What does it take to build a customer support experience your users won't hate? Ask Bitovi.

Bitovi has developed an AI agent designed to enhance customer support interactions by maintaining user context across sessions, utilizing the Temporal platform for its architecture. This agent addresses the common issue of chatbots failing to recognize returning users, requiring them to start from scratch each time. By leveraging AWS Bedrock AgentCore Memory, the system incorporates semantic memory, user preferences, and session summaries into its workflow, ensuring a more seamless and personalized support experience. The AI agent operates within a ReAct loop as a Temporal Workflow, with LLM inference running as isolated Activities to maintain deterministic workflow logic. This setup allows for long-running sessions without state limits, providing retry semantics and observability. By integrating these elements, Bitovi ensures that their AI agent can handle complex support interactions efficiently and effectively, while also being easy to iterate on due to the durable and scalable nature of Temporal.