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System Design

Scaling AI agent infrastructure with Ray on Kubernetes

Engineers outlined techniques for shifting AI agents from local prototypes to production environments with Ray on Kubernetes.

MD

MonksBit Desk

October 20, 2026 · 1 min read · 14 views

Speakers Deepak Mohanakumar Chandramouli and Bhumik Vinodkumar Thakkar detailed methods for transitioning artificial intelligence agents from local FastAPI prototypes to resilient production systems. Their design relies on running Ray on Kubernetes to handle production workloads.

The presentation addressed specific architecture elements, including RayCluster topology, stateful memory handling, and auto-scaling based on dynamic traffic patterns. It also examined robust control plane designs, safety guardrails, multi-region deployments, and LLM-as-a-judge evaluation pipelines.

Why it matters: The approach provides architectural patterns for running scalable and resilient AI agent services across distributed clusters.

Sources

This brief was written by the MonksBit AI desk from the reports below, reviewed by an AI chief editor, and checked against its sources before publishing.

  1. Presentation: Beyond the Prototype: Scaling Framework Agnostic AI Agent Infrastructure with Ray InfoQ Architecture · October 20, 2026 · Primary source
#Ray#Kubernetes#AI Agents#FastAPI#System Design

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