# Self-Reflection Agent with Error Correction

## Overview
Demonstrates advanced agent capabilities with error correction and self-improvement loops. This agent can evaluate its own responses, identify mistakes, and iteratively improve its outputs through structured reflection patterns.

## Use Case
- Code review and debugging assistance
- Content quality improvement systems
- Educational feedback and tutoring
- Decision validation and refinement

## Agent Specification

### Core Functionality
- **Input**: Task instructions with quality criteria
- **Processing**: Initial response generation followed by self-evaluation
- **Output**: Refined results with improvement history
- **Memory**: Reflection history and improvement patterns

### Signature Pattern
```python
class SelfReflectionSignature(dspy.Signature):
    """Generate response with self-evaluation and iterative improvement."""
    task: str = dspy.InputField(desc="Task to complete with quality requirements")
    context: str = dspy.InputField(desc="Additional context or constraints")
    previous_attempts: str = dspy.InputField(desc="Previous attempts and feedback")

    response: str = dspy.OutputField(desc="Current best response to the task")
    self_evaluation: str = dspy.OutputField(desc="Critical assessment of response quality")
    improvement_areas: str = dspy.OutputField(desc="Specific areas needing improvement")
    confidence: float = dspy.OutputField(desc="Confidence in response quality (0.0-1.0)")
    should_iterate: bool = dspy.OutputField(desc="Whether further refinement is needed")
```

## Expected Execution Flow

### Phase 1: Initial Response Generation (0-200ms)
```
[00:00:000] WorkflowBuilder initialized with reflection loop
[00:00:025] Task parsed and quality criteria extracted
[00:00:050] Initial response generation started
[00:00:120] First draft response completed
[00:00:145] Self-evaluation process initiated
[00:00:180] Quality assessment completed
[00:00:200] Iteration decision made
```

### Phase 2: Reflection Loop (200ms-5s)
```
[00:00:200] Iteration 1: Initial response evaluation
{
  "response": "Draft response with potential issues",
  "self_evaluation": "Response lacks specificity in examples, argumentation could be stronger",
  "improvement_areas": "Add concrete examples, strengthen logical flow",
  "confidence": 0.65,
  "should_iterate": true
}

[00:01:200] Iteration 2: Improved response
{
  "response": "Enhanced response with concrete examples",
  "self_evaluation": "Significant improvement in clarity and examples",
  "improvement_areas": "Minor refinements in conclusion",
  "confidence": 0.85,
  "should_iterate": true
}

[00:02:800] Iteration 3: Final refinement
{
  "response": "Polished response meeting quality criteria",
  "self_evaluation": "Response meets all quality requirements",
  "improvement_areas": "None identified",
  "confidence": 0.92,
  "should_iterate": false
}
```

### Phase 3: Result Compilation (5s-5.2s)
```
[00:05:000] Reflection history compiled
[00:05:050] Improvement trajectory analyzed
[00:05:100] Final response validated
[00:05:150] Learning patterns extracted for future use
[00:05:200] Complete results returned
```

## Technical Requirements

### Dependencies
```python
# Core Kailash SDK
from kailash.workflow.builder import WorkflowBuilder
from kailash.runtime.local import LocalRuntime
from kailash.nodes.llm_agent import LLMAgentNode
from kailash.nodes.conditional import ConditionalNode
from kailash.nodes.loop import LoopNode

# Signature programming and utilities
import dspy
from typing import List, Dict, Optional
import json
```

### Configuration
```yaml
reflection_config:
  max_iterations: 5
  confidence_threshold: 0.85
  improvement_threshold: 0.1
  timeout_per_iteration: 30

llm_config:
  provider: "openai"
  model: "gpt-4"
  temperature: 0.3
  max_tokens: 800

quality_criteria:
  clarity_weight: 0.3
  accuracy_weight: 0.4
  completeness_weight: 0.3
```

### Memory Requirements
- **Runtime Memory**: ~150MB (includes reflection history)
- **Model Context**: 8K tokens maximum
- **Iteration Storage**: ~50MB for full reflection chain

## Architecture Overview

### Agent Coordination Pattern
```
Initial Task → Response Generator → Self Evaluator → Improvement Loop
     ↑                                                      ↓
     ←─────────── Quality Gate ←────────── Refined Response
```

### Data Flow
1. **Task Analysis**: Parse requirements and quality criteria
2. **Response Generation**: Create initial response using LLM
3. **Self-Evaluation**: Critical assessment of response quality
4. **Iteration Decision**: Determine if improvement needed
5. **Refinement Loop**: Iterative improvement until criteria met
6. **Learning Extraction**: Capture improvement patterns

## Success Criteria

### Functional Requirements
- ✅ Generates initial responses within quality parameters
- ✅ Accurately identifies areas for improvement
- ✅ Shows measurable quality improvement across iterations
- ✅ Converges to acceptable quality within max iterations

### Quality Metrics
- ✅ Final response quality score >85%
- ✅ Improvement trajectory shows consistent progress
- ✅ Self-evaluation accuracy >80% (human validated)
- ✅ Iteration efficiency (meaningful improvements per cycle)

### Performance Requirements
- ✅ Max 3 iterations for 80% of tasks
- ✅ Total processing time <10 seconds
- ✅ Memory growth <20MB per iteration
- ✅ Confidence calibration accuracy >85%

## Enterprise Considerations

### Quality Assurance
- Human-in-the-loop validation for critical tasks
- Quality metrics tracking and trending
- Automated regression testing for improvement patterns
- Audit trails for all reflection iterations

### Scalability
- Batch processing capabilities for multiple tasks
- Parallel iteration processing where possible
- Efficient memory management for long reflection chains
- Resource usage monitoring and optimization

### Integration
- Plugin architecture for domain-specific quality criteria
- Integration with existing QA and review systems
- API endpoints for external quality validators
- Workflow integration with approval processes

## Error Scenarios

### Infinite Iteration Loop
```python
# Safeguard against endless improvement cycles
{
  "final_response": "Best effort response after max iterations",
  "iterations_completed": 5,
  "final_confidence": 0.78,
  "termination_reason": "MAX_ITERATIONS_REACHED",
  "improvement_trajectory": [0.45, 0.62, 0.71, 0.76, 0.78]
}
```

### Quality Regression
```python
# Handling cases where iterations make response worse
{
  "final_response": "Best response from iteration history",
  "best_iteration": 2,
  "final_confidence": 0.84,
  "termination_reason": "QUALITY_REGRESSION_DETECTED",
  "note": "Reverted to iteration 2 due to quality decline"
}
```

### Evaluation Failure
```python
# Response when self-evaluation system fails
{
  "final_response": "Generated response (evaluation unavailable)",
  "iterations_completed": 1,
  "final_confidence": 0.5,
  "termination_reason": "EVALUATION_SYSTEM_ERROR",
  "fallback_mode": "BASIC_GENERATION"
}
```

## Testing Strategy

### Unit Tests
- Individual component testing (generator, evaluator, loop control)
- Quality metric calculation validation
- Iteration termination logic testing
- Memory leak detection across iterations

### Integration Tests
- End-to-end reflection workflow validation
- Quality improvement measurement
- Performance benchmarking across task types
- Integration with external quality systems

### Behavioral Tests
- Convergence behavior analysis
- Quality regression prevention
- Edge case handling (contradictory criteria)
- Human-AI agreement measurement

### Performance Tests
- Iteration efficiency measurement
- Memory usage profiling across reflection chains
- Concurrent reflection process testing
- Resource exhaustion and recovery scenarios

## Implementation Details

### Key Components
1. **Response Generator**: Initial response creation with task understanding
2. **Quality Evaluator**: Self-assessment using structured criteria
3. **Improvement Analyzer**: Identifies specific areas needing enhancement
4. **Iteration Controller**: Manages reflection loop and termination
5. **Learning Recorder**: Captures patterns for future improvement

### Algorithms
- **Quality Scoring**: Multi-dimensional assessment with weighted criteria
- **Improvement Detection**: Delta analysis between iterations
- **Convergence Logic**: Adaptive threshold adjustment based on task complexity
- **Pattern Learning**: Historical analysis for reflection strategy optimization
