Scaling AI Systems

Introduction

Scaling agents means handling concurrent users, long runs and bursty traffic without melting your wallet or your provider rate limits.

Beginner analogy: growing from a food truck to a chain — the recipe is the same; logistics change.

Understanding the topic

Core concepts:

• Horizontal scale of workers via queues.
• Per-user / per-tenant rate limiting.
• Provider concurrency caps — request increases early.
• Sticky sessions for stateful agents.
• Cache and dedupe identical requests.

Syntax reference

Visual workflow / architecture:

Users ─► API ─► Queue ─► Workers (autoscale) ─► LLM
                       │                        │
                       ▼                        ▼
                  Rate limiter           Provider caps

Real-world use

OpenAI / Anthropic both have org-level rate limits — large customers request raises ahead of launches.

Best practices

• Plan provider rate limit raises 2-4 weeks ahead.
• Dedupe identical queries with a tiny cache.

Common mistakes

• Launching to 100K users without contacting your model provider.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. Two layers of rate limiting.
• Q2. Why dedupe identical requests?
• Q3. Scenario: a viral spike 10x'd traffic. Three actions in the first hour?