Scaling AI Agents: From Prototype to 1 Million Requests per Day

The Scaling Challenge

AI agent scaling requires architecture designed for AI workloads from the start. Key constraints: per-key token rate limits, 2-30 second response latency, high per-request cost, and non-idempotent operations.

Core Architecture: Queue Plus Workers

Client sends tasks to a Redis queue. Workers pull tasks, acquire a semaphore to limit concurrency, call Claude, and store results with TTL. Dead letter queue captures tasks that exhaust retries.

import asyncio, anthropic, json, time
from redis.asyncio import Redis

client = anthropic.AsyncAnthropic()
redis = Redis(host="localhost", port=6379)

async def process_task(task):
    try:
        resp = await client.messages.create(
            model=task.get("model", "claude-sonnet-4-6"),
            max_tokens=2048,
            messages=[{"role": "user", "content": task["prompt"]}]
        )
        result = {"status": "completed", "output": resp.content[0].text,
                  "tokens": resp.usage.input_tokens + resp.usage.output_tokens}
    except anthropic.RateLimitError:
        task["retries"] = task.get("retries", 0) + 1
        if task["retries"] < 5:
            await asyncio.sleep(2 ** task["retries"])
            await redis.lpush("tasks", json.dumps(task))
        return
    await redis.setex(f"result:{task["id"]}", 3600, json.dumps(result))

Cost Management

• Semantic caching (SHA256 of prompt): 30% cache hit rate saves thousands monthly
• Route simple tasks to Haiku: 60-70% cost reduction
• Track token usage per task type to identify optimization opportunities

Key Metrics

Monitor: queue depth (leading indicator), P99 latency, RateLimitError rate, cache hit rate, dead letter queue size. At 1M requests/day with Sonnet (avg 800 tokens): ~,400/day. With 30% cache hits and Haiku routing: ~00/day.