The brief
SonarQube's Remediation Agent (SQRA) launched into open beta in early 2026 — and adoption was well below expectations. While the team was more focused on "why aren't people using it?", for me it was: “It’s free to try, why aren’t people activating it in the first place?”
What I did
I ran a structured investigation across three fronts:
- Funnel diagnosis: Took a 10% sample of TAM (N=1500 for Beta), worked with account managers to trace where outreach is failing, and analyzed beta marketing campaign data to evaluate product desirability.
- Direct customer outreach: Spoke with some customers from the sample who had access but hadn't activated to understand why.
- Competitive landscape: Mapped the remediation tool landscape across Wiz, Dependabot, CodeRabbit, Copilot, Sentry/Seer, Cursor/BugBot, and Devin to understand what we were being compared against and on what dimensions customers were making decisions.
What I found
Adoption wasn't low because of a single missing feature. The real problem was a stack of compounding friction:
- 12% of the sample from the TAM had accounts that were delinquent, about to churn, or don’t have projects that the remediation agent can support. This proved the targeting was too broad and there’s a need to revise TAM.
- Remediation agent offered three key features: 1. tackling issues on PR, 2. assigning issues from backlog to the agent and 3. automated backlog reduction. Customers I talked to, found the automated backlog reduction a more impactful use case than tackling issues on PR.
- Many eligible customers hadn't heard of SQRA or didn't understand it was available to them. This indicated lack of awareness and discoverability within the product and even through the beta marketing campaign.
- Positioning was unclear, internally and externally. Customers and Sales had the same questions. They needed clear documentation on why Sonar remediation agent was the right choice over what’s in the market.
- Customers mentioned they do not want to try out the agent without having a price estimate first, as it takes considerable effort on their end to go through internal review to enable a AI-powered feature. It’s about the missing cost-benefit analysis.
- The market context added urgency: the space was moving fast toward shift-left environments, iterative remediation, transparent reasoning, and tighter PR-workflow integration.
The decisions this enabled
- Shifted the internal conversation from "why is usage low?" to a more specific and actionable question: “who are the right customers, and what conditions need to be in place before they'll convert?”
- The findings enabled the entire squad (Product, UX and Engineering) to agree that we need to focus our upcoming efforts on what resonated most with users (i.e., automated backlog reduction) and focus less on PR workflow.
- Built a live GitHub-hosted resource using Claude that gave Sales a reliable, always-current answer to the most common questions raised during customer interactions.
- Established an evidence base for revising the TAM and success criteria as we prepare for General Availability of the product.