Helios Retail publishes a suite of point of sale management software. Its technical desk faced the problem every software vendor knows: engineers re-diagnosing incidents that have already been solved dozens of times, with the knowledge locked inside old tickets, phone calls and the memory of a few experts.
Three agents, three uses
Three specialised agents were put into production on a common technical base.
The resolution agent first judges whether the ticket contains enough information to work with, and if not, generates the clarifying questions to ask. It then searches in parallel through similar resolved tickets, fixes already scheduled and the documentation, and drafts a proposal that can be acted on, with a confidence score and its sources. The engineer stays in the loop: the agent proposes, the person decides.
The guided diagnosis agent offers an interactive mode. It prescribes a check to run, interprets the engineer’s answer, including screenshots, and refines its hypothesis.
The self-service agent, on the end user side, answers questions about how to use the product, staying strictly grounded in the product documentation, filtered by retail brand.
Anti-hallucination as an architectural principle
An agent that invents a table name is worse than useless. The answer was to index the product’s real code, over five hundred thousand lines of SQL and around five thousand classes, combining exact, full-text and semantic search. Search accuracy, measured internally, went from roughly sixty per cent to ninety-five, and the agent reasons about the real names of tables, procedures and screens.
Two further mechanisms complete the system. Detection of already-fixed incidents compares the version installed at the customer site against scheduled fixes and flags the cases where the answer is simply to update. And a feedback loop adds to the knowledge base every time an engineer validates a suggestion.