Beyond Session Memory: Building Agents That Track Work, Not Conversations

Most AI agents treat chat sessions as their primary unit of memory, which breaks down when users switch channels or need to resume work across multiple interactions. By separating task identity from execution identity, developers can build systems that understand continuous work rather than fragmented conversations. This architectural shift becomes critical as agents move from demos to production environments where multi-channel support and collaborative workflows are essential.