I Built an AI Support Agent That Never Repeats a Failed Fix Using Hindsight
ResolveIQ is a support workflow built around that distinction. It combines ordinary operational records in PostgreSQL, a language model for drafting replies, and Hindsight for customer-level memory across conversations. The through-line is simple: a proposed fix is not a resolution. We need to know what was tried, whether it worked, and what the next response should avoid. A support agent can…
ResolveIQ is a support workflow that employs an AI support agent capable of learning from previous interactions. The system combines traditional data storage, language models, and a specialized memory component called Hindsight to enhance the support experience. The agent's main goal is to ensure that proposed fixes are not repeated, as previous attempts should be recorded and their outcomes noted.
The AI system uses a React and TypeScript frontend, a FastAPI backend, and PostgreSQL database to manage customer, ticket, conversation, message, resolution attempts, and escalation data. The AI agent utilizes Hindsight to maintain customer memories, allowing it to recall relevant context during conversations, making responses more informed and personalized.
The design separates responsibilities between PostgreSQL and Hindsight, with each component managing distinct aspects of the support workflow. This architecture allows the AI agent to retrieve and utilize customer memories, as well as maintain a structured record of attempted solutions, which aids in generating appropriate responses while avoiding repetition of failed fixes.
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