Model Pipeline - State graphs and transitions
This pipeline shows how an agent uses a state graph to decide actions. The agent moves between states based on transitions, learning which paths lead to success.
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State graphs and transitions in Agentic AI - Model Pipeline Trace
Data Flow - 5 Stages
1Initial States
1 graph with 5 states→Define states and possible transitions→Graph with 5 nodes and edges
States: S0, S1, S2, S3, S4; Transitions: S0->S1, S1->S2, S2->S3, S3->S4, S4->S0
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2Agent Observes Current State
Current state S0→Agent reads current state from graph→State vector representing S0
Agent sees it is in state S0
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3Transition Decision
State vector S0→Agent chooses next state based on learned policy→Next state S1
Agent decides to move from S0 to S1
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4State Update
Current state S0, next state S1→Agent updates its state to S1→Agent now in state S1
Agent moves to state S1
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5Reward Feedback
Transition S0->S1→Agent receives reward signal→Reward value scalar
Agent gets reward 0.5 for moving to S1
Training Trace - Epoch by Epoch
Loss
1.0 |**********
0.8 |********
0.6 |******
0.4 |****
0.2 |**
0.0 |*
1 2 3 4 5 Epochs
| Epoch | Loss ↓ | Accuracy ↑ | Observation |
|---|---|---|---|
| 1 | 0.9 | 0.2 | Agent starts with random transitions, low accuracy |
| 2 | 0.7 | 0.4 | Agent learns better transitions, accuracy improves |
| 3 | 0.5 | 0.6 | Agent refines policy, loss decreases steadily |
| 4 | 0.3 | 0.8 | Agent approaches optimal transitions |
| 5 | 0.15 | 0.95 | Agent achieves high accuracy, low loss |
Prediction Trace - 5 Layers
Layer 1: Input State Encoding
Layer 2: Policy Network
Layer 3: Action Selection
Layer 4: State Transition
Layer 5: Reward Reception
Model Quiz - 3 Questions
Test your understanding
What does the agent use to decide the next state?
Key Insight
State graphs help agents learn which transitions lead to better outcomes. Encoding states as vectors and using a policy network allows the agent to predict and choose the best next state, improving over training by reducing loss and increasing accuracy.