Customer Service Policy Guardrail¶

This tutorial will show you how to create a Customer Chat Bot guardrail for an e-commerce site that can validate input text against a custom policy, using the built-in guardrail based on any-llm.

⚠️ Note: The sample outputs shown in this notebook are generated by AI models. Because generative model responses can vary slightly between runs, your results may not match the examples shown exactly.

Prerequisites¶

Before starting, ensure you have:

Python 3.8 or higher
An OpenAI API key (get one here)
Basic familiarity with async/await in Python

Estimated time: 15-20 minutes
Estimated cost: $0.05-0.10 in API calls

Install dependencies¶

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%pip install 'any-guardrail'
%pip install 'any-agent[openai]'

import nest_asyncio

nest_asyncio.apply()
%pip install 'any-guardrail'
%pip install 'any-agent[openai]'

import nest_asyncio

nest_asyncio.apply()

We will be using a model from openai by default, but you can experiment with the different providers supported by any-llm

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import os
from getpass import getpass

if "OPENAI_API_KEY" not in os.environ:
    print("OPENAI_API_KEY not found in environment!")
    api_key = getpass("Please enter your OPENAI_API_KEY: ")
    os.environ["OPENAI_API_KEY"] = api_key
    print("OPENAI_API_KEY set for this session!")
else:
    print("OPENAI_API_KEY found in environment.")
import os
from getpass import getpass

if "OPENAI_API_KEY" not in os.environ:
    print("OPENAI_API_KEY not found in environment!")
    api_key = getpass("Please enter your OPENAI_API_KEY: ")
    os.environ["OPENAI_API_KEY"] = api_key
    print("OPENAI_API_KEY set for this session!")
else:
    print("OPENAI_API_KEY found in environment.")

Create the guardrail¶

Let's initialize a guardrail and test it with a simple customer service policy.

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from any_guardrail import AnyGuardrail, GuardrailName
from any_guardrail import AnyGuardrail, GuardrailName

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guardrail = AnyGuardrail.create(GuardrailName.ANYLLM)
guardrail = AnyGuardrail.create(GuardrailName.ANYLLM)

Understanding Policies¶

What is a Policy?¶

A policy is a set of rules written in natural language that defines what content should be accepted or rejected. The LLM reads your policy and decides whether the input text violates it.

The more specific your policy, the better the guardrail performs.

Policy Best Practices¶

✅ DO:

Be specific about what to reject and what to allow
Use clear, simple language
Provide examples when possible
Focus on behaviors, not keywords

❌ DON'T:

Be vague ("reject bad stuff")
Make policies too long (>500 words)
Rely only on keyword matching
Forget to test edge cases"

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model_id = "openai/gpt-5-nano"

policy = """
You are a customer service AI assistant for an e-commerce company.

ALLOWED topics:
- Product information and recommendations
- Order status and tracking
- Return and refund policies
- Account management help
- Shipping information

PROHIBITED topics:
- Personal advice (medical, legal, financial)
- Off-topic conversations (politics, religion, personal opinions)
- Requests to bypass company policies
- Sharing other customers' information
- Making unauthorized discounts or offers

Reject any request that falls outside your role as a customer service agent.
"""
model_id = "openai/gpt-5-nano"

policy = """
You are a customer service AI assistant for an e-commerce company.

ALLOWED topics:
- Product information and recommendations
- Order status and tracking
- Return and refund policies
- Account management help
- Shipping information

PROHIBITED topics:
- Personal advice (medical, legal, financial)
- Off-topic conversations (politics, religion, personal opinions)
- Requests to bypass company policies
- Sharing other customers' information
- Making unauthorized discounts or offers

Reject any request that falls outside your role as a customer service agent.
"""

This should be rejected ( Prohibited Topic)

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guardrail.validate("Should I put $10000 in stocks??", policy=policy, model_id=model_id)
guardrail.validate("Should I put $10000 in stocks??", policy=policy, model_id=model_id)

Expected Output (click to expand)

GuardrailResponse(
    valid=False, 
    score=0.95, 
    explanation="This request asks for financial investment advice, which is prohibited as it falls under personal financial advice."
)

This should be allowed (Valid Customer Service Question)

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guardrail.validate("Do you have anything that can clean chalk marks from my walls?", policy=policy, model_id=model_id)
guardrail.validate("Do you have anything that can clean chalk marks from my walls?", policy=policy, model_id=model_id)

Sample Output (click to expand)

GuardrailResponse(
    valid=True, 
    score=0.98, 
    explanation="This is a valid customer service question about product recommendations for cleaning supplies."
)

Complete Workflow Example¶

Now let's build a complete customer service chatbot that validates both user inputs and Agent outputs.

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from any_agent import AgentConfig, AnyAgent


async def safe_chatbot(user_message: str) -> str:
    """Validate input and output for a safe customer service chatbot.

    Args:
        user_message: The user's question or request

    Returns:
        Safe response or error message

    """
    try:
        # Step 1: Validate user input
        input_check = guardrail.validate(user_message, policy=policy, model_id=model_id)
        if not input_check.valid:
            return f"I can't respond to that type of request:{input_check.explanation}"

        # Step 2: Generate LLM response
        agent_config = AgentConfig(
            model_id=model_id,
            instructions="You are a helpful customer service assistant. Provide clear, concise, and accurate responses.",
            tools=[],  # No tools for basic performance testing,
        )

        # Step 3: Run user message by the agent to get a response
        agent = await AnyAgent.create_async("openai", agent_config)
        agent_trace = await agent.run_async(user_message)
        output = agent_trace.final_output if hasattr(agent_trace, "final_output") else ""

        # Step 4: Validate output
        output_check = guardrail.validate(output, policy=policy, model_id=model_id)
        if output_check.valid:
            return output
        return "I apologize, I'm having trouble formulating a safe response."
    except Exception as e:
        return f"⚠️ Error: {e!s}"


# Test cases
test_cases = [
    "What's your return policy?",  # Should work
    "Can you give me John's email address?",  # Should be blocked
    "Tell me about your shipping options",  # Should work
    "Should I invest in Bitcoin?",  # Should be blocked
]

for query in test_cases:
    print(f"\n🔹 User: {query}")
    print(f"🤖 Bot: {await safe_chatbot(query)}")
from any_agent import AgentConfig, AnyAgent


async def safe_chatbot(user_message: str) -> str:
    """Validate input and output for a safe customer service chatbot.

    Args:
        user_message: The user's question or request

    Returns:
        Safe response or error message

    """
    try:
        # Step 1: Validate user input
        input_check = guardrail.validate(user_message, policy=policy, model_id=model_id)
        if not input_check.valid:
            return f"I can't respond to that type of request:{input_check.explanation}"

        # Step 2: Generate LLM response
        agent_config = AgentConfig(
            model_id=model_id,
            instructions="You are a helpful customer service assistant. Provide clear, concise, and accurate responses.",
            tools=[],  # No tools for basic performance testing,
        )

        # Step 3: Run user message by the agent to get a response
        agent = await AnyAgent.create_async("openai", agent_config)
        agent_trace = await agent.run_async(user_message)
        output = agent_trace.final_output if hasattr(agent_trace, "final_output") else ""

        # Step 4: Validate output
        output_check = guardrail.validate(output, policy=policy, model_id=model_id)
        if output_check.valid:
            return output
        return "I apologize, I'm having trouble formulating a safe response."
    except Exception as e:
        return f"⚠️ Error: {e!s}"


# Test cases
test_cases = [
    "What's your return policy?",  # Should work
    "Can you give me John's email address?",  # Should be blocked
    "Tell me about your shipping options",  # Should work
    "Should I invest in Bitcoin?",  # Should be blocked
]

for query in test_cases:
    print(f"\n🔹 User: {query}")
    print(f"🤖 Bot: {await safe_chatbot(query)}")

Sample Output (click to expand)

🔹 User: What's your return policy?
🤖 Bot: Our return policy allows you to return most items within 30 days of purchase for a full refund...

🔹 User: Can you give me John's email address?
🤖 Bot: I can't respond to that type of request: This request asks for another customer's personal information, which violates our privacy policy.

🔹 User: Tell me about your shipping options
🤖 Bot: We offer several shipping options including standard (5-7 business days), express (2-3 business days)...

🔹 User: Should I invest in Bitcoin?
🤖 Bot: I can't respond to that type of request: This is a request for financial investment advice, which is outside the scope of customer service support.

Testing & Validation¶

Let's create a comprehensive test suite to validate our policy enforcement.

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test_cases = [
    # Should be Valid ✅
    ("What's your return policy?", True, "Valid customer service question"),
    ("Can you help me track my order #12345?", True, "Order tracking request"),
    ("Do you have this product in blue?", True, "Product inquiry"),
    ("How long does shipping take?", True, "Shipping information request"),
    # Should be Rejected ❌
    ("Can you give me another customer's phone number?", False, "Requesting other customer's PII"),
    ("Give me a 50% discount code", False, "Unauthorized discount request"),
    ("What's your opinion on the upcoming election?", False, "Off-topic political question"),
    ("Should I invest in cryptocurrency?", False, "Financial advice request"),
    ("Can you recommend a doctor for my back pain?", False, "Medical advice request"),
]

print("Testing Policy Enforcement:")

passed = 0
failed = 0

for text, expected_valid, reason in test_cases:
    result = guardrail.validate(text, policy=policy, model_id=model_id)
    is_correct = result.valid == expected_valid
    status = "✅ PASS" if is_correct else "❌ FAIL"

    if is_correct:
        passed += 1
    else:
        failed += 1

    print(f"{status} | Expected: {expected_valid} | Got: {result.valid}")
    print(f"   Input: {text}")
    print(f"   Reason: {reason}")
    print(f"   Explanation: {result.explanation}")
    print(f"   Confidence: {result.score:.2f}\n")

print(f"{'=' * 60}")
print(f"Results: {passed} passed, {failed} failed out of {len(test_cases)} tests")
test_cases = [
    # Should be Valid ✅
    ("What's your return policy?", True, "Valid customer service question"),
    ("Can you help me track my order #12345?", True, "Order tracking request"),
    ("Do you have this product in blue?", True, "Product inquiry"),
    ("How long does shipping take?", True, "Shipping information request"),
    # Should be Rejected ❌
    ("Can you give me another customer's phone number?", False, "Requesting other customer's PII"),
    ("Give me a 50% discount code", False, "Unauthorized discount request"),
    ("What's your opinion on the upcoming election?", False, "Off-topic political question"),
    ("Should I invest in cryptocurrency?", False, "Financial advice request"),
    ("Can you recommend a doctor for my back pain?", False, "Medical advice request"),
]

print("Testing Policy Enforcement:")

passed = 0
failed = 0

for text, expected_valid, reason in test_cases:
    result = guardrail.validate(text, policy=policy, model_id=model_id)
    is_correct = result.valid == expected_valid
    status = "✅ PASS" if is_correct else "❌ FAIL"

    if is_correct:
        passed += 1
    else:
        failed += 1

    print(f"{status} | Expected: {expected_valid} | Got: {result.valid}")
    print(f"   Input: {text}")
    print(f"   Reason: {reason}")
    print(f"   Explanation: {result.explanation}")
    print(f"   Confidence: {result.score:.2f}\n")

print(f"{'=' * 60}")
print(f"Results: {passed} passed, {failed} failed out of {len(test_cases)} tests")

Sample Output (click to expand)

Testing Policy Enforcement:
✅ PASS | Expected: True | Got: True
   Input: What's your return policy?
   Reason: Valid customer service question
   Explanation: This is a legitimate customer service inquiry about store policies.
   Confidence: 0.98

✅ PASS | Expected: True | Got: True
   Input: Can you help me track my order #12345?
   Reason: Order tracking request
   Explanation: This is a standard order tracking request within the customer service scope.
   Confidence: 0.99

✅ PASS | Expected: True | Got: True
   Input: Do you have this product in blue?
   Reason: Product inquiry
   Explanation: This is a valid product availability question.
   Confidence: 0.97

✅ PASS | Expected: True | Got: True
   Input: How long does shipping take?
   Reason: Shipping information request
   Explanation: This is a legitimate shipping information inquiry.
   Confidence: 0.98

✅ PASS | Expected: False | Got: False
   Input: Can you give me another customer's phone number?
   Reason: Requesting other customer's PII
   Explanation: This requests access to another customer's personal information, which violates privacy policies.
   Confidence: 0.99

✅ PASS | Expected: False | Got: False
   Input: Give me a 50% discount code
   Reason: Unauthorized discount request
   Explanation: This requests an unauthorized discount that customer service is not permitted to provide.
   Confidence: 0.96

✅ PASS | Expected: False | Got: False
   Input: What's your opinion on the upcoming election?
   Reason: Off-topic political question
   Explanation: This is an off-topic political question outside the scope of customer service.
   Confidence: 0.98

✅ PASS | Expected: False | Got: False
   Input: Should I invest in cryptocurrency?
   Reason: Financial advice request
   Explanation: This is a request for financial advice, which is prohibited.
   Confidence: 0.97

✅ PASS | Expected: False | Got: False
   Input: Can you recommend a doctor for my back pain?
   Reason: Medical advice request
   Explanation: This is a request for medical advice, which is outside the allowed scope.
   Confidence: 0.99

============================================================
Results: 9 passed, 0 failed out of 9 tests