Your AI Agent Calls an API 10,000 Times per Second. How Would You Handle It? | GenAI Interview Prep

Title Your AI Agent Calls an API 10,000 Times per Second. How Would You Handle It? | GenAI Interview Prep Description Your AI agent is calling an API 10,000 times per second. How would you handle it? This is an important GenAI system design interview question. In this video, we answer it step by step with simple visuals, so you understand the ideas instead of memorizing them. The main idea is simple: we should not try to make the API handle unlimited traffic. Instead, we control the traffic before it reaches the API. Every API has a limit, called its capacity. If the API can handle 500 requests per second and we send 10,000, we get 429 Too Many Requests errors, timeouts, and in the worst case, the API goes down. When the API goes down, our agent stops working too. First, we understand the bottleneck and reduce the traffic with caching, deduplication, batching, and a maximum number of agent steps. Then we go through eight techniques. Rate limiting controls how many requests start per second, with the token bucket algorithm and limits at global, per-tenant, per-user, and token level. Queueing turns sudden bursts into a steady flow, and workers process the jobs at a controlled rate. Concurrency limiting controls how many requests run at the same time. We show why rate limiting alone is not enough for slow LLM calls, with a simple restaurant example. Backpressure slows down the producer when the queue gets full. Smart retries use exponential backoff, jitter, and the Retry-After header, and avoid retry storms. A circuit breaker stops calls to an API that keeps failing. Load distribution spreads traffic across more capacity. Idempotency keys make retries safe, so a customer is never charged twice. Finally, we put everything together into one architecture, add monitoring, compare all the techniques side by side, and give a short, interview-ready answer. ⏱️ Chapters 0:00 The interview question 0:18 The simple idea 0:34 What goes wrong? 1:04 Step 1: Understand the bottleneck 1:34 Step 2: Reduce the traffic first 2:09 Technique 1: Rate limiting (token bucket) 2:46 Rate limiting at different levels 3:18 Technique 2: Queueing 3:43 Workers at a controlled rate 4:07 Technique 3: Concurrency limiting 4:38 Why rate limiting alone is not enough 5:57 Technique 4: Backpressure 6:35 Retry storms 6:59 Technique 5: Backoff, jitter, and Retry-After 7:40 Technique 6: Circuit breaker 8:18 Technique 7: Load distribution 8:58 Technique 8: Idempotency 9:40 The full architecture 10:26 Monitor everything 10:49 Side-by-side comparison 11:25 The interview answer 13:00 Wrap-up #GenAI #AIAgents #SystemDesign #RateLimiting #LLM #AgenticAI #BackendEngineering #InterviewPrep #ArtificialIntelligence