New AI Research Could Fix Enterprise Software's Biggest Frustration
Researchers developed a proof-of-concept showing how a new AI training method called Reinforcement Learning with Verifiable Rewards (RLVR) could make enterprise software tools work more reliably by training AI directly in the target environment instead of just predicting the next word. This could mean fewer silent errors and smoother workflows in business applications like Jira and Confluence.
A new proof-of-concept study on arXiv from researchers (including those affiliated with Atlassian) shows how a novel AI training method called Reinforcement Learning with Verifiable Rewards (RLVR) could improve how AI agents interact with enterprise software. Unlike traditional LLMs, which are trained simply to predict the next token, RLVR trains an AI to properly use specific software APIs by testing it directly in the target environment and rewarding correct sequences of tool calls.
This matters because current AI assistants often fail silently in business software: they drop required fields, hallucinate non-existent tools, or stop after a single read-only action. The paper addresses this 'objective mismatch' — LLMs are optimized for text prediction, not for taking the right actions in a specific API. As a proof of concept, the team built a suite of five synthetic Atlassian workflows (such as those involving Jira and Confluence) to demonstrate that RLVR can teach an AI to hit the right endpoint with the right nested arguments in the right order.
If you use enterprise software like Jira or Confluence, keep an eye out for updates from Atlassian. In the meantime, you can read the full research paper on arXiv: https://arxiv.org/abs/2607.01465.