Agent architecture (observe, think, act) in Prompt Engineering / GenAI - Model Pipeline Trace

Model Pipeline - Agent architecture (observe, think, act)

This agent architecture shows how an AI system works by observing its environment, thinking about what it sees, and then acting based on that thinking. It repeats this cycle to learn and improve.

Data Flow - 3 Stages

1Observe

1 environment state snapshot→Agent senses or receives data from environment→1 observation vector

Image pixels or sensor readings like temperature and position

↓

2Think

1 observation vector→Agent processes observation to decide next action→1 action decision vector

Neural network outputs probabilities for possible moves

↓

3Act

1 action decision vector→Agent performs action in environment→Environment changes state

Robot moves forward or AI clicks a button

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	0.85	0.40	Agent starts learning, loss is high, accuracy low
2	0.65	0.55	Agent improves by better thinking and acting
3	0.45	0.70	Agent learns to act more correctly
4	0.30	0.85	Agent's decisions become more accurate
5	0.20	0.92	Agent converges to good performance

Prediction Trace - 3 Layers

Layer 1: Observe

Layer 2: Think

Layer 3: Act

Model Quiz - 3 Questions

What is the first step the agent takes in this architecture?

A Observe the environment

B Think about the action

C Act on the environment

D Train the model

Key Insight

This agent architecture shows a simple but powerful cycle: first sensing the world, then deciding what to do, and finally acting. Training improves the agent's decisions by reducing mistakes over time.