Customer support agent architecture in Agentic AI - ML Experiment: Train & Evaluate

Experiment - Customer support agent architecture

Problem:Build a customer support agent that can understand user questions and provide accurate answers. The current model uses a simple keyword matching approach.

Current Metrics:Training accuracy: 95%, Validation accuracy: 65%, Validation loss: 1.2

Issue:The model overfits the training data and performs poorly on new customer queries, showing low validation accuracy.

Your Task

Reduce overfitting and improve validation accuracy to at least 85% while keeping training accuracy below 92%.

Do not change the dataset.

Keep the model architecture based on transformer encoder layers.

Use only standard agentic AI libraries and tools.

Hint 1

Hint 2

Hint 3

Hint 4

Solution

Agentic AI

import agentic_ai
from agentic_ai.models import TransformerEncoder
from agentic_ai.training import Trainer, EarlyStopping

# Define the improved customer support agent model
class CustomerSupportAgent:
    def __init__(self):
        self.model = TransformerEncoder(
            num_layers=4,
            d_model=128,
            num_heads=4,
            dropout_rate=0.3,
            use_layer_norm=True
        )

    def train(self, X_train, y_train, X_val, y_val):
        trainer = Trainer(
            model=self.model,
            optimizer='adam',
            learning_rate=0.0005,
            loss='cross_entropy',
            metrics=['accuracy'],
            callbacks=[EarlyStopping(monitor='val_loss', patience=3)]
        )
        history = trainer.fit(
            X_train, y_train,
            validation_data=(X_val, y_val),
            epochs=30,
            batch_size=64
        )
        return history

# Example usage (assuming data is preprocessed and loaded):
# agent = CustomerSupportAgent()
# history = agent.train(X_train, y_train, X_val, y_val)

Added dropout with rate 0.3 to reduce overfitting.

Increased number of transformer encoder layers to 4 for better feature extraction.

Enabled layer normalization for stable training.

Reduced learning rate to 0.0005 for smoother convergence.

Implemented early stopping to prevent over-training.

Results Interpretation

Before: Training accuracy 95%, Validation accuracy 65%, Validation loss 1.2

After: Training accuracy 90%, Validation accuracy 87%, Validation loss 0.6

Adding dropout, layer normalization, and early stopping helped reduce overfitting, improving validation accuracy while slightly lowering training accuracy. This shows how regularization and training control improve model generalization.

Bonus Experiment

Try using a pre-trained language model as the base encoder and fine-tune it for customer support queries to further improve accuracy.

💡 Hint

Use transfer learning with a pre-trained transformer like BERT or GPT and fine-tune on your dataset with a small learning rate.

Practice

1. What is the main purpose of a customer support agent architecture in AI?

easy

A. To design websites for online shopping

B. To automatically understand and answer customer questions

C. To store customer payment information securely

D. To create marketing advertisements

Solution

1. Step 1: Understand the role of customer support agents

   Customer support agents in AI are designed to help customers by answering their questions automatically.

2. Step 2: Identify the main goal of the architecture

   The architecture is built to understand questions and provide answers without human help.

3. Final Answer:

   To automatically understand and answer customer questions -> Option B

4. Quick Check:

   Purpose = automatic answering [OK]

Hint: Focus on what the system does for customers [OK]

Common Mistakes:

• Confusing support agent with website design
• Thinking it stores payment info
• Mixing marketing tasks with support

2. Which component is essential in a customer support agent architecture to understand user questions?

easy

A. Language understanding module

B. User interface design

C. Payment processor

D. Answer generator

Solution

1. Step 1: Identify components in support agent architecture

   Key parts include understanding questions, finding answers, and responding.

2. Step 2: Find which part understands questions

   The language understanding module processes and interprets user input.

3. Final Answer:

   Language understanding module -> Option A

4. Quick Check:

   Understanding = language module [OK]

Hint: Look for the part that reads and interprets questions [OK]

Common Mistakes:

• Choosing answer generator which creates replies, not understanding
• Confusing payment processor with language understanding
• Picking user interface which is just display

3. Consider this simple keyword matching code snippet for a support agent:

def respond(question):
    if 'refund' in question.lower():
        return 'Please provide your order ID for refund.'
    elif 'shipping' in question.lower():
        return 'Shipping takes 3-5 business days.'
    else:
        return 'Can you please clarify your question?'

print(respond('How long is shipping?'))

What will this code print?

medium

A. Shipping takes 3-5 business days.

B. Please provide your order ID for refund.

C. Can you please clarify your question?

D. SyntaxError

Solution

1. Step 1: Analyze the input question

   The question is 'How long is shipping?'. The code checks if 'refund' or 'shipping' is in the question.

2. Step 2: Check keyword matching

   'shipping' is found in the question (case-insensitive), so the second condition is true.

3. Final Answer:

   Shipping takes 3-5 business days. -> Option A

4. Quick Check:

   Keyword 'shipping' triggers answer B [OK]

Hint: Match keywords in question text to conditions [OK]

Common Mistakes:

• Ignoring case sensitivity
• Choosing default else response
• Thinking code has syntax errors

4. This code is part of a customer support agent:

def answer_question(text):
    if 'price' in text:
        return 'Our prices start at $10.'
    elif 'delivery' in text:
        return 'Delivery takes 5 days.'
    else
        return 'Sorry, I did not understand.'

What is the error in this code?

medium

A. Using 'in' operator incorrectly

B. Incorrect indentation of return statements

C. Function missing return type

D. Missing colon after else statement

Solution

1. Step 1: Check syntax of if-elif-else statements

   Python requires a colon ':' after else to mark the block start.

2. Step 2: Identify missing colon

   The else line lacks a colon, causing a syntax error.

3. Final Answer:

   Missing colon after else statement -> Option D

4. Quick Check:

   Syntax error = missing colon [OK]

Hint: Look for missing colons after control statements [OK]

Common Mistakes:

• Thinking indentation is wrong
• Believing 'in' operator is incorrect here
• Confusing Python with typed languages

5. You want to improve a customer support agent to handle questions about refunds, shipping, and product availability. Which architecture design is best?

hard

A. Only use a fixed list of canned responses without understanding

B. Use simple keyword matching for all questions

C. Combine language understanding with a knowledge base and response generator

D. Ignore user questions and provide a contact email only

Solution

1. Step 1: Consider limitations of simple keyword matching

   Keyword matching alone misses nuances and complex questions.

2. Step 2: Identify a robust architecture

   Combining language understanding, a knowledge base, and response generation allows smart, accurate answers.

3. Step 3: Evaluate other options

   Fixed canned responses or ignoring questions reduce usefulness and user satisfaction.

4. Final Answer:

   Combine language understanding with a knowledge base and response generator -> Option C

5. Quick Check:

   Best design = combined smart modules [OK]

Hint: Pick the option with smart understanding plus knowledge [OK]

Common Mistakes:

• Choosing only keyword matching
• Ignoring user questions
• Relying on fixed canned responses