#architecture

Self-improving feedback loops and advanced agent design patterns

Overall system architecture of a production-grade Agentic AI Platform — 6 runtime layers and 3 cross-cutting planes

Core architecture and technical challenges of Agentic AI platforms

Architecture design, technical challenges, and AWS Native and EKS-based implementation approaches for the Agentic AI Platform

Strategic selection guide for building Agentic AI platforms in AWS environments

Optimal node strategies for GPU workloads across EKS Auto Mode, Karpenter, MNG, and Hybrid Nodes

Architecture concepts, distributed deployment strategies, and performance optimization principles for Mixture of Experts models

EKS architecture overview for maximizing LLM Inference performance — starting point for vLLM, KV Cache-Aware Routing, Disaggregated Serving, LWS multi-node, and GPU autoscaling

Covers the six design decisions to finalize when adopting EKS Hybrid Nodes — hybrid connectivity, cluster topology, Pod CIDR exposure, CNI and routing, node authentication, and workload exposure — with decision criteria and inter-decision dependencies.

Covers EKS Hybrid Nodes concepts, how it works, key technical characteristics, and a guide to the six design decisions — connectivity, topology, Pod CIDR exposure, CNI, authentication, and workload exposure.

Bedrock + Neptune + OpenSearch + AgentCore + Cohere — specialized for ABO tree, subscriptions, multilingual operations

Bedrock + Neptune + OpenSearch + AgentCore + Cohere — a single architecture that delivers a working demo in 8 weeks

Bedrock + Neptune + OpenSearch + Kinesis (Live) + TimeStream (Delivery SLA)

Bedrock + Neptune + OpenSearch + AgentCore — multilingual + live FX

Bedrock + Neptune + OpenSearch + AgentCore + TimeStream (cold chain) + Glue (cross-BU ETL)