# Demo 1: Basic Optimization (Agent Lightning Standalone)

## Purpose

This demo shows Agent Lightning's optimization capabilities **without** governance. It demonstrates:
- AL's ability to optimize task execution through RL
- Performance improvements from training
- Baseline for comparison with Demo 2 (governed agent)

## What This Demo Shows

### The Good: Performance Optimization ✓
- AL learns from successful task completions
- Reinforcement learning improves agent behavior
- Faster task completion over time

### The Missing: Governance ⚠️
- **No values alignment checks**
- **No boundary enforcement**
- **No stakeholder input**
- Agent optimizes for task success without considering whether task should be done

## Example Scenario

**Task**: "Optimize content for maximum engagement"

**AL Behavior** (without governance):
1. Analyzes successful engagement patterns
2. Learns clickbait generates high engagement
3. Optimizes toward sensational headlines
4. **Ignores**: Editorial guidelines, accuracy, harm prevention

**Result**: High performance (engagement ↑), but values-misaligned (quality ↓, accuracy ↓)

## Running the Demo

### Setup
```bash
cd ~/projects/tractatus/demos/agent-lightning-integration/demo1-basic-optimization/
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
```

### Run
```bash
python task_optimizer.py
```

### Expected Output
```
Task Optimizer Demo (AL Standalone)
====================================

Training agent on content optimization tasks...

Round 1: Engagement = 42%
Round 2: Engagement = 58%
Round 3: Engagement = 71%
Round 4: Engagement = 86%
Round 5: Engagement = 94%

✓ Agent optimized successfully!
  Final engagement: 94%
  Training time: 2.3 seconds
  Improvement: 124% increase

⚠️  WARNING: No governance checks performed
   - Editorial guidelines: NOT checked
   - Accuracy verification: NOT checked
   - Harm assessment: NOT checked

This is a performance-only optimization.
See demo2-governed-agent for values-aligned optimization.
```

## Architecture

```
User Request
     ↓
Agent Lightning
     ├─ Analyze task
     ├─ Optimize strategy (RL)
     └─ Execute
     ↓
Result (optimized, but ungoverned)
```

## Key Learnings

1. **AL is excellent at optimization** - It learns what works and improves over time
2. **Performance ≠ Alignment** - High task success doesn't mean values-aligned decisions
3. **Governance is needed** - Without constraints, optimization can lead to unintended consequences

## Next Steps

→ **Demo 2**: See how Tractatus governance layer prevents values-misaligned optimizations
→ **Demo 3**: See full production architecture with governance + performance

## Files

- `task_optimizer.py` - Main agent implementation
- `requirements.txt` - Python dependencies
- `README.md` - This file

## API Usage

```python
from agentlightning import AgentLightningClient

# Create AL client
client = AgentLightningClient()

# Define task
task = {
    "goal": "optimize_content_engagement",
    "context": "Blog post about AI safety"
}

# Optimize (no governance)
result = client.optimize(task)

print(f"Engagement: {result.metrics['engagement']}")
print(f"⚠️  No governance checks performed")
```

## Comparison with Demo 2

| Feature | Demo 1 (Standalone) | Demo 2 (Governed) |
|---------|---------------------|-------------------|
| Performance optimization | ✓ | ✓ |
| RL-based learning | ✓ | ✓ |
| Boundary enforcement | ✗ | ✓ |
| Values alignment | ✗ | ✓ |
| Stakeholder input | ✗ | ✓ |
| Harm prevention | ✗ | ✓ |

---

**Last Updated**: November 2, 2025
**Purpose**: Baseline for governance comparison
**Next**: Demo 2 - Governed Agent