Production incidents cost customer trust, engineering time, and momentum. AI-accelerated development is making it worse, more code, shipped faster, more issues slipping past existing tools. LogicStar finds them early and surfaces the ones that matter, before they become incidents.
Try against your repo
LogicStar builds a deep understanding of your codebase and correlates it with your observability, support, and engineering systems, surfacing the issues that matter, with root cause and remediation. 5-min setup, first results within the hour.
LogicStar turns them into a clear priority of what to fix next.
Which bugs affect customers, which ones threaten revenue, and what to fix next.
A P1 in dead code doesn't matter. A P3 in your highest-revenue checkout flow does. LogicStar ranks every defect by ARR at risk and the customers it affects, so your team fixes what moves the business, not what sounds urgent.
Bugs don't start as incidents. They start as warnings nobody had time to investigate. LogicStar cuts through the noise and proposes a validated fix.
I could just generate small PRs 12 hours a day that fix issues that just don't really matter. So, really focusing, the system only has so much capacity to absorb change, both from a code review standpoint and from a just risk management of pushing code to production standpoint.
Tom Kleinpeter
If there's a critical 500 error in your top service, you fix it immediately, no debate needed. A system that produces production-ready PRs someone just merges? That's 100% a must.
Lazar Kanelov
What I see here, it's clear separation with known bugs and not known bugs, which you found. And I think not known bugs probably even more important because known bugs probably already somehow prioritized in our queue.
Iunir Iakupov
the correlation aspect where you're reading from like other sources as opposed to just the codebase. That is also like an awesome addition I would say
Sandeep Sripada
Proven on real-world systems, we publish the leading benchmarks for AI coding agents. That same expertise drives our internal evaluations, so LogicStar keeps getting better as models evolve.
validating tests generated
LogicStar reproduces every bug with a failing test that proves it's real and validates fixes actually resolve them. State-of-the-art performance on SWT-Bench Verified.
overestimation of success rate in SWE-Bench Verified
Many AI coding agents overfit to a single benchmark. We automatically create new benchmarks for every use-case and show popular code agents lose up to 60% of performance on an application focused benchmark of 366 diverse codebases.
of working AI-generated code is exploitable
Even frontier models produce exploitable backends. Across 392 tasks, one in three working solutions contains SQL injection, path traversal, or code injection vulnerabilities.
cost increase, zero performance gain
Over 60,000 repos include AGENTS.md files to guide AI agents. Our evaluation shows these files reduce success rates by up to 3% while adding 20% to inference costs.
of AI refactoring attempts break code
AI agents solve only 22% of multi-file refactoring tasks and introduce breakage in 63% of attempts. CodeTaste measures whether AI restructures code the way a senior engineer would.
LogicStar shows the bugs impacting customers and revenue, ranked and ready to act on.
No workflow changes. Results in ~1 hour.
