
    pub name: String,
    pub pros: Vec<String>,
    pub cons: Vec<String>,
    pub estimated_cost: Cost,
    pub success_probability: f64,
}

pub struct Ranking {
    pub options: Vec<(Option, f64)>, // (option, score)
    pub recommended: Option,
    pub reasoning: String,
}
```

### Examples of Adaptive Decisions

#### 1. Template Selection

```rust
// User: "Generate blog code"
// Available templates: rust-models, graphql-schema, rest-api

let analysis = analyze_ontology(&ontology);

if analysis.has_relationships {
    // Recommend graph-based
    select_template("graphql-schema")
} else if analysis.simple_types {
    // Recommend models
    select_template("rust-models")
} else {
    // Try all and pick best
    let results = generate_with_all(&templates)?;
    pick_by_quality(&results)
}
```

#### 2. Conflict Resolution

```rust
// Two templates want to generate the same file
// rust-models wants: src/models.rs
// graphql-schema wants: src/models.rs

let conflict = ConflictResolver::new()
    .prefer_by_priority(template_priority)
    .merge_compatible_output(&models, &schema)
    .or_generate_to_different_files();

apply_resolution(&conflict)?;
```

#### 3. Performance Optimization

```rust
// Large ontology with 1000 classes
// Ultrathink decides whether to:
// 1. Generate everything at once (fast, high memory)
// 2. Generate in batches (slower, low memory)
// 3. Use distributed generation (complex setup)

if ontology.size < 100 {
    strategy = GenerationStrategy::All
} else if ontology.size < 10_000 {
    strategy = GenerationStrategy::Batches {
        batch_size: 100
    }
} else {
    strategy = GenerationStrategy::Distributed {
        workers: num_cpus::get()
    }
}
```

---

## Self-Healing and Error Recovery

Ultrathink automatically detects and recovers from failures:

### Failure Detection

```rust
pub struct HealthCheck {
    pub is_healthy: bool,
    pub issues: Vec<HealthIssue>,
    pub recovery_available: bool,
}

pub enum HealthIssue {
    AgentCrashed(AgentId),
    MemoryLeak(usize),
    DeadlockDetected(Duration),
    TimeoutExceeded(TaskId),
    OutputInvalid(String),
}
```

### Automatic Recovery

```rust
match health_check.issues[0] {
    HealthIssue::AgentCrashed(agent_id) => {
        // Restart agent
        agents.restart(agent_id)?;

        // Re-run failed task
        let result = wip.retry_last_task()?;
    },

    HealthIssue::TimeoutExceeded(task_id) => {
        // Increase timeout or reduce load
        increase_timeout(&task_id);
        or_reduce_batch_size();

        // Retry
        wip.retry_task(&task_id)?;
    },

    HealthIssue::OutputInvalid(error) => {
        // Fall back to alternative strategy
        let alt_strategy = find_alternative_strategy()?;
        retry_with_strategy(&alt_strategy)?;
    },
}
```

---

## Learning and Improvement

Ultrathink learns from each generation to improve future performance:

### Learning Mechanism

```rust
pub struct LearningSystem {
    pub pattern_database: HashMap<Pattern, PatternMetrics>,
    pub strategy_effectiveness: HashMap<Strategy, EffectivenessScore>,
    pub agent_performance: HashMap<AgentId, PerformanceMetrics>,
}

pub struct PatternMetrics {
    pub occurrence_count: u64,
    pub success_rate: f64,
    pub avg_duration: Duration,
    pub quality_score: f64,
}

impl LearningSystem {
    pub fn learn_from_result(&mut self, result: &GenerationResult) {
        // Update pattern database
        for pattern in result.detected_patterns {
            let metrics = self.pattern_database
                .entry(pattern)
                .or_insert_default();

            metrics.occurrence_count += 1;
            metrics.success_rate = (metrics.success_rate * count + success) / (count + 1);
            metrics.avg_duration = calculate_avg(metrics.avg_duration, result.duration);
            metrics.quality_score = result.quality_score;
        }

        // Update strategy effectiveness
        for strategy in result.strategies_used {
            let score = self.strategy_effectiveness
                .entry(strategy)
                .or_insert(0.0);

            *score = (*score * history + result.quality_score) / (history + 1);
        }
    }

    pub fn recommend_strategy(&self, context: &Context) -> Strategy {
        // Find best strategy for this context
        self.strategy_effectiveness
            .iter()
            .filter(|s| s.0.applicable_to(context))
            .max_by_key(|s| s.1)
            .map(|s| s.0.clone())
            .unwrap_or_default()
    }
}
```

### Feedback Loop

```
Generation → Validation → Metrics → Learning → Improved Strategy
    ↑                                                    ↓
    └────────────────────────────────────────────────────┘
```

---

## Monitoring and Observability

### Core Metrics

```rust
pub struct UlrathinkMetrics {
    pub generation_count: u64,
    pub success_rate: f64,
    pub avg_duration: Duration,
    pub quality_score: f64,

    pub agent_utilization: HashMap<AgentId, f64>,
    pub agent_efficiency: HashMap<AgentId, f64>,
    pub agent_error_rate: HashMap<AgentId, f64>,

    pub memory_usage: u64,
    pub cpu_usage: f64,

    pub learned_patterns: u64,
    pub strategy_effectiveness: HashMap<String, f64>,
}
```

### Dashboard Access

```rust
use ggen_ultrathink::metrics::MetricsDashboard;

let dashboard = MetricsDashboard::connect("localhost:9090")?;

// View real-time metrics
println!("{}", dashboard.current_status());

// Export for monitoring
dashboard.export_prometheus("prometheus.txt")?;

// Historical analysis
let history = dashboard.metrics_last_24h()?;
println!("Success rate: {}%", history.success_rate * 100.0);
println!("Avg duration: {:?}", history.avg_duration);
```

### Example Dashboard Output

```
Ultrathink Status Dashboard
═════════════════════════════════════════

Current Work (3 tasks in progress)
  Task A (Generate Rust)      ████████░░ 84%
  Task B (Validate Schema)    ██████░░░░ 60%
  Task C (Optimize Output)    ███░░░░░░░ 30%

Agent Status
  ✅ Analysis Agent        - Idle (completed 124 tasks)
  ✅ Generation Agent      - Active (current: 12 files)
  ✅ Validation Agent      - Active (checking output)
  ⚠️  Optimization Agent    - Backlogged (2 pending)

Metrics (Last 24h)
  Generation Success Rate:    98.7%
  Avg Generation Time:        342ms
  Quality Score (avg):        0.94

  Most Effective Strategy:    "Parallel Generation"
  Most Common Pattern:        "Class Hierarchy"

Memory Usage:               256 MB / 2048 MB (12.5%)
CPU Usage:                  2.4 cores / 16 cores (15%)
```

---

## Use Cases

### 1. Complex Ontology Generation

```rust
// User provides large, complex ontology
// Ultrathink automatically:
// 1. Analyzes structure
// 2. Detects patterns (hierarchies, relationships, cycles)
// 3. Plans optimal task sequence
// 4. Generates with multiple templates
// 5. Validates and optimizes
// 6. Learns for next time

let result = ultrathink.generate_from_ontology("complex.ttl").await?;
```

### 2. Multi-Language Generation

```rust
// Generate same code in 4 languages
// Ultrathink coordinates agents to:
// 1. Parse ontology once
// 2. Dispatch to language-specific generators
// 3. Resolve conflicts between languages
// 4. Optimize each output for target platform
// 5. Combine results with cross-language validation

let result = ultrathink.multi_language_generate(
    ontology,
    vec!["rust", "typescript", "python", "java"]
).await?;
```

### 3. Incremental Generation

```rust
// Ontology changed, only regenerate affected code
// Ultrathink:
// 1. Detects changes (diff analysis)
// 2. Plans minimal regeneration
// 3. Updates only affected files
// 4. Preserves manual edits (frozen sections)
// 5. Validates no regressions

let result = ultrathink.incremental_generate(
    old_ontology,
    new_ontology,
    &generated_files
).await?;
```

---

## Best Practices

1. **Enable Metrics** - Monitor Ultrathink performance
2. **Review Decisions** - Understand why choices were made
3. **Provide Feedback** - Help Ultrathink learn better strategies
4. **Handle Failures Gracefully** - Use self-healing features
5. **Tune Parameters** - Adjust timeouts and resource limits

---

## Troubleshooting

### Ultrathink Takes Too Long

**Cause:** Too many agents competing, inefficient task breakdown

**Solution:**
```rust
ultrathink
    .set_max_agents(4)
    .set_timeout(Duration::from_secs(30))
    .set_task_batch_size(50);
```

### Memory Usage Growing

**Cause:** Learning system accumulating too much history

**Solution:**
```rust
ultrathink.learning_system().cleanup_old_patterns(
    Duration::from_days(7)
)?;
```

### WIP Synchronization Failures

**Cause:** Agent crashes or network issues

**Solution:**
```rust
ultrathink.enable_auto_recovery(true);
ultrathink.set_checkpoint_interval(Duration::from_secs(10));
```

---

## Next Steps

1. Enable autonomous generation in your projects
2. Monitor Ultrathink metrics and dashboard
3. Review generated code quality and suggestions
4. Provide feedback to improve learning system
5. Explore multi-agent coordination features

## Resources

- **Ultrathink API:** `ggen ultrathink --help`
- **Agent Reference:** See crate documentation
- **WIP System:** Advanced usage guide
- **Metrics Dashboard:** Web interface on port 9090
