EKS Agent Platform: Architecture Analysis of a Kubernetes-based Multi-tenant AI Agent Platform

eks-agent-platform is a Kubernetes-based cloud-native multi-tenant AI agent platform. It implements multi-tenant management via CRD, integrates AWS Bedrock, KEDA auto-scaling, and Argo Workflows, addressing challenges such as multi-tenant isolation, cost control, and operational complexity in enterprise AI agent deployment, and provides a complete AI agent deployment and governance solution for enterprises.

With the rapid development of LLMs and AI agents, enterprises face three core challenges in deployment:

1. Multi-tenant Isolation: Need to provide independent runtime environments for different teams/projects, ensuring data isolation, resource quota, and security;
2. Cost Control: Cloud-based LLM API usage easily leads to cost overruns, with a lack of effective budget control;
3. Operational Complexity: Need to integrate multiple tech stacks such as IAM permissions, key management, scaling, and workflow orchestration, making component integration difficult. This project aims to solve these issues and provide a Kubernetes-native solution.

The project adopts a "Platform-as-a-Platform" design philosophy, building an AI agent runtime environment based on Amazon EKS. The core idea is to abstract the agent lifecycle management into Kubernetes resources and achieve automated operations via declarative configuration. Key features:

• Fully cloud-native: Based on Kubernetes and AWS native services, leveraging EKS elasticity and reliability;
• Multi-tenant design: Implement workload isolation and resource quota management via Tenant CR;
• Cost-controllable: Built-in budget熔断 mechanism to prevent cost overruns;
• Automated operations: Integrate KEDA for scaling and Argo Workflows for complex workflow orchestration.

1. Tenant CR and Multi-tenant Management

When creating a Tenant, it automatically configures: independent IAM roles, KMS keys (for encrypting sensitive data), and S3 buckets (for storing agent data/logs), achieving cross-tenant isolation.

2. agentctl Tool

A command-line tool for agent lifecycle management: register workloads, configure parameters, monitor status, trigger deployment/update/rollback, with operations converted into Kubernetes resource updates.

3. agentgateway Gateway

A unified API entry that provides traffic management (routing/load balancing), security control (authentication/authorization), and observability (metrics/log collection).

4. kagent Runtime

Supports LangChain/LlamaIndex frameworks, natively integrates AWS Bedrock for model calls, integrates with KEDA for elastic scaling, and has built-in health checks to ensure high availability.

KEDA Auto-scaling

Triggers scaling based on request queue depth, CPU/memory usage, and custom metrics (e.g., model latency), handling traffic peaks while saving resources.

Budget熔断 Mechanism

• Configure monthly/quarterly budgets for tenants;
• Real-time monitoring of Bedrock API costs and resource consumption;
• Automatically pause non-critical workloads when thresholds are exceeded;
• Send alerts via AWS SNS/Slack to prevent cost overruns.

Argo Workflows Evaluation Pipeline

Supports batch testing, A/B testing, data feedback collection, and CI/CD integration, facilitating continuous agent optimization.

Applicable Scenarios

1. Enterprise AI Middle Platform: Unify agent development and deployment capabilities, balancing governance and cost control;
2. Multi-team Collaboration: Different teams share infrastructure with data/resource isolation;
3. AI Application SaaSification: Create independent environments for customers to implement multi-tenant SaaS architecture.

Tech Stack Integration

Domain	Tech Component	Purpose
Container Orchestration	Amazon EKS	Managed Kubernetes service
Large Model Service	AWS Bedrock	Managed LLM API access
Auto Scaling	KEDA	Event-driven scaling
Workflow Engine	Argo Workflows	Evaluation pipeline orchestration
Key Management	AWS KMS	Data encryption and key rotation
Object Storage	Amazon S3	Data persistence
Identity Authentication	AWS IAM	Fine-grained permission control

eks-agent-platform is not just about running agents on Kubernetes; it provides an enterprise-level solution covering multi-tenant isolation, cost control, automated operations, and continuous evaluation. For teams exploring AI agent productionization, this project offers a reference architecture blueprint, demonstrating the desired form of a cloud-native AI platform: declarative, observable, cost-controllable, and easily extensible. As AI agent applications deepen, such infrastructure projects will help organizations enjoy AI capabilities while maintaining effective control over cost, security, and governance.