Method for Developing a Context-Aware AI Assistant on Amazon Web Services
Details are provided on how to build personal AI assistants that maintain context on AWS using OpenClaw and AgentCore memory components.
A new guide shared by Amazon Web Services explains how to build a personal assistant that retains context as memory using the AgentCore runtime and the OpenClaw system, addressing the lack of conversation continuity in standard AI assistants.
Architecture Ensuring Continuity
Although off-the-shelf AI assistants successfully answer individual questions, they fall short in continuity because every conversation starts from scratch. Eliminating the burden on users to re-explain context, this new architectural approach aims to establish persistent knowledge accumulation.
Illustrated through an example gardening assistant named Sprout, this system offers a flexible structure that can be adapted to different domains such as support bots, fitness coaches, or internal help desks via a single AWS CloudFormation template.
Technological Infrastructure and Security
Among its architectural components are Telegram webhooks integrated via Amazon API Gateway and Lambda functions, Amazon EventBridge Scheduler for scheduled tasks, and the AgentCore runtime.
For the system's data security and storage requirements, encryption operations are managed by AWS KMS, secret key management by AWS Secrets Manager, and workspace storage areas by Amazon S3.
Multi-Model and Skill Management
The system routes text chats and visual understanding tasks to different Claude models located on Amazon Bedrock. Claude Haiku 4.5 is preferred for fast and cost-effective text processing.
For visual tasks requiring stronger multi-modal reasoning, such as diagnosing plant photos, the Claude Sonnet 4.5 model is used, and capabilities are declared through a community manifest.
Advanced Memory Management
The AgentCore memory system provides short-term memory that records each conversation turn as an event, along with long-term memory structures asynchronously generated by managed inference strategies.
Records are filed in namespaces specific to each user to maintain strict user isolation, and the most relevant preferences are included and injected into the system prompt.