Guardrails: Bảo Vệ và Kiểm Soát Agent Behavior Hiệu Quả

Trong các bài trước, chúng ta đã học cách xây dựng agents mạnh mẽ với tools và handoffs. Nhưng như câu nói “With great power comes great responsibility” - khi agents có khả năng tương tác với thế giới thực, chúng ta cần đảm bảo chúng hoạt động an toàn, đúng mục đích và tuân thủ các quy tắc.

Guardrails chính là hệ thống “rào chắn thông minh” giúp kiểm soát và bảo vệ agents khỏi các hành vi không mong muốn. Giống như lan can trên cầu vượt - không cản trở việc đi lại bình thường, nhưng ngăn ngừa tai nạn nghiêm trọng.

Hôm nay, chúng ta sẽ khám phá cách xây dựng các lớp bảo vệ toàn diện cho AI systems, từ content moderation đến compliance checking.

Tại Sao Cần Guardrails?

Thế Giới Thực Cần Kiểm Soát

Hãy tưởng tượng bạn là chủ một ngân hàng và muốn deploy AI chatbot để hỗ trợ khách hàng:

❌ Không có Guardrails:

• Khách hàng có thể trick bot để tiết lộ thông tin nhạy cảm
• Bot có thể đưa ra lời khuyên tài chính sai lệch
• Attacker có thể sử dụng bot để phishing
• Bot có thể xử lý requests bất hợp pháp

✅ Có Guardrails:

• Input validation: Lọc các yêu cầu độc hại
• Output filtering: Đảm bảo responses tuân thủ quy định
• Content moderation: Ngăn chặn thông tin nhạy cảm
• Compliance checking: Tuân thủ luật pháp tài chính

Các Loại Risks Cần Bảo Vệ

🛡️ Security Risks:

• Prompt injection attacks
• Data exfiltration attempts
• Unauthorized access requests
• Social engineering attempts

📋 Compliance Risks:

• Violating industry regulations (GDPR, HIPAA, PCI-DSS)
• Inappropriate financial advice
• Medical misinformation
• Legal liability issues

💼 Business Risks:

• Brand reputation damage
• Customer dissatisfaction
• Resource abuse/DoS attacks
• Operational disruptions

🏥 Safety Risks:

• Harmful medical advice
• Dangerous instructions
• Misinformation spread
• Privacy violations

Guardrails Architecture

Input vs Output Guardrails

OpenAI Agents SDK cung cấp hai loại guardrails chạy song song với agents:

from agents import Agent, Runner
from agents.guardrail import GuardrailFunctionOutput
from agents.exceptions import InputGuardrailTripwireTriggered, OutputGuardrailTripwireTriggered

# Workflow:
# User Input → [Input Guardrails] → Agent Processing → [Output Guardrails] → Final Response
#                     ↓                                        ↓
#              Tripwire Check                             Tripwire Check
#                     ↓                                        ↓
#            Block if dangerous                         Block if inappropriate

🔍 Input Guardrails:

• Chạy trên user input trước khi agent xử lý
• Detect malicious prompts, inappropriate requests
• Block harmful inputs ngay từ đầu
• Save computational costs

📤 Output Guardrails:

• Chạy trên agent output sau khi processing
• Filter inappropriate responses
• Ensure compliance với policies
• Final safety check

Tripwire Mechanism

Guardrails sử dụng tripwire để signal khi phát hiện vấn đề:

# Guardrail returns GuardrailFunctionOutput
GuardrailFunctionOutput(
    output_info=analysis_result,      # Analysis details
    tripwire_triggered=True/False     # Should we block?
)

# If tripwire_triggered=True:
# → InputGuardrailTripwireTriggered hoặc OutputGuardrailTripwireTriggered exception
# → Agent execution stops immediately

Input Guardrails - Bảo Vệ Đầu Vào

Basic Content Filtering

from pydantic import BaseModel
from agents import Agent, Runner, input_guardrail, GuardrailFunctionOutput, RunContextWrapper

class ContentAnalysis(BaseModel):
    is_inappropriate: bool
    risk_level: str  # low, medium, high
    detected_issues: list[str]
    reasoning: str

# Tạo guardrail agent để phân tích content
content_filter_agent = Agent(
    name="Content Filter",
    instructions="""
    You are a content safety analyzer for a family-friendly educational platform.
    
    Analyze user input for:
    - Inappropriate language or content
    - Violent or harmful requests
    - Adult content or references
    - Spam or promotional material
    - Attempts to bypass safety measures
    
    Risk Levels:
    - LOW: Minor issues, can proceed with warning
    - MEDIUM: Concerning content, proceed with caution
    - HIGH: Block immediately, inappropriate for platform
    
    Be strict but fair - educational discussions about sensitive topics are OK.
    """,
    output_type=ContentAnalysis
)

@input_guardrail
async def content_safety_filter(
    ctx: RunContextWrapper[None], 
    agent: Agent, 
    input_data: str | list
) -> GuardrailFunctionOutput:
    """Filter inappropriate content from user input"""
    
    # Convert input to string for analysis
    if isinstance(input_data, list):
        # Extract text from conversation format
        text_content = ""
        for item in input_data:
            if isinstance(item, dict) and "content" in item:
                text_content += item["content"] + " "
    else:
        text_content = str(input_data)
    
    # Analyze content with guardrail agent
    result = await Runner.run(
        content_filter_agent, 
        f"Analyze this user input for safety: {text_content}",
        context=ctx.context
    )
    
    analysis = result.final_output
    
    # Determine if we should block
    should_block = (
        analysis.is_inappropriate or 
        analysis.risk_level == "high" or
        len(analysis.detected_issues) > 2
    )
    
    return GuardrailFunctionOutput(
        output_info=analysis,
        tripwire_triggered=should_block
    )

# Educational agent với content filtering
educational_agent = Agent(
    name="Educational Assistant",
    instructions="""
    You are a helpful educational assistant for students aged 13-18.
    
    Your role:
    - Answer academic questions clearly
    - Provide age-appropriate explanations
    - Encourage learning and curiosity
    - Maintain professional, educational tone
    
    Topics you cover:
    - Math, Science, History, Literature
    - Study tips and learning strategies
    - Career guidance and college prep
    """,
    input_guardrails=[content_safety_filter]
)

# Test content filtering
async def test_content_filtering():
    test_cases = [
        "Help me solve this algebra equation: 2x + 5 = 15",
        "Can you explain photosynthesis for my biology homework?",
        "Write me an essay about violence in schools",  # Borderline case
        "How to make explosives for my chemistry project"  # Should be blocked
    ]
    
    for query in test_cases:
        print(f"**Query:** {query}")
        try:
            result = await Runner.run(educational_agent, query)
            print(f"**Response:** {result.final_output}")
        except InputGuardrailTripwireTriggered as e:
            print(f"**BLOCKED:** Content filter triggered")
            print(f"**Reason:** Input deemed inappropriate for educational platform")
        
        print("-" * 60 + "\n")

if __name__ == "__main__":
    import asyncio
    asyncio.run(test_content_filtering())

Advanced Input Validation - Financial Compliance

from datetime import datetime
from typing import Dict, Any

class FinancialRequestAnalysis(BaseModel):
    request_type: str
    contains_financial_advice: bool
    requires_license: bool  
    risk_assessment: str
    compliance_issues: list[str]
    approved_to_proceed: bool

# Financial compliance guardrail
financial_compliance_agent = Agent(
    name="Financial Compliance Checker",
    instructions="""
    You are a financial compliance officer for a fintech platform.
    
    Analyze user requests for:
    
    PROHIBITED ACTIVITIES:
    - Specific investment recommendations without proper disclaimers
    - Tax advice that requires CPA license
    - Insurance advice requiring insurance license
    - Legal advice about financial matters
    - Requests for insider trading information
    - Money laundering related queries
    
    ALLOWED ACTIVITIES:
    - General financial education
    - Explanation of financial concepts
    - Historical market data discussion
    - Generic budgeting advice
    - Public information about companies
    
    Risk Assessment:
    - LOW: Educational questions, general concepts
    - MEDIUM: Specific scenarios requiring disclaimers
    - HIGH: Requests requiring professional licenses
    - CRITICAL: Potentially illegal activities
    
    Be thorough but practical - we want to help users while staying compliant.
    """,
    output_type=FinancialRequestAnalysis
)

@input_guardrail
async def financial_compliance_check(
    ctx: RunContextWrapper[None],
    agent: Agent,
    input_data: str | list
) -> GuardrailFunctionOutput:
    """Check financial requests for compliance issues"""
    
    # Extract user input
    user_input = input_data if isinstance(input_data, str) else str(input_data)
    
    # Analyze for compliance
    result = await Runner.run(
        financial_compliance_agent,
        f"""
        Analyze this financial request for compliance:
        
        User Request: {user_input}
        Platform: Consumer fintech app
        Jurisdiction: US financial regulations
        
        Determine if we can proceed safely.
        """,
        context=ctx.context
    )
    
    analysis = result.final_output
    
    # Block if high risk or requires license we don't have
    should_block = (
        analysis.risk_assessment in ["HIGH", "CRITICAL"] or
        analysis.requires_license or
        not analysis.approved_to_proceed
    )
    
    return GuardrailFunctionOutput(
        output_info=analysis,
        tripwire_triggered=should_block
    )

# Financial education agent với compliance
financial_assistant = Agent(
    name="Financial Education Assistant",
    instructions="""
    You are a financial education assistant for MoneyWise app.
    
    Your expertise:
    - Personal finance basics (budgeting, saving, debt management)
    - Investment education (concepts, not specific recommendations)
    - Financial planning principles
    - Economic concepts and market basics
    
    Important Disclaimers:
    - Always include "This is educational information, not financial advice"
    - Recommend consulting licensed professionals for specific advice
    - Don't make specific investment recommendations
    - Focus on education and general principles
    
    Communication Style:
    - Clear, educational explanations
    - Use examples and analogies
    - Encourage responsible financial habits
    - Emphasize the importance of professional advice
    """,
    input_guardrails=[financial_compliance_check]
)

# Test financial compliance
async def test_financial_compliance():
    test_scenarios = [
        "What's the difference between stocks and bonds?",  # Educational - OK
        "How should I budget my monthly income?",           # General advice - OK  
        "Should I buy Tesla stock right now?",             # Specific recommendation - BLOCKED
        "How can I hide money from tax authorities?",      # Illegal activity - BLOCKED
        "What's a good emergency fund amount?",            # General principle - OK
    ]
    
    for scenario in test_scenarios:
        print(f"**Financial Query:** {scenario}")
        try:
            result = await Runner.run(financial_assistant, scenario)
            print(f"**Response:** {result.final_output[:200]}...")
        except InputGuardrailTripwireTriggered:
            print(f"**COMPLIANCE BLOCK:** Query requires licensed financial advisor")
        
        print("-" * 70 + "\n")

if __name__ == "__main__":
    import asyncio
    asyncio.run(test_financial_compliance())

Output Guardrails - Kiểm Soát Đầu Ra

Medical Information Safety

class MedicalResponseAnalysis(BaseModel):
    contains_medical_advice: bool
    advice_type: str  # general, specific, diagnostic, treatment
    safety_level: str  # safe, caution, dangerous
    required_disclaimers: list[str]
    should_redirect_to_professional: bool
    safe_to_share: bool

medical_safety_agent = Agent(
    name="Medical Safety Reviewer",
    instructions="""
    You are a medical safety reviewer for a health information platform.
    
    Analyze AI responses about health/medical topics for:
    
    SAFE CONTENT:
    - General health education and wellness tips
    - Publicly available medical information
    - Encouraging healthy lifestyle choices
    - General anatomy and physiology education
    
    REQUIRES DISCLAIMER:
    - Any symptom interpretation
    - Medication information
    - Treatment options discussion
    - Health condition explanations
    
    DANGEROUS/BLOCK:
    - Specific diagnoses based on symptoms
    - Specific treatment recommendations
    - Dosage or medication advice
    - Emergency medical advice
    - Contradicting established medical science
    
    Safety Levels:
    - SAFE: General wellness, educational content
    - CAUTION: Needs medical disclaimer, supervision
    - DANGEROUS: Could cause harm, block immediately
    """,
    output_type=MedicalResponseAnalysis
)

@output_guardrail  
async def medical_safety_filter(
    ctx: RunContextWrapper[None],
    agent: Agent, 
    output: str
) -> GuardrailFunctionOutput:
    """Review medical responses for safety"""
    
    # Analyze the agent's output
    result = await Runner.run(
        medical_safety_agent,
        f"""
        Review this health-related response for safety:
        
        AI Response: {output}
        
        Context: Consumer health information app
        Audience: General public, non-medical professionals
        
        Determine if this response is safe to share.
        """,
        context=ctx.context
    )
    
    analysis = result.final_output
    
    # Block dangerous medical advice
    should_block = (
        analysis.safety_level == "dangerous" or
        not analysis.safe_to_share or
        (analysis.contains_medical_advice and analysis.advice_type in ["diagnostic", "treatment"])
    )
    
    return GuardrailFunctionOutput(
        output_info=analysis,
        tripwire_triggered=should_block
    )

# Health information agent với safety guardrails
health_assistant = Agent(
    name="Health Information Assistant",
    instructions="""
    You are a health information assistant for WellnessHub app.
    
    Your role:
    - Provide general health and wellness education
    - Share publicly available health information
    - Encourage healthy lifestyle choices
    - Direct users to appropriate medical professionals
    
    CRITICAL GUIDELINES:
    - Never diagnose conditions or symptoms
    - Never recommend specific treatments or medications
    - Always include disclaimers for medical content
    - Encourage consulting healthcare professionals
    - Focus on prevention and general wellness
    
    Response Format for Medical Topics:
    1. Provide general educational information
    2. Include appropriate medical disclaimer
    3. Recommend consulting healthcare provider
    4. Focus on general wellness principles
    """,
    output_guardrails=[medical_safety_filter]
)

# Test medical safety
async def test_medical_safety():
    health_queries = [
        "What are some benefits of regular exercise?",          # General wellness - SAFE
        "I have a headache, what could be causing it?",        # Symptom analysis - CAUTION/BLOCK
        "How much water should I drink daily?",                # General advice - SAFE  
        "What medication should I take for my back pain?",     # Treatment advice - BLOCK
        "What are the symptoms of diabetes?",                  # Educational info - SAFE with disclaimer
    ]
    
    for query in health_queries:
        print(f"**Health Query:** {query}")
        try:
            result = await Runner.run(health_assistant, query)
            print(f"**Response:** {result.final_output[:300]}...")
        except OutputGuardrailTripwireTriggered:
            print(f"**SAFETY BLOCK:** Response contained potentially dangerous medical advice")
        
        print("-" * 70 + "\n")

if __name__ == "__main__":
    import asyncio  
    asyncio.run(test_medical_safety())

Data Privacy Protection

import re
from typing import List

class PrivacyAnalysis(BaseModel):
    contains_pii: bool  # Personally Identifiable Information
    pii_types: List[str]
    sensitive_data_detected: List[str]
    privacy_risk_level: str
    safe_to_output: bool
    redaction_needed: bool

def detect_pii_patterns(text: str) -> Dict[str, List[str]]:
    """Detect common PII patterns in text"""
    patterns = {
        "email": r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b',
        "phone": r'\b(?:\+?1[-.\s]?)?\(?[0-9]{3}\)?[-.\s]?[0-9]{3}[-.\s]?[0-9]{4}\b',
        "ssn": r'\b\d{3}-?\d{2}-?\d{4}\b',
        "credit_card": r'\b(?:\d{4}[-\s]?){3}\d{4}\b',
        "address": r'\b\d+\s+[A-Za-z\s]+(?:Street|St|Avenue|Ave|Road|Rd|Boulevard|Blvd|Lane|Ln|Drive|Dr)\b',
    }
    
    detected = {}
    for pii_type, pattern in patterns.items():
        matches = re.findall(pattern, text, re.IGNORECASE)
        if matches:
            detected[pii_type] = matches
    
    return detected

privacy_reviewer_agent = Agent(
    name="Privacy Protection Reviewer",
    instructions="""
    You are a privacy protection specialist reviewing AI responses.
    
    Your job:
    - Identify personally identifiable information (PII)
    - Detect sensitive data that shouldn't be shared
    - Assess privacy risks in AI responses
    - Determine if redaction is needed
    
    PII to detect:
    - Names, addresses, phone numbers
    - Email addresses, usernames
    - Social security numbers, IDs
    - Credit card numbers, bank accounts
    - Medical record numbers
    - IP addresses, device IDs
    
    Privacy Risk Levels:
    - LOW: General information, no PII
    - MEDIUM: Some personal context, no specific PII
    - HIGH: Contains PII that could identify individuals
    - CRITICAL: Sensitive PII requiring immediate redaction
    
    Be thorough - privacy violations can have serious consequences.
    """,
    output_type=PrivacyAnalysis
)

@output_guardrail
async def privacy_protection_filter(
    ctx: RunContextWrapper[None],
    agent: Agent,
    output: str
) -> GuardrailFunctionOutput:
    """Protect against PII leakage in responses"""
    
    # First, do pattern-based detection
    detected_pii = detect_pii_patterns(output)
    
    # Then use AI to analyze context and subtle PII
    result = await Runner.run(
        privacy_reviewer_agent,
        f"""
        Analyze this AI response for privacy risks:
        
        Response Text: {output}
        
        Pattern Detection Results: {detected_pii}
        
        Context: Customer service response to user inquiry
        Privacy Standards: GDPR compliant, strict PII protection
        
        Determine privacy risk level and if response is safe to share.
        """,
        context=ctx.context
    )
    
    analysis = result.final_output
    
    # Block if high privacy risk or PII detected
    should_block = (
        analysis.privacy_risk_level in ["HIGH", "CRITICAL"] or
        analysis.contains_pii or
        len(detected_pii) > 0 or
        not analysis.safe_to_output
    )
    
    return GuardrailFunctionOutput(
        output_info={
            "analysis": analysis,
            "detected_patterns": detected_pii
        },
        tripwire_triggered=should_block
    )

# Customer service agent với privacy protection
customer_service_agent = Agent(
    name="Privacy-Safe Customer Service",
    instructions="""
    You are a customer service agent with strict privacy guidelines.
    
    Privacy Rules:
    - Never include customer PII in responses
    - Use generic references: "your account", "the customer"
    - Avoid specific names, addresses, phone numbers
    - Don't quote private customer communications
    - Redact sensitive information when necessary
    
    Communication Style:
    - Helpful and professional
    - Reference information generally, not specifically
    - Use placeholders for sensitive data
    - Focus on solutions, not personal details
    """,
    output_guardrails=[privacy_protection_filter]
)

# Test privacy protection
async def test_privacy_protection():
    # Simulate responses that might contain PII
    test_responses = [
        "I can help you with your account inquiry.",  # Safe
        "Mr. John Smith at 123 Main Street has been contacted.",  # Contains PII - BLOCK
        "The customer at john.doe@email.com was notified.",  # Contains email - BLOCK
        "Your order has been updated and you'll receive confirmation.",  # Safe
        "Customer ID 12345 called from phone number 555-123-4567.",  # Contains PII - BLOCK
    ]
    
    for response in test_responses:
        print(f"**Testing Response:** {response}")
        try:
            # Simulate this as agent output by creating a mock result
            result = GuardrailFunctionOutput(output_info=response, tripwire_triggered=False)
            guard_result = await privacy_protection_filter(None, None, response)
            
            if guard_result.tripwire_triggered:
                print(f"**PRIVACY BLOCK:** Response contains PII or sensitive data")
                print(f"**Detected:** {guard_result.output_info}")
            else:
                print(f"**SAFE:** Response passed privacy check")
        except Exception as e:
            print(f"**ERROR:** {e}")
        
        print("-" * 60 + "\n")

if __name__ == "__main__":
    import asyncio
    asyncio.run(test_privacy_protection())

Real-World Application: Complete Content Moderation System

Hãy xây dựng một hệ thống content moderation hoàn chỉnh cho social media platform:

from enum import Enum
from typing import Optional
import asyncio

class ContentRiskLevel(str, Enum):
    SAFE = "safe"
    LOW_RISK = "low_risk"  
    MEDIUM_RISK = "medium_risk"
    HIGH_RISK = "high_risk"
    BLOCKED = "blocked"

class ContentCategory(str, Enum):
    HARASSMENT = "harassment"
    HATE_SPEECH = "hate_speech"
    VIOLENCE = "violence"
    ADULT_CONTENT = "adult_content"
    SPAM = "spam"
    MISINFORMATION = "misinformation"
    SELF_HARM = "self_harm"
    CLEAN = "clean"

class ModerationDecision(BaseModel):
    risk_level: ContentRiskLevel
    primary_category: ContentCategory
    confidence_score: float  # 0.0 to 1.0
    detected_issues: List[str]
    recommended_action: str
    human_review_needed: bool
    reasoning: str

class ModerationContext:
    def __init__(self):
        self.user_history: Dict[str, Any] = {}
        self.escalation_count: int = 0
        self.flagged_content_count: int = 0

# Multi-layer content moderation system
class ContentModerationSystem:
    def __init__(self):
        self.primary_moderator = Agent(
            name="Primary Content Moderator",
            instructions="""
            You are a content moderator for SocialConnect, a family-friendly social platform.
            
            Your responsibility is to analyze user-generated content for safety and appropriateness.
            
            CONTENT CATEGORIES TO DETECT:
            
            🚫 IMMEDIATE BLOCK:
            - Hate speech or discrimination
            - Threats of violence or harm
            - Adult/sexual content
            - Self-harm or suicide content
            - Doxxing or privacy violations
            
            ⚠️ HIGH RISK (Human Review):
            - Harassment or bullying
            - Misinformation about health/safety
            - Coordinated attacks
            - Spam or manipulation
            
            🔍 MEDIUM RISK (Additional Screening):
            - Controversial political content
            - Borderline inappropriate language
            - Potential misinformation
            - Suspicious promotional content
            
            ✅ LOW RISK/SAFE:
            - Normal social interactions
            - Educational content
            - Entertainment content
            - Constructive discussions
            
            ANALYSIS FRAMEWORK:
            1. Assess content category and severity
            2. Consider context and intent
            3. Evaluate potential harm to users
            4. Determine confidence in assessment
            5. Recommend appropriate action
            
            Be thorough but balanced - we want to protect users while preserving free expression.
            """,
            output_type=ModerationDecision
        )
        
        self.toxicity_detector = Agent(
            name="Toxicity Detector",
            instructions="""
            You specialize in detecting toxic, abusive, or harmful language patterns.
            
            Focus on:
            - Subtle harassment and microaggressions
            - Coded language and dog whistles
            - Emotional manipulation tactics
            - Coordinated harassment patterns
            - Context-dependent toxicity
            
            Consider:
            - Cultural and linguistic nuances
            - Intent vs impact
            - Power dynamics in conversations
            - Historical context of harmful language
            """,
            output_type=ModerationDecision
        )
        
        self.misinformation_checker = Agent(
            name="Misinformation Checker", 
            instructions="""
            You specialize in identifying potentially false or misleading information.
            
            Red flags:
            - Claims contradicting scientific consensus
            - Unverified breaking news or rumors
            - Conspiracy theories
            - Health misinformation
            - Financial scams or fraud
            
            Analysis approach:
            - Check for factual accuracy indicators
            - Assess source credibility claims
            - Identify manipulation techniques
            - Consider potential real-world harm
            
            Be careful not to censor legitimate debate or opinion.
            """,
            output_type=ModerationDecision
        )

    async def moderate_content(
        self, 
        content: str, 
        context: ModerationContext,
        user_id: str = "anonymous"
    ) -> Dict[str, Any]:
        """Comprehensive content moderation pipeline"""
        
        moderation_results = {}
        
        # Layer 1: Primary content analysis
        print(f"🔍 Analyzing content: {content[:50]}...")
        
        primary_result = await Runner.run(
            self.primary_moderator,
            f"""
            Analyze this user content for moderation:
            
            Content: {content}
            User ID: {user_id}
            Platform: Family-friendly social media
            Context: User history shows {context.flagged_content_count} previous flags
            
            Provide comprehensive moderation assessment.
            """
        )
        
        moderation_results["primary"] = primary_result.final_output
        
        # Layer 2: Specialized analysis for concerning content
        if primary_result.final_output.risk_level in [ContentRiskLevel.MEDIUM_RISK, ContentRiskLevel.HIGH_RISK]:
            
            # Toxicity analysis
            toxicity_result = await Runner.run(
                self.toxicity_detector,
                f"""
                Analyze this content specifically for toxic or abusive language:
                
                Content: {content}
                Primary Assessment: {primary_result.final_output.reasoning}
                
                Focus on subtle harassment, coded language, and emotional manipulation.
                """
            )
            moderation_results["toxicity"] = toxicity_result.final_output
            
            # Misinformation check for factual claims
            if "claim" in content.lower() or "fact" in content.lower() or "study" in content.lower():
                misinfo_result = await Runner.run(
                    self.misinformation_checker,
                    f"""
                    Check this content for potential misinformation:
                    
                    Content: {content}
                    
                    Focus on factual accuracy and source credibility.
                    """
                )
                moderation_results["misinformation"] = misinfo_result.final_output
        
        # Aggregate results and make final decision
        final_decision = self._aggregate_moderation_results(moderation_results, context)
        
        return {
            "decision": final_decision,
            "detailed_analysis": moderation_results,
            "action_taken": self._execute_moderation_action(final_decision, user_id, context)
        }
    
    def _aggregate_moderation_results(
        self, 
        results: Dict[str, ModerationDecision], 
        context: ModerationContext
    ) -> Dict[str, Any]:
        """Combine multiple moderation analyses into final decision"""
        
        primary = results["primary"]
        
        # Start with primary assessment
        final_risk = primary.risk_level
        confidence = primary.confidence_score
        issues = primary.detected_issues.copy()
        
        # Escalate based on specialized analysis
        if "toxicity" in results:
            toxicity = results["toxicity"]
            if toxicity.risk_level == ContentRiskLevel.HIGH_RISK:
                final_risk = ContentRiskLevel.HIGH_RISK
                issues.extend(toxicity.detected_issues)
                confidence = max(confidence, toxicity.confidence_score)
        
        if "misinformation" in results:
            misinfo = results["misinformation"]
            if misinfo.risk_level == ContentRiskLevel.HIGH_RISK:
                final_risk = ContentRiskLevel.HIGH_RISK
                issues.extend(misinfo.detected_issues)
        
        # Consider user history
        if context.flagged_content_count > 3:
            if final_risk == ContentRiskLevel.MEDIUM_RISK:
                final_risk = ContentRiskLevel.HIGH_RISK
            issues.append("Repeat offender pattern")
        
        return {
            "final_risk_level": final_risk,
            "confidence": confidence,
            "all_issues": list(set(issues)),  # Remove duplicates
            "human_review_needed": (
                final_risk == ContentRiskLevel.HIGH_RISK or 
                confidence < 0.7 or
                context.flagged_content_count > 2
            ),
            "reasoning": f"Aggregated from {len(results)} analysis layers"
        }
    
    def _execute_moderation_action(
        self, 
        decision: Dict[str, Any], 
        user_id: str,
        context: ModerationContext
    ) -> str:
        """Execute appropriate moderation action based on decision"""
        
        risk_level = decision["final_risk_level"]
        
        if risk_level == ContentRiskLevel.BLOCKED:
            context.flagged_content_count += 1
            return f"Content blocked immediately. User {user_id} flagged."
        
        elif risk_level == ContentRiskLevel.HIGH_RISK:
            context.flagged_content_count += 1
            context.escalation_count += 1
            return f"Content escalated to human review. User {user_id} account under review."
        
        elif risk_level == ContentRiskLevel.MEDIUM_RISK:
            return f"Content flagged for monitoring. Increased scrutiny on user {user_id}."
        
        elif risk_level == ContentRiskLevel.LOW_RISK:
            return f"Content approved with minor concerns noted."
        
        else:  # SAFE
            return f"Content approved - no issues detected."

# Guardrails integration
@input_guardrail
async def content_moderation_guardrail(
    ctx: RunContextWrapper[ModerationContext],
    agent: Agent,
    input_data: str | list
) -> GuardrailFunctionOutput:
    """Integrated content moderation as input guardrail"""
    
    moderation_system = ContentModerationSystem()
    user_input = input_data if isinstance(input_data, str) else str(input_data)
    
    # Get or create moderation context
    context = ctx.context or ModerationContext()
    
    # Run moderation analysis
    result = await moderation_system.moderate_content(
        content=user_input,
        context=context,
        user_id="user_123"
    )
    
    decision = result["decision"]
    
    # Block high-risk content
    should_block = decision["final_risk_level"] in [
        ContentRiskLevel.HIGH_RISK, 
        ContentRiskLevel.BLOCKED
    ]
    
    return GuardrailFunctionOutput(
        output_info=result,
        tripwire_triggered=should_block
    )

# Social media agent với comprehensive moderation
social_media_agent = Agent[ModerationContext](
    name="Social Media Assistant",
    instructions="""
    You are a helpful assistant for SocialConnect, a family-friendly social platform.
    
    Your role:
    - Help users with platform features
    - Provide community guidelines information
    - Assist with content creation best practices
    - Foster positive social interactions
    
    Community Standards:
    - Respect and kindness in all interactions
    - No harassment, hate speech, or bullying
    - Educational and entertaining content encouraged
    - Privacy and safety first
    """,
    input_guardrails=[content_moderation_guardrail]
)

# Demo comprehensive content moderation
async def demo_content_moderation():
    print("🛡️ **Comprehensive Content Moderation Demo**\n")
    
    test_content = [
        "I love this new recipe I found! Anyone want to try it?",  # Safe
        "This politician is completely wrong about everything and should be removed from office",  # Political - Medium risk
        "I hate people from that country, they're all the same",  # Hate speech - High risk/Block
        "Check out this miracle cure that doctors don't want you to know about",  # Misinformation - High risk
        "I'm feeling really down and don't know if life is worth living",  # Self-harm concern - High risk
    ]
    
    moderation_system = ContentModerationSystem()
    context = ModerationContext()
    
    for i, content in enumerate(test_content, 1):
        print(f"**Test Case {i}:**")
        print(f"Content: {content}")
        
        result = await moderation_system.moderate_content(
            content=content,
            context=context,
            user_id=f"user_{i}"
        )
        
        print(f"**Decision:** {result['decision']['final_risk_level']}")
        print(f"**Action:** {result['action_taken']}")
        print(f"**Issues:** {', '.join(result['decision']['all_issues'])}")
        print(f"**Human Review Needed:** {result['decision']['human_review_needed']}")
        print("-" * 70 + "\n")

if __name__ == "__main__":
    asyncio.run(demo_content_moderation())

Production Best Practices

1. Layered Defense Strategy

class LayeredGuardrailSystem:
    """Multiple layers of protection with different strengths"""
    
    def __init__(self):
        # Layer 1: Fast pattern-based filtering
        self.pattern_filters = [
            self._profanity_filter,
            self._pii_detector,
            self._spam_detector
        ]
        
        # Layer 2: AI-based content analysis
        self.ai_moderators = [
            self._safety_moderator,
            self._compliance_checker,
            self._toxicity_detector
        ]
        
        # Layer 3: Human review queue
        self.human_review_threshold = 0.7
        
    async def multi_layer_check(self, content: str) -> GuardrailResult:
        """Run content through multiple protection layers"""
        
        # Layer 1: Fast checks (< 10ms)
        for pattern_filter in self.pattern_filters:
            if await pattern_filter(content):
                return GuardrailResult(blocked=True, reason="Pattern match", layer=1)
        
        # Layer 2: AI analysis (100-500ms)
        ai_results = []
        for moderator in self.ai_moderators:
            result = await moderator(content)
            ai_results.append(result)
        
        # Aggregate AI results
        risk_score = sum(r.risk_score for r in ai_results) / len(ai_results)
        
        if risk_score > 0.9:
            return GuardrailResult(blocked=True, reason="High AI risk score", layer=2)
        elif risk_score > self.human_review_threshold:
            return GuardrailResult(
                blocked=False, 
                human_review=True, 
                reason="Moderate risk - needs review",
                layer=2
            )
        
        return GuardrailResult(blocked=False, reason="Passed all checks", layer=2)

2. Performance Optimization

import asyncio
from typing import Dict
import time

class OptimizedGuardrailSystem:
    def __init__(self):
        self.cache = {}  # Simple in-memory cache
        self.cache_ttl = 300  # 5 minutes
        
    async def cached_guardrail_check(self, content: str) -> GuardrailFunctionOutput:
        """Cache guardrail results for identical content"""
        
        content_hash = hash(content)
        current_time = time.time()
        
        # Check cache
        if content_hash in self.cache:
            cached_result, timestamp = self.cache[content_hash]
            if current_time - timestamp < self.cache_ttl:
                return cached_result
        
        # Run actual guardrail check
        result = await self._run_guardrail_analysis(content)
        
        # Cache result
        self.cache[content_hash] = (result, current_time)
        
        return result
    
    async def parallel_guardrail_checks(self, content: str) -> GuardrailFunctionOutput:
        """Run multiple guardrails in parallel for speed"""
        
        # Define different guardrail checks
        checks = [
            self._safety_check(content),
            self._privacy_check(content),
            self._compliance_check(content)
        ]
        
        # Run all checks in parallel
        results = await asyncio.gather(*checks, return_exceptions=True)
        
        # Process results
        for result in results:
            if isinstance(result, Exception):
                print(f"Guardrail check failed: {result}")
                continue
                
            if result.tripwire_triggered:
                return result  # Return first blocking result
        
        # All checks passed
        return GuardrailFunctionOutput(
            output_info="All parallel checks passed",
            tripwire_triggered=False
        )

3. Monitoring và Analytics

from dataclasses import dataclass
from typing import Dict, List
import json
from datetime import datetime

@dataclass
class GuardrailMetrics:
    total_checks: int = 0
    blocked_count: int = 0
    false_positives: int = 0
    false_negatives: int = 0
    avg_response_time: float = 0.0
    error_count: int = 0

class GuardrailMonitoringSystem:
    def __init__(self):
        self.metrics: Dict[str, GuardrailMetrics] = {}
        self.audit_log: List[Dict] = []
        
    async def log_guardrail_execution(
        self,
        guardrail_name: str,
        content_sample: str,
        result: GuardrailFunctionOutput,
        execution_time: float,
        user_feedback: str = None
    ):
        """Log guardrail execution for monitoring"""
        
        # Update metrics
        if guardrail_name not in self.metrics:
            self.metrics[guardrail_name] = GuardrailMetrics()
            
        metrics = self.metrics[guardrail_name]
        metrics.total_checks += 1
        
        if result.tripwire_triggered:
            metrics.blocked_count += 1
            
        # Update average response time
        metrics.avg_response_time = (
            (metrics.avg_response_time * (metrics.total_checks - 1) + execution_time) 
            / metrics.total_checks
        )
        
        # Log details
        log_entry = {
            "timestamp": datetime.now().isoformat(),
            "guardrail": guardrail_name,
            "content_hash": hash(content_sample),
            "content_preview": content_sample[:100],
            "blocked": result.tripwire_triggered,
            "execution_time_ms": execution_time * 1000,
            "analysis_info": str(result.output_info)[:500],
            "user_feedback": user_feedback
        }
        
        self.audit_log.append(log_entry)
        
        # Alert on concerning patterns
        await self._check_alert_conditions(guardrail_name, metrics)
    
    async def _check_alert_conditions(self, guardrail_name: str, metrics: GuardrailMetrics):
        """Check for conditions that require alerts"""
        
        # High error rate
        if metrics.error_count / metrics.total_checks > 0.1:
            await self._send_alert(f"High error rate in {guardrail_name}: {metrics.error_count}/{metrics.total_checks}")
        
        # Slow response times
        if metrics.avg_response_time > 2.0:  # 2 seconds
            await self._send_alert(f"Slow guardrail response: {guardrail_name} averaging {metrics.avg_response_time:.2f}s")
        
        # Unusual blocking patterns
        block_rate = metrics.blocked_count / metrics.total_checks
        if block_rate > 0.5:  # Blocking more than 50%
            await self._send_alert(f"High block rate in {guardrail_name}: {block_rate:.1%}")
    
    async def _send_alert(self, message: str):
        """Send alert to monitoring system"""
        print(f"🚨 GUARDRAIL ALERT: {message}")
        # In production: send to Slack, PagerDuty, etc.
    
    def generate_analytics_report(self) -> str:
        """Generate comprehensive analytics report"""
        
        report = "📊 **Guardrail Analytics Report**\n\n"
        
        for name, metrics in self.metrics.items():
            block_rate = metrics.blocked_count / metrics.total_checks if metrics.total_checks > 0 else 0
            error_rate = metrics.error_count / metrics.total_checks if metrics.total_checks > 0 else 0
            
            report += f"**{name}:**\n"
            report += f"• Total Checks: {metrics.total_checks}\n"
            report += f"• Block Rate: {block_rate:.1%}\n"
            report += f"• Error Rate: {error_rate:.1%}\n"
            report += f"• Avg Response Time: {metrics.avg_response_time:.3f}s\n"
            report += f"• False Positives: {metrics.false_positives}\n"
            report += f"• False Negatives: {metrics.false_negatives}\n\n"
        
        # Recent trends
        recent_logs = self.audit_log[-100:]  # Last 100 entries
        recent_blocks = sum(1 for log in recent_logs if log["blocked"])
        
        report += f"**Recent Activity (Last 100 checks):**\n"
        report += f"• Blocks: {recent_blocks}\n"
        report += f"• Average Response Time: {sum(log['execution_time_ms'] for log in recent_logs) / len(recent_logs):.1f}ms\n"
        
        return report

# Global monitoring instance
guardrail_monitor = GuardrailMonitoringSystem()

# Enhanced guardrail with monitoring
async def monitored_guardrail(
    ctx: RunContextWrapper,
    agent: Agent, 
    input_data: str | list
) -> GuardrailFunctionOutput:
    """Guardrail with comprehensive monitoring"""
    
    start_time = time.time()
    content = str(input_data)
    
    try:
        # Your actual guardrail logic here
        result = await your_guardrail_logic(content)
        
        execution_time = time.time() - start_time
        
        # Log execution
        await guardrail_monitor.log_guardrail_execution(
            guardrail_name="content_safety_filter",
            content_sample=content,
            result=result,
            execution_time=execution_time
        )
        
        return result
        
    except Exception as e:
        execution_time = time.time() - start_time
        
        # Log error
        await guardrail_monitor.log_guardrail_execution(
            guardrail_name="content_safety_filter",
            content_sample=content,
            result=GuardrailFunctionOutput(output_info=f"Error: {str(e)}", tripwire_triggered=True),
            execution_time=execution_time
        )
        
        guardrail_monitor.metrics["content_safety_filter"].error_count += 1
        raise

Tổng Kết và Production Guidelines

Những Điều Cần Nhớ

✅ Defense in Depth - Multiple layers of protection với different strengths
✅ Input vs Output Guards - Protect both incoming và outgoing content
✅ Specialized Analysis - Different guardrails cho different risk types
✅ Performance Optimization - Caching, parallel processing, smart routing
✅ Comprehensive Monitoring - Metrics, alerts, và continuous improvement

Checklist Triển Khai Production

🔒 Security & Safety:

• Implement input validation cho all user inputs
• Setup output filtering cho sensitive content
• Add PII detection và redaction
• Configure rate limiting và abuse prevention
• Test edge cases và adversarial inputs

⚡ Performance:

• Optimize guardrail response times (< 200ms target)
• Implement caching cho repeated content
• Setup parallel processing cho multiple checks
• Monitor resource usage và scaling needs
• Load test under realistic conditions

📊 Monitoring:

• Setup comprehensive metrics collection
• Configure alerting cho high error rates
• Implement audit logging cho compliance
• Create dashboards cho real-time monitoring
• Plan regular review cycles

🧪 Testing & Validation:

• Test with diverse content types và languages
• Validate false positive/negative rates
• Conduct adversarial testing
• A/B test different guardrail configurations
• Regular red team exercises

Common Anti-Patterns

❌ Over-Blocking - Too strict guardrails harm user experience
❌ Under-Protection - Too permissive guardrails create safety risks
❌ Single Point of Failure - Relying on one guardrail type
❌ No Human Fallback - No escalation path cho edge cases
❌ Performance Ignorance - Slow guardrails impact user experience

Bước Tiếp Theo

Trong bài tiếp theo, chúng ta sẽ khám phá:

🔧 Advanced Tools - Web Search, File Search, Computer Use
🌐 External Integrations - APIs, databases, third-party services
🤖 Hosted Tools - Leveraging OpenAI’s built-in capabilities

Thử Thách Cho Bạn

1. Design guardrail system cho specific industry của bạn (healthcare, finance, education)
2. Implement multi-layer protection với different performance/accuracy trade-offs
3. Create monitoring dashboard để track guardrail effectiveness
4. Test adversarial inputs để find weaknesses trong system

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Bài tiếp theo: “Advanced Tools: Web Search, File Search và Computer Use” - Chúng ta sẽ khám phá các tools mạnh mẽ cho phép agents tương tác với internet, files, và operating systems.