Kubernetes

Kubernetes is the foundation of our AI agent infrastructure, providing container orchestration, auto-scaling, and service management for our autonomous systems. It's the platform that enables our serverless AI agents to scale efficiently.

Why Kubernetes is essential for AI agents:

• Container Orchestration: Manages complex multi-agent deployments with service discovery
• Auto-Scaling: Handles variable AI workloads from zero to high-demand automatically
• Resource Management: Efficient GPU and CPU allocation for LLM processing
• High Availability: Ensures agent systems remain available with rolling updates
• Multi-Tenancy: Isolates different agent workflows and customer environments

AI-specific capabilities:

• GPU Scheduling: Native support for GPU resources needed for local LLM inference
• Job Management: Batch processing for training and fine-tuning AI models
• Service Mesh Integration: Works with Istio for secure agent-to-agent communication
• Custom Resources: Extensible for AI-specific resources like model servers
• Horizontal Pod Autoscaling: Scales based on custom metrics like token throughput

Integration with our platform:

• EKS Foundation: Our production clusters run on AWS EKS with optimized node groups
• Karpenter: Just-in-time node provisioning for cost-effective AI workloads
• Knative: Serverless layer on top of Kubernetes for scale-to-zero agents
• ArgoCD: GitOps deployment of agent configurations and models
• External Secrets: Secure management of API keys and model credentials

Best practices for AI workloads:

• Use resource quotas and limits for predictable AI agent performance
• Implement pod disruption budgets for critical agent services
• Leverage node affinity for GPU-intensive workloads
• Use persistent volumes for model storage and caching
• Monitor resource usage with custom metrics for LLM token consumption