Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
Data Science
Towards Data Science·Jul 23·5 min read
Why Adding More AI Agents Made Our System Slower
TL;DR
Adding more AI agents to a system can unexpectedly increase latency due to bottlenecks in request handling. Planck discovered that simultaneous requests to multiple agents led to slower response times, despite quick completions from LLM providers.
✦ Why It Matters
Evaluate your system architecture to prevent bottlenecks when scaling AI agents.
Key Takeaways
- Profile your system to identify bottlenecks in CPU-bound tasks.
- Optimize serialization methods to improve performance.
- Distribute workloads across multiple processes to enhance scalability.
- Avoid relying on a single event loop for high concurrency.
How It Works
The architecture was redesigned to fan out requests across multiple workers, each with its own event loop and connection pool. This allowed for better distribution of CPU tasks, preventing bottlenecks from accumulating in a single process.