AI Agent Memory: Conversation vs Context

The article draws a clean line between conversation memory, which is semantic and lossy by design, and context memory, which is a precise reference to earlier state. The distinction matters when agents start behaving inconsistently and you cannot tell whether the problem is retrieval quality or context window management. The walkthrough uses Strands and AgentCore as the concrete implementation surface, so it is not purely theoretical. If you are building agents that need to track multi-step workflows without hallucinating earlier decisions, the framing here is genuinely useful scaffolding. The reservation is that this reads as vendor-adjacent content — the AWS affiliation is upfront — so the tool choices in the examples are not neutral recommendations. Take the conceptual model and adapt it to whatever memory layer you are already using. -> Best for: AI engineer building stateful agents who is debugging inconsistent memory retrieval