diff --git a/examples/error_handling.py b/examples/error_handling.py
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+"""
+Error Handling and Resilience Patterns in Agentics
+
+This example demonstrates robust transduction patterns including:
+- Retry logic with exponential backoff
+- Graceful error handling and fallback strategies
+- Partial result processing
+- Input validation and sanitization
+- Error categorization and reporting
+"""
+
+import asyncio
+import logging
+from typing import Optional, List, Union
+from enum import Enum
+
+from pydantic import BaseModel, Field, ValidationError
+from agentics import AG
+
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+class ErrorType(str, Enum):
+    VALIDATION_ERROR = "validation_error"
+    LLM_ERROR = "llm_error"
+    TIMEOUT_ERROR = "timeout_error"
+    PARSE_ERROR = "parse_error"
+    UNKNOWN_ERROR = "unknown_error"
+
+
+class RobustAnswer(BaseModel):
+    """A resilient answer type that captures both success and failure states."""
+    
+    answer: Optional[str] = Field(None, description="The extracted answer")
+    confidence: Optional[float] = Field(None, ge=0.0, le=1.0, description="Confidence score 0-1")
+    source_question: Optional[str] = Field(None, description="Original input question")
+    error_type: Optional[ErrorType] = Field(None, description="Type of error if processing failed")
+    error_message: Optional[str] = Field(None, description="Detailed error message")
+    retry_count: int = Field(0, description="Number of retry attempts made")
+    processing_time_ms: Optional[float] = Field(None, description="Processing time in milliseconds")
+    is_success: bool = Field(True, description="Whether processing succeeded")
+
+
+class ValidationResult(BaseModel):
+    """Result of input validation with sanitized input."""
+    
+    is_valid: bool
+    sanitized_input: Optional[str] = None
+    validation_errors: List[str] = []
+
+
+def validate_and_sanitize_input(input_text: str) -> ValidationResult:
+    """Validate and sanitize input before processing."""
+    
+    errors = []
+    sanitized = input_text.strip()
+    
+    # Basic validation rules
+    if not sanitized:
+        errors.append("Empty input")
+        return ValidationResult(is_valid=False, validation_errors=errors)
+    
+    if len(sanitized) > 1000:
+        errors.append("Input too long, truncating")
+        sanitized = sanitized[:1000] + "..."
+    
+    # Remove potential harmful characters or patterns
+    forbidden_patterns = ["<script>", "javascript:", "eval("]
+    for pattern in forbidden_patterns:
+        if pattern.lower() in sanitized.lower():
+            errors.append(f"Removed forbidden pattern: {pattern}")
+            sanitized = sanitized.replace(pattern, "[SANITIZED]")
+    
+    return ValidationResult(
+        is_valid=True,
+        sanitized_input=sanitized,
+        validation_errors=errors
+    )
+
+
+async def safe_transduction_with_retry(
+    agent: AG,
+    input_data: Union[str, List[str]],
+    max_retries: int = 3,
+    timeout_seconds: float = 30.0
+) -> AG:
+    """
+    Perform transduction with retry logic and comprehensive error handling.
+    
+    Args:
+        agent: The AG instance to use for transduction
+        input_data: Input data to process
+        max_retries: Maximum number of retry attempts
+        timeout_seconds: Timeout for each attempt
+        
+    Returns:
+        AG instance with results (may include error states)
+    """
+    
+    import time
+    from asyncio import wait_for, TimeoutError
+    
+    # Ensure input_data is a list
+    if isinstance(input_data, str):
+        input_data = [input_data]
+    
+    results = []
+    
+    for i, input_item in enumerate(input_data):
+        start_time = time.time()
+        retry_count = 0
+        
+        # Validate input first
+        validation = validate_and_sanitize_input(input_item)
+        if not validation.is_valid:
+            results.append(RobustAnswer(
+                source_question=input_item,
+                error_type=ErrorType.VALIDATION_ERROR,
+                error_message="; ".join(validation.validation_errors),
+                is_success=False,
+                processing_time_ms=(time.time() - start_time) * 1000
+            ))
+            continue
+        
+        # Use sanitized input
+        sanitized_input = validation.sanitized_input
+        
+        # Retry loop
+        while retry_count <= max_retries:
+            try:
+                # Create a fresh agent instance for each retry
+                temp_agent = AG(atype=RobustAnswer, llm=agent.llm)
+                
+                # Attempt transduction with timeout
+                result = await wait_for(
+                    temp_agent << [sanitized_input],
+                    timeout=timeout_seconds
+                )
+                
+                # Extract the result and add metadata
+                if result.states and len(result.states) > 0:
+                    answer = result.states[0]
+                    answer.source_question = input_item
+                    answer.retry_count = retry_count
+                    answer.processing_time_ms = (time.time() - start_time) * 1000
+                    answer.is_success = True
+                    results.append(answer)
+                    logger.info(f"Successfully processed item {i+1} after {retry_count} retries")
+                    break
+                else:
+                    raise ValueError("No result returned from transduction")
+                    
+            except TimeoutError:
+                logger.warning(f"Timeout on item {i+1}, retry {retry_count+1}")
+                retry_count += 1
+                if retry_count > max_retries:
+                    results.append(RobustAnswer(
+                        source_question=input_item,
+                        error_type=ErrorType.TIMEOUT_ERROR,
+                        error_message=f"Timeout after {timeout_seconds}s, {max_retries} retries",
+                        retry_count=retry_count - 1,
+                        is_success=False,
+                        processing_time_ms=(time.time() - start_time) * 1000
+                    ))
+                else:
+                    # Exponential backoff
+                    await asyncio.sleep(2 ** retry_count)
+                    
+            except ValidationError as e:
+                logger.error(f"Validation error on item {i+1}: {e}")
+                results.append(RobustAnswer(
+                    source_question=input_item,
+                    error_type=ErrorType.VALIDATION_ERROR,
+                    error_message=str(e),
+                    retry_count=retry_count,
+                    is_success=False,
+                    processing_time_ms=(time.time() - start_time) * 1000
+                ))
+                break
+                
+            except Exception as e:
+                logger.error(f"Unexpected error on item {i+1}, retry {retry_count+1}: {e}")
+                retry_count += 1
+                if retry_count > max_retries:
+                    results.append(RobustAnswer(
+                        source_question=input_item,
+                        error_type=ErrorType.UNKNOWN_ERROR,
+                        error_message=str(e),
+                        retry_count=retry_count - 1,
+                        is_success=False,
+                        processing_time_ms=(time.time() - start_time) * 1000
+                    ))
+                else:
+                    # Exponential backoff
+                    await asyncio.sleep(2 ** retry_count)
+    
+    # Create result agent with all results
+    result_agent = AG(atype=RobustAnswer)
+    result_agent.states = results
+    
+    return result_agent
+
+
+def analyze_results(agent: AG) -> dict:
+    """Analyze the results and provide a summary report."""
+    
+    total = len(agent.states)
+    successful = sum(1 for state in agent.states if state.is_success)
+    failed = total - successful
+    
+    # Categorize errors
+    error_counts = {}
+    total_retries = 0
+    total_processing_time = 0
+    
+    for state in agent.states:
+        if state.error_type:
+            error_counts[state.error_type] = error_counts.get(state.error_type, 0) + 1
+        total_retries += state.retry_count
+        if state.processing_time_ms:
+            total_processing_time += state.processing_time_ms
+    
+    return {
+        "total_items": total,
+        "successful": successful,
+        "failed": failed,
+        "success_rate": successful / total if total > 0 else 0,
+        "error_breakdown": error_counts,
+        "total_retries": total_retries,
+        "avg_processing_time_ms": total_processing_time / total if total > 0 else 0
+    }
+
+
+async def main():
+    """Demonstrate error handling patterns with various input scenarios."""
+    
+    print("🔧 Agentics Error Handling Demo")
+    print("=" * 50)
+    
+    # Test inputs with different error scenarios
+    test_inputs = [
+        "What is the capital of France?",  # Normal case
+        "",  # Empty input - validation error
+        "What is 2 + 2?",  # Simple math
+        "x" * 1500,  # Too long - will be truncated
+        "<script>alert('xss')</script>What is AI?",  # Malicious input
+        "What happens when the LLM fails to respond properly and we need fallback handling?",  # Complex case
+        "Simple question",  # Normal case
+    ]
+    
+    # Create agent
+    if not AG.get_llm_provider():
+        print("⚠️  No LLM provider configured. Set up your API key in .env file.")
+        return
+        
+    agent = AG(atype=RobustAnswer, llm=AG.get_llm_provider())
+    
+    print(f"📝 Processing {len(test_inputs)} test inputs with error handling...")
+    print()
+    
+    # Process with error handling
+    results = await safe_transduction_with_retry(
+        agent=agent,
+        input_data=test_inputs,
+        max_retries=2,
+        timeout_seconds=15.0
+    )
+    
+    # Display results
+    print("📊 RESULTS:")
+    print("-" * 50)
+    
+    for i, result in enumerate(results.states):
+        status = "✅" if result.is_success else "❌"
+        print(f"{status} Item {i+1}:")
+        print(f"   Question: {result.source_question[:50]}...")
+        if result.is_success:
+            print(f"   Answer: {result.answer}")
+            print(f"   Confidence: {result.confidence}")
+        else:
+            print(f"   Error: {result.error_type} - {result.error_message}")
+        print(f"   Retries: {result.retry_count}, Time: {result.processing_time_ms:.1f}ms")
+        print()
+    
+    # Summary analysis
+    analysis = analyze_results(results)
+    print("📈 SUMMARY ANALYSIS:")
+    print("-" * 50)
+    print(f"Success Rate: {analysis['success_rate']:.1%} ({analysis['successful']}/{analysis['total_items']})")
+    print(f"Total Retries: {analysis['total_retries']}")
+    print(f"Avg Processing Time: {analysis['avg_processing_time_ms']:.1f}ms")
+    
+    if analysis['error_breakdown']:
+        print("\nError Breakdown:")
+        for error_type, count in analysis['error_breakdown'].items():
+            print(f"  {error_type}: {count}")
+    
+    print("\n🎯 Key Takeaways:")
+    print("- Input validation prevents processing of malformed data")
+    print("- Retry logic with exponential backoff handles transient failures")
+    print("- Structured error reporting aids debugging and monitoring")
+    print("- Partial success handling allows processing to continue despite failures")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
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