Rovo Trust Blockers: Data Cleanliness & Historical Data — Has Anyone Solved These?

[Dikla Tavor-Haimpur]

These two questions are currently acting as trust blockers for a number of our customers' Rovo adoption decisions. Any clarity from the Atlassian team or experienced community members would be greatly appreciated.

Question 1: Intelligent Data Relevance Filtering in Rovo

Many of our customers are evaluating Rovo's ability to work with real-world, imperfect data environments. A recurring concern is the data cleanup prerequisite before Rovo can deliver meaningful results.

Does Rovo have — or is there a roadmap for — an adaptive learning mechanism that can progressively identify and filter out irrelevant or low-quality data? Ideally, this would reduce the manual cleanup burden over time, allowing Rovo to become more accurate and useful as it's exposed to an organization's data patterns. We're seeing competing AI platforms position this as a key differentiator, so understanding Rovo's stance here would be very valuable.

Question 2: Historical Sprint Data Access via Teamwork Graph

This is a critical question for customers looking to use Rovo agents for agile team analytics.

Does the Teamwork Graph retain and expose historical sprint data in a way that Rovo agents can query and visualize? As a concrete example: if a Rovo agent is configured to manage sprint operations, can it generate a bar chart showing the number of issues that remained open across the last five sprints? Understanding the depth and accessibility of historical data available to Rovo agents would directly impact our customers' confidence in using Rovo as a reliable source of truth for retrospective analysis and reporting.