Summary Points
- Initial optimism with async: The team believed Python’s async would handle many concurrent LLM agent calls efficiently, but encountered increasing latency and event loop lag as more agents were added.
- Hidden CPU bottleneck: Even with asynchronous I/O, small CPU tasks after each LLM response accumulated and competed for CPU resources, causing system slowdowns and timeouts.
- Limitations of pure async architecture: Scaling issues weren’t due to I/O constraints alone but the bottleneck created by serial CPU-bound tasks, which don’t scale with async or multiple cores alone.
- Effective solution: Distributing workload across multiple processes/servers prevented latency from increasing with new agents, highlighting that system design must prioritize workload distribution for true scalability.
Understanding the Initial Expectations
When deploying multiple AI agents, it’s natural to assume that running them concurrently will keep response times low. After all, most tasks here are I/O-bound, meaning they wait for external responses. Using asynchronous programming should make all agents run swiftly at once. When tested in a controlled environment, responses seem quick, and the system appears scalable. However, as more agents join the system, response times begin to slow unexpectedly. This pattern suggests there is more at play than just the expected I/O delays, leading engineers to look deeper into what actually limits performance.
Discovering the Hidden Bottleneck
Soon, developers notice event loop lag warnings, which show the system struggles to handle many concurrent tasks. Even with optimizations—like speeding up JSON processing or increasing connection limits—the response times still increase with more agents. To understand why, simplified tests simulate many agents performing virtually identical tasks. These tests reveal a common pattern: the system cannot process hundreds or thousands of quick CPU tasks simultaneously. Although each task seems tiny, their combined load exceeds the system’s capacity, creating a bottleneck. In reality, the challenge isn’t just the speed of individual components; it’s how the total workload distributes across the hardware.
Rethinking System Design for Better Scalability
The core issue is that CPU work does not scale like I/O operations. While asynchronous I/O helps handle many requests, CPU-bound tasks—like processing responses or validating data—accumulate at a critical point. This piling up overburdens a single process or event loop, slowing everything down. The fix involves spreading the workload across multiple processes, services, or machines. By dividing tasks, each CPU core handles only part of the work. This approach ensures response times stay steady as more agents are added. The lesson extends beyond Python: designing systems that split workloads prevents bottlenecks and enables continuous growth. Good engineering practices focus on how tasks are organized, not just how efficiently they’re written. Sometimes, the key to scalability is stepping back and reconsidering the entire system’s architecture to meet future demands.
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