Building a context-aware AI assistant on AgentCore and OpenClaw

A system is presented for building context-aware AI assistants by combining AgentCore, OpenClaw, and Amazon Bedrock AgentCore memory to provide long-term conversational context. The architecture allows agents to accumulate knowledge through event creation and structured extraction strategies, enabling the assistant to remember past details across sessions. Personalization is achieved by storing user-specific memories in isolated namespaces tied to a chat ID. The process involves retrieving long-term records based on a current query, assembling them with explicit preferences and inferred facts, and injecting this context into the system prompt for model responses. This architecture routes tasks, using Claude Haiku for fast text and Claude Sonnet for vision tasks, and incorporates cost-saving measures like prompt caching to manage inference costs.

The pattern demonstrated is the externalization of state management from the stateless nature of large language models into a structured, auditable memory layer managed by an orchestration framework. The efficacy hinges not just on the retrieval mechanism but on the deliberate structure applied to memory—specifically creating clear namespaces and using indexed metadata for filtering—which enforces cognitive boundaries between different user contexts. The cost-saving mechanism via prompt caching highlights a critical tension in agentic design: balancing the need for rich, personalized context with the necessity of keeping inference costs low. Furthermore, routing models based on task (Haiku vs. Sonnet) is an example of architectural segmentation that acknowledges varying computational needs, suggesting that optimizing performance requires matching tool/model selection to the complexity of the required reasoning, rather than applying a single monolithic approach. The design guidelines suggest that true personalization emerges from systematic structure rather than emergent behavior; this implies that building reliable, personalized systems relies on disciplined pre-definition of memory boundaries and prompt formatting before allowing the model to perform its core task.