Building Context-Aware AI Assistants with AgentCore and OpenClaw

A new approach to building personal AI assistants has emerged, one that prioritizes continuity over individual question-answering. Traditional off-the-shelf AI assistants excel at answering specific questions but struggle to retain context between conversations. This limitation can be frustrating for users who need to re-explain their situation or preferences each time they interact with the assistant.

The problem lies not in the quality of the answers themselves, but rather that the assistant lacks memory and fails to accumulate context over time. To address this issue, a team has developed a personal AI assistant using OpenClaw, an open-source agentic system, running on AgentCore runtime, a capability of Amazon Bedrock AgentCore. This setup enables the creation of durable knowledge from disposable chats.

The architecture is domain-agnostic and can be applied to various domains such as support bots, fitness coaches, or internal help desks. The entire system lives within a single AWS CloudFormation template, which deploys with one command and runs on a consumption-based model that costs only a few dollars per month for light personal use.

To build this assistant, you’ll need Amazon Bedrock AgentCore access, including the runtime and memory capabilities. You’ll also require model access granted for the models you plan to route to, such as Claude Haiku 4.5 for text or Claude Sonnet 4.5 for vision. Additionally, you’ll need Docker with linux/arm64 build support and the AWS Command Line Interface (AWS CLI) configured.

The architecture consists of several key components: AgentCore runtime, OpenClaw as the agent substrate, two models routed by task, skills as reusable capability units, Telegram as the serverless front door, memory turning disposable chats into durable knowledge, and namespaces isolating each user’s data.