Agentic AI Platform
The Agentic AI Platform is a unified platform that enables autonomous AI agents to perform complex tasks. Deploying a single monolithic LLM for mission-critical enterprise workloads has clear limitations in terms of cost, latency, accuracy (hallucination), and governance. Organizations must transition to a heterogeneous multi-model ecosystem where LLMs handle complex reasoning while domain-specific SLMs handle repetitive tasks — and platform-level infrastructure is the key to operating this efficiently. Kubernetes is rapidly expanding AI-native capabilities such as DRA, Gateway API Inference Extension, and Kueue, and this platform supports multi-model switching without code changes on top of the K8s ecosystem.
This documentation series guides you through understanding the platform architecture, identifying the 5 key challenges faced during deployment, and addressing them through two approaches: AWS Native managed services and EKS-based open architecture. These two approaches are complementary, and we recommend a gradual journey starting with AWS Native and expanding to EKS as needed.
Documentation Structure
Platform Building Path: Design & Architecture → Model Serving & Inference Infrastructure → Operations & Governance → Reference Architecture
GenAI Application Development Path: Model Serving (vLLM) → Distributed Inference (llm-d) → Gateway (Inference Gateway) → RAG (Milvus) → Agent (Kagent) → Evaluation (Ragas)
Related Categories
- AIDLC — AI Development Lifecycle and AgenticOps
- Hybrid Infrastructure — AI deployment in hybrid environments
- EKS Best Practices — EKS operational best practices