Dashboard design for agent monitoring in Agentic Ai - Model Pipeline Trace

Model Pipeline - Dashboard design for agent monitoring

This dashboard helps monitor an AI agent's performance by showing data flow, training progress, and prediction results in a clear, visual way.

Data Flow - 6 Stages

1Raw Data Input

1000 rows x 10 columns→Collect agent interaction logs and sensor data→1000 rows x 10 columns

Agent ID, Timestamp, Action, Sensor1, Sensor2, ..., Sensor7

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2Data Preprocessing

1000 rows x 10 columns→Clean missing values and normalize sensor readings→1000 rows x 10 columns

Normalized sensor values between 0 and 1

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3Feature Engineering

1000 rows x 10 columns→Create new features like action frequency and time since last action→1000 rows x 12 columns

Added columns: Action_Frequency, Time_Since_Last_Action

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4Model Training

800 rows x 12 columns→Train agent behavior prediction model→Model trained on 800 rows, 12 features

Training on 80% of data, 200 rows reserved for testing

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5Model Evaluation

200 rows x 12 columns→Test model and calculate accuracy and loss→Accuracy: 85%, Loss: 0.35

Model predicts next agent action with 85% accuracy

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6Prediction

1 row x 12 columns→Predict next action for a single agent instance→Predicted action probabilities

Action A: 0.7, Action B: 0.2, Action C: 0.1

Training Trace - Epoch by Epoch

Loss
0.9 |*       
0.8 | *      
0.7 |  *     
0.6 |   *    
0.5 |    *   
0.4 |     *  
0.3 |      * 
    +--------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	0.55	Model starts learning, accuracy just above random
2	0.65	0.68	Loss decreases, accuracy improves
3	0.50	0.75	Model gains better understanding of agent behavior
4	0.40	0.80	Training progressing well, accuracy rising
5	0.35	0.85	Model converges with good accuracy

Prediction Trace - 4 Layers

Layer 1: Input Layer

Layer 2: Hidden Layer (ReLU activation)

Layer 3: Output Layer (Softmax)

Layer 4: Prediction Result

Model Quiz - 3 Questions

What happens to the data shape after feature engineering?

A It increases in columns from 10 to 12

B It decreases in rows from 1000 to 800

C It stays the same shape

D It changes to 200 rows x 12 columns

Key Insight

This visualization shows how data flows from raw input through cleaning and feature creation, then trains a model that improves over time, finally predicting agent actions with probabilities. Watching loss decrease and accuracy increase confirms the model learns well.