Scaling agents horizontally in Agentic Ai - Model Metrics & Evaluation

Metrics & Evaluation - Scaling agents horizontally

Which metric matters for scaling agents horizontally and WHY

When scaling agents horizontally, the key metrics to watch are throughput and latency. Throughput measures how many tasks or requests the system can handle per second. Latency measures how fast each task is completed. These metrics matter because adding more agents should increase throughput without making latency worse. Also, resource utilization helps check if agents are efficiently used. Monitoring error rates ensures quality does not drop as you add agents.

Confusion matrix or equivalent visualization

Throughput and Latency Example:

| Number of Agents | Throughput (tasks/sec) | Latency (ms/task) |
|-----------------|------------------------|-------------------|
| 1               | 100                    | 50                |
| 2               | 190                    | 52                |
| 4               | 370                    | 55                |
| 8               | 720                    | 60                |

This table shows throughput nearly doubling as agents double, while latency slightly increases.

Error Rate Example:

| Number of Agents | Total Tasks | Errors | Error Rate (%) |
|-----------------|-------------|--------|----------------|
| 1               | 1000        | 5      | 0.5            |
| 4               | 4000        | 20     | 0.5            |
| 8               | 8000        | 40     | 0.5            |

Error rate stays stable, showing quality is maintained.
    

Precision vs Recall tradeoff analogy for scaling agents

Think of precision as the quality of each agent's work and recall as how many tasks get done. When scaling horizontally, you want to increase recall (more tasks done) without losing precision (quality). If you add many agents but quality drops, it means precision suffers. If you keep quality high but throughput stays low, recall is low. The tradeoff is balancing speed and quality as you add agents.

For example, a customer support system adding more chat agents should handle more chats (higher recall) but still give correct answers (high precision). If agents rush and make mistakes, precision drops.

What "good" vs "bad" metric values look like for scaling agents horizontally

Good:

• Throughput increases close to linearly with number of agents.
• Latency increases only slightly or stays stable.
• Error rate remains low and stable.
• Resource utilization is balanced (agents are busy but not overloaded).

Bad:

• Throughput plateaus or grows very slowly despite adding agents.
• Latency increases sharply, causing delays.
• Error rate rises, showing quality loss.
• Some agents are idle while others are overloaded.

Common pitfalls when measuring scaling metrics

• Ignoring latency: Only tracking throughput can hide delays that frustrate users.
• Resource contention: Adding agents without enough CPU or memory causes slowdowns.
• Data leakage: Sharing state incorrectly between agents can cause errors.
• Overfitting to test load: Optimizing for a specific workload but failing in real use.
• Not measuring error rates: High throughput with many errors is useless.

Self-check question

Your system has 98% accuracy but only 12% recall on fraud detection when scaling agents horizontally. Is it good for production? Why or why not?

Answer: No, it is not good. The low recall means the system misses 88% of fraud cases, which is dangerous. Even with high accuracy, missing most frauds is unacceptable. You need to improve recall before production.

Key Result

Throughput and latency are key metrics; good scaling means throughput grows with agents while latency and error rates stay low.