Retry and fallback logic in Agentic AI - Model Pipeline Trace

Model Pipeline - Retry and fallback logic

This pipeline shows how an AI agent tries to complete a task by retrying when it fails and using a fallback method if retries don't work.

Data Flow - 5 Stages

1Input Task

1 task request→Receive a task to perform→1 task request

Task: Classify an image

↓

2Primary Model Attempt

1 task request→Try to complete the task using the main AI model→1 prediction or failure signal

Model predicts 'cat' or returns error

↓

3Retry Logic

1 failure signal→Retry the primary model up to 3 times if it fails→1 prediction or failure after retries

Retries: 3 times, still no prediction

↓

4Fallback Method

1 failure after retries→Use a simpler fallback model or rule-based method→1 fallback prediction

Fallback predicts 'dog'

↓

5Final Output

1 prediction from primary or fallback→Return the final prediction→1 prediction

Final prediction: 'dog'

Training Trace - Epoch by Epoch

Loss
0.8 |****
0.6 |*** 
0.4 |**  
     1  2  3 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.8	0.5	Model starts learning, accuracy low
2	0.6	0.65	Accuracy improves with training
3	0.4	0.8	Model converges well

Prediction Trace - 5 Layers

Layer 1: Primary Model Prediction

Layer 2: Retry Attempt 1

Layer 3: Retry Attempt 2

Layer 4: Retry Attempt 3

Layer 5: Fallback Method

Model Quiz - 3 Questions

What happens if the primary model fails to predict confidently?

A The system immediately uses the fallback method

B The system retries the primary model up to 3 times

C The system stops and returns no prediction

D The system increases the input data size

Key Insight

Retry and fallback logic helps AI systems be more reliable by trying multiple times and using simpler methods if needed, improving overall prediction success.