Customer support agent architecture in Agentic Ai - Model Pipeline Trace

Model Pipeline - Customer support agent architecture

This pipeline builds a customer support agent that understands questions, finds answers, and replies helpfully. It processes chat data, learns from it, and improves its answers over time.

Data Flow - 5 Stages

1Raw chat data input

10000 rows x 2 columns→Collect customer questions and agent replies→10000 rows x 2 columns

[{'question': 'How to reset password?', 'answer': 'Click on forgot password link.'}]

↓

2Text preprocessing

10000 rows x 2 columns→Lowercase, remove punctuation, tokenize text→10000 rows x 2 columns

[{'question': ['how', 'to', 'reset', 'password'], 'answer': ['click', 'on', 'forgot', 'password', 'link']}]

↓

3Feature engineering

10000 rows x 2 columns→Convert tokens to word embeddings (vectors)→10000 rows x 2 columns x 50 features

[{'question': [[0.1,0.2,...], ...], 'answer': [[0.3,0.1,...], ...]}]

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4Train/test split

10000 rows x 2 columns x 50 features→Split data into 8000 training and 2000 testing samples→Train: 8000 rows x 2 columns x 50 features, Test: 2000 rows x 2 columns x 50 features

Training sample example: question vector and answer vector pairs

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5Model training

8000 rows x 2 columns x 50 features→Train sequence-to-sequence neural network to map questions to answers→Trained model

Model learns to generate answer vectors from question vectors

Training Trace - Epoch by Epoch

Loss
1.2 |****
0.9 |***
0.7 |**
0.55|*
0.45| 
     +----
     Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Model starts learning basic patterns
2	0.9	0.60	Loss decreases, accuracy improves
3	0.7	0.72	Model captures common question-answer pairs
4	0.55	0.80	Better understanding of language context
5	0.45	0.85	Model converges with good accuracy

Prediction Trace - 4 Layers

Layer 1: Input question vector

Layer 2: Encoder neural network

Layer 3: Decoder neural network

Layer 4: Output text generation

Model Quiz - 3 Questions

What happens during the text preprocessing stage?

A Data is split into training and testing sets

B Text is cleaned and split into words

C Model predicts answers

D Vectors are converted back to words

Key Insight

This visualization shows how a customer support agent learns to understand questions and generate helpful answers by transforming text data into vectors, training a neural network, and improving accuracy over time.