Branching and conditional logic in Agentic AI - Model Pipeline Trace

Model Pipeline - Branching and conditional logic

This pipeline shows how an AI agent uses branching and conditional logic to decide actions based on input data. It mimics how a person chooses different paths depending on conditions.

Data Flow - 4 Stages

1Input Data

1 sample x 3 features→Receive sensor readings (e.g., temperature, humidity, light)→1 sample x 3 features

[22.5, 55, 300]

↓

2Condition Check

1 sample x 3 features→Check if temperature > 25 and light < 200→1 sample x 1 boolean

False

↓

3Branching Decision

1 sample x 1 boolean→If True, choose action A; else choose action B→1 sample x 1 action label

"Action B"

↓

4Action Execution

1 sample x 1 action label→Perform the chosen action (e.g., turn on fan or light)→1 sample x 1 status

"Fan turned off"

Training Trace - Epoch by Epoch

Loss
0.5 |****
0.4 |***
0.3 |**
0.2 |*
0.1 | 
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.45	0.60	Initial model guesses actions with moderate accuracy.
2	0.30	0.75	Model learns to better separate conditions.
3	0.20	0.85	Clear improvement in decision making.
4	0.15	0.90	Model converges with high accuracy.
5	0.12	0.92	Final fine tuning of branching logic.

Prediction Trace - 4 Layers

Layer 1: Input Data

Layer 2: Condition Check

Layer 3: Branching Decision

Layer 4: Action Execution

Model Quiz - 3 Questions

What happens if the temperature is 20 and light is 250?

A Agent chooses Action A

B Agent chooses Action B

C Agent ignores the input

D Agent turns on both fan and light

Key Insight

Branching and conditional logic lets an AI agent make choices like a person deciding between options. Training helps the agent learn when to pick each action based on input data.