AI Infra · Personal Agent Platform

AI Infrastructure for a Consumer Personal Agent

An integrated platform for user understanding, long-term memory, proactive recommendations, EKS GPU scheduling, model routing, and accelerated SGLang inference.

Amazon EKSSGLangGPU AutoscalingModel RoutingAurora & OpenSearch

Try the interactive demo

Solution Case

AI Infrastructure for a Consumer Personal Agent

Solution overview

A personal agent that understands users from day one

We designed and built the complete AI infrastructure for a consumer personal-agent product. Consented user data, preferences, historical memory, and live behavior become retrievable context, reducing the need for repetitive complex prompts and enabling proactive recommendations. EKS CPU pods host agent orchestration, profiles, memory, the tool gateway, and notifications. A policy router selects small, medium, private in-VPC, embedding, or safety models based on complexity, privacy, and cost. GPU node groups elastically serve private LLMs, embedding models, and rerankers.

The platform reduces interaction effort while preserving privacy boundaries, and controls latency and compute cost through elastic GPUs, tiered model routing, and inference acceleration.

EKS HybridCPU / GPU orchestration

SGLangHigh-throughput inference

Privacy RouteSensitive data stays in VPC

Implementation flow

A closed loop across context, routing, and elastic inference

Understand the user

Hydrate permission-aware context from profile, memory, and events without indiscriminately attaching personal data.

Route by policy

Select small, medium, private, embedding, or safety models by complexity, privacy, latency, and cost.

Scale inference

Autoscale GPUs using queue depth, token budgets, and memory pressure; reduce cold starts with warm pools, quantization, and model parallelism.

Act proactively

Accelerate generation with SGLang prefix caching, RadixAttention, and continuous batching, then trigger preference-aware recommendations through events.

Interactive experience

Live AI infrastructure data flow

Choose a request scenario and watch profile hydration, data retrieval, model routing, EKS GPU scaling, and SGLang streaming inference.

Personal Agent AI PlatformEKS · VPC Private · Multi-model Runtime

EKS Cluster Healthy

Request scenarioCombine profile and long-term memory for a personalized travel suggestion

GPU nodes2 / 8warm pool

GPU memory34%18.4 / 54 GB

Throughput—SGLang runtime

P95 latency—waiting

Consumer Agent RequestProactive recommendation

preference: travelmemory: 128prompt: 8 tokens

context

EKS CPU PodAgent service mesh

Agent OrchestratorProfile ServiceMemory ServiceTool GatewayProactive Notification

policy

Model policy routingPolicy Router

Small ModelclassificationMedium ModelconversationPrivate LLMVPC sensitiveEmbeddingretrievalSafety Modelguardrail

schedule

GPU Node GroupElastic inference cluster

GPU-01GPU-02GPU-03GPU-04

GPU warm pool ready

Private LLMEmbedding ModelReranker

SGLang RuntimeRadixAttentionPrefix CacheContinuous Batching

User understanding and memory data planeConsent, profile, vector memory, cache, and event streams

Aurora PostgreSQLOpenSearch / pgvectorElastiCache RedisAmazon S3SQS / EventBridge

Architecture simulation: metrics and scheduling events are generated in the browser; no cloud resources are created.