Single agent vs multi-agent systems in Agentic AI - Model Approaches Compared

Model Pipeline - Single agent vs multi-agent systems

This pipeline compares how a single agent learns and acts alone versus multiple agents learning and acting together in a shared environment.

Data Flow - 5 Stages

1Environment Setup

1 environment with 10 features→Initialize environment with state features→1 environment with 10 features

State features: [position_x=5, position_y=3, velocity=1, ...]

↓

2Agent Observation

1 environment with 10 features→Agent(s) observe environment state→Single agent: 1 observation vector (10 features); Multi-agent: 3 observation vectors (each 10 features)

Single agent observes [5,3,1,...]; Multi-agent each observe similar vectors

↓

3Action Selection

Observation vectors→Agent(s) select actions based on observations→Single agent: 1 action vector; Multi-agent: 3 action vectors

Single agent action: [move_right]; Multi-agent actions: [move_up, move_left, stay]

↓

4Environment Update

Actions from agent(s)→Environment updates state based on actions→Updated environment with 10 features

New state: [position_x=6, position_y=3, velocity=1, ...]

↓

5Reward Calculation

Updated environment state→Calculate reward(s) for agent(s)→Single agent: 1 reward scalar; Multi-agent: 3 reward scalars

Single agent reward: 1.0; Multi-agent rewards: [0.8, 1.2, 0.5]

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	0.3	Initial learning with high loss and low accuracy for both single and multi-agent
2	0.7	0.5	Loss decreases and accuracy improves as agents learn environment dynamics
3	0.5	0.65	Agents start to coordinate better in multi-agent system, improving performance
4	0.35	0.8	Significant improvement in multi-agent coordination; single agent also improves
5	0.25	0.9	Training converges with low loss and high accuracy; multi-agent system shows better overall performance

Prediction Trace - 4 Layers

Layer 1: Agent Observation

Layer 2: Action Selection

Layer 3: Environment Update

Layer 4: Reward Calculation

Model Quiz - 3 Questions

What is a key difference between single-agent and multi-agent systems in this pipeline?

A Single-agent systems always have higher accuracy

B Multi-agent systems have multiple observations and actions per step

C Multi-agent systems do not update the environment

D Single-agent systems use multiple rewards per step

Key Insight

Multi-agent systems can learn to coordinate actions and share information, leading to better performance than single-agent systems in complex environments.