Scaling agents horizontally in Agentic Ai - Model Pipeline Trace

Model Pipeline - Scaling agents horizontally

This pipeline shows how multiple AI agents work together by adding more agents side-by-side to handle more tasks at once. This is called horizontal scaling, which helps the system work faster and smarter by sharing the workload.

Data Flow - 4 Stages

1Initial task input

1000 tasks→Receive tasks to be processed→1000 tasks

A list of 1000 customer support requests

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2Distribute tasks to agents

1000 tasks→Split tasks evenly among 5 agents→5 agents each with 200 tasks

Agent 1 gets tasks 1-200, Agent 2 gets 201-400, etc.

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3Agents process tasks in parallel

5 agents x 200 tasks each→Each agent processes its tasks independently→5 agents x 200 processed results

Agent 3 completes sentiment analysis on its 200 tasks

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4Collect processed results

5 agents x 200 processed results→Combine all results into one output→1000 processed results

All agents' outputs merged into a single report

Training Trace - Epoch by Epoch

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

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.45	0.60	Initial training with 1 agent, moderate accuracy
2	0.38	0.68	Loss decreased, accuracy improved as agent learns
3	0.32	0.74	Better performance, model converging
4	0.28	0.78	Continued improvement with stable training
5	0.25	0.81	Final epoch shows good accuracy and low loss

Prediction Trace - 3 Layers

Layer 1: Task distribution

Layer 2: Agent processing

Layer 3: Result aggregation

Model Quiz - 3 Questions

What happens to the number of tasks each agent handles when you add more agents?

A Each agent handles the same number of tasks

B Each agent handles more tasks

C Each agent handles fewer tasks

D Tasks are not divided among agents

Key Insight

Scaling agents horizontally allows the system to handle more tasks at the same time by splitting work across multiple agents. This parallel work speeds up processing and improves overall system efficiency.