# Simple Q&A Agent - Detailed Execution Flow

## Complete Execution Trace

### Scenario: Standard Question Processing
**Input**: "What is machine learning?"
**Context**: "Explain for a general audience"

### Phase 1: Initialization (0-50ms)

```
[00:00:000] INFO: Initializing Q&A workflow
[00:00:005] DEBUG: Creating WorkflowBuilder instance
[00:00:012] DEBUG: QASignature class defined with fields:
             - Input: question (str), context (str)
             - Output: answer (str), confidence (float), reasoning (str)
[00:00:025] DEBUG: LLMAgentNode configuration:
             - Provider: openai
             - Model: gpt-4
             - Temperature: 0.1
             - Max tokens: 300
[00:00:035] DEBUG: Adding LLMAgentNode to workflow with ID 'qa_agent'
[00:00:042] DEBUG: LocalRuntime initialized
[00:00:048] INFO: Workflow initialized in 48.2ms
```

### Phase 2: Input Processing (50-80ms)

```
[00:00:050] INFO: Processing question: What is machine learning?...
[00:00:055] DEBUG: Input validation passed:
             - Question length: 26 characters
             - Context length: 32 characters
             - No prohibited content detected
[00:00:065] DEBUG: Workflow input prepared:
{
  "qa_agent": {
    "question": "What is machine learning?",
    "context": "Explain for a general audience"
  }
}
[00:00:075] INFO: Executing Q&A workflow
[00:00:078] DEBUG: Workflow built and validated successfully
```

### Phase 3: LLM Processing (80-1400ms)

```
[00:00:080] DEBUG: LLMAgentNode 'qa_agent' starting execution
[00:00:085] DEBUG: Signature template construction:
             System: "Answer questions accurately and concisely with confidence scoring."
             User prompt: "Question: What is machine learning?\nContext: Explain for a general audience\n\nProvide your response with answer, confidence, and reasoning."
[00:00:095] DEBUG: OpenAI API call initiated:
             - Model: gpt-4
             - Temperature: 0.1
             - Max tokens: 300
             - Request ID: req_abc123xyz
[00:00:120] DEBUG: API request sent, awaiting response...
[00:01:350] DEBUG: OpenAI API response received:
             - Response time: 1230ms
             - Tokens used: 145 (prompt: 45, completion: 100)
             - Finish reason: stop
[00:01:365] DEBUG: Raw LLM output:
{
  "answer": "Machine learning is a subset of artificial intelligence that enables computers to learn and improve from data without being explicitly programmed for every task. Instead of following pre-written instructions, ML systems identify patterns in data and use those patterns to make predictions or decisions about new information.",
  "confidence": 0.92,
  "reasoning": "This is a well-established definition that accurately captures the core concept of ML for a general audience, avoiding technical jargon while maintaining accuracy."
}
```

### Phase 4: Output Validation (1400-1450ms)

```
[00:01:400] DEBUG: Output validation starting
[00:01:405] DEBUG: Required fields check:
             ✅ answer: present (267 characters)
             ✅ confidence: present (0.92)
             ✅ reasoning: present (138 characters)
[00:01:415] DEBUG: Confidence score validation:
             - Value: 0.92
             - Range check: ✅ (0.0 <= 0.92 <= 1.0)
             - Threshold check: ✅ (0.92 >= 0.7)
[00:01:425] DEBUG: Content quality checks:
             ✅ Answer relevance score: 0.94
             ✅ No harmful content detected
             ✅ Appropriate length for question complexity
[00:01:445] DEBUG: Execution metrics calculated:
             - Total execution time: 1365ms
             - LLM processing time: 1270ms
             - Validation time: 45ms
```

### Phase 5: Response Assembly (1450-1500ms)

```
[00:01:450] DEBUG: Assembling final response
[00:01:465] DEBUG: Metadata compilation:
{
  "execution_time_ms": 1365.2,
  "run_id": "run_20240923_214500_abc123",
  "model_used": "gpt-4",
  "timestamp": 1695500700.123
}
[00:01:485] INFO: Question processed in 1365.2ms (confidence: 0.92)
[00:01:495] DEBUG: Response structure validated and ready for return
[00:01:500] INFO: Workflow execution completed successfully
```

### Final Response Structure

```json
{
  "answer": "Machine learning is a subset of artificial intelligence that enables computers to learn and improve from data without being explicitly programmed for every task. Instead of following pre-written instructions, ML systems identify patterns in data and use those patterns to make predictions or decisions about new information.",
  "confidence": 0.92,
  "reasoning": "This is a well-established definition that accurately captures the core concept of ML for a general audience, avoiding technical jargon while maintaining accuracy.",
  "metadata": {
    "execution_time_ms": 1365.2,
    "run_id": "run_20240923_214500_abc123",
    "model_used": "gpt-4",
    "timestamp": 1695500700.123
  }
}
```

## Error Scenario: API Timeout

### Input: "What is quantum computing?"
### Execution Flow:

```
[00:00:000] INFO: Processing question: What is quantum computing?...
[00:00:080] DEBUG: OpenAI API call initiated
[00:00:120] DEBUG: API request sent, awaiting response...
[00:30:000] ERROR: OpenAI API timeout after 30 seconds
[00:30:005] DEBUG: Retry attempt 1/3 initiated
[00:30:100] DEBUG: API request sent, awaiting response...
[01:00:000] ERROR: OpenAI API timeout after 30 seconds (retry 1)
[01:00:005] DEBUG: Retry attempt 2/3 initiated
[01:00:100] DEBUG: API request sent, awaiting response...
[01:30:000] ERROR: OpenAI API timeout after 30 seconds (retry 2)
[01:30:005] DEBUG: Retry attempt 3/3 initiated
[01:30:100] DEBUG: API request sent, awaiting response...
[02:00:000] ERROR: OpenAI API timeout after 30 seconds (retry 3)
[02:00:005] ERROR: All retry attempts exhausted
[02:00:010] INFO: Generating error response for API timeout
[02:00:015] DEBUG: Error response assembled:
{
  "answer": "I'm temporarily unable to process your question. Please try again.",
  "confidence": 0.0,
  "reasoning": "Service temporarily unavailable - API timeout after 3 retry attempts",
  "metadata": {
    "error_code": "API_TIMEOUT",
    "timestamp": 1695500820.123,
    "execution_time_ms": 0
  }
}
```

## Performance Metrics Dashboard

### Standard Operation Metrics
```
Response Time Distribution (last 1000 queries):
- P50: 1.2s
- P90: 2.1s
- P95: 2.8s
- P99: 4.2s

Success Rate: 99.2% (992/1000)
Average Confidence: 0.84

Error Breakdown:
- API Timeouts: 0.5% (5/1000)
- Invalid Input: 0.2% (2/1000)
- Content Filter: 0.1% (1/1000)
```

### Resource Utilization
```
Memory Usage:
- Baseline: 45MB
- Peak during processing: 78MB
- Post-GC: 52MB

CPU Usage:
- Idle: 0.1%
- During LLM call: 15.2%
- Response parsing: 8.7%

Network:
- Avg request size: 2.1KB
- Avg response size: 1.8KB
- Total bandwidth: 3.9KB per query
```

## Agent Decision Points

### Confidence Scoring Logic
```
High Confidence (0.8-1.0):
- Well-defined concepts with clear answers
- Questions matching training data patterns
- Sufficient context provided

Medium Confidence (0.5-0.8):
- Ambiguous questions requiring interpretation
- Complex topics with multiple valid answers
- Limited context provided

Low Confidence (0.0-0.5):
- Questions outside training scope
- Conflicting information in context
- Unclear or malformed queries
```

### Error Recovery Strategies
```
API Failures:
1. Exponential backoff retry (3 attempts)
2. Fallback to cached responses if available
3. Graceful degradation with error message

Content Issues:
1. Input sanitization and validation
2. Output content filtering
3. Compliance violation handling

Performance Issues:
1. Request queuing and rate limiting
2. Response time monitoring
3. Circuit breaker pattern for cascading failures
```

## Integration Touchpoints

### Monitoring Hooks
- **Pre-execution**: Input validation, rate limiting
- **During execution**: Progress tracking, performance metrics
- **Post-execution**: Quality scoring, audit logging
- **Error handling**: Failure categorization, alerting

### Audit Trail Points
- **Request received**: Question, context, timestamp
- **Processing started**: Workflow ID, configuration
- **LLM interaction**: Model, tokens, response time
- **Result generated**: Answer, confidence, reasoning
- **Response delivered**: Final output, execution metrics