November 21, 2025 / 1 min read
Modern engineering teams face a sharp rise in code written by AI tools, yet the rate of software failures continues to grow. Backlogs expand, incidents slip through, and valuable engineering time is burned on maintenance instead of product development.
LogicStar changes this dynamic with fully validated, autonomous bug fixing.
In this short walk-through, our co-founder Mark Müller shows a real example from our own services. The LogicStar Agent detects a tricky concurrency bug, reproduces it in our sandbox, evaluates candidate fixes, validates the correct one with targeted tests, and ships a merge-ready pull request.
Watch the demo video:
“Interested in Self-Healing Software? Check out this walk-through where I demonstrate how the LogicStar Agent finds, reproduces, and fixes a tricky concurrency issue in our codebase, providing me with a merge-ready and well-tested PR.”
Our team now sees several such fixes every day, all generated before we even open our laptops. As one comment noted:
“It is amazing that we are getting a couple of such bug fixes every day and 95 percent of the work is done before we even log in in the morning.”
The result is simple. Faster recovery, fewer regressions, and engineering teams that can finally focus on shipping meaningful product improvements instead of combing through incidents.
If you want to explore what self-healing applications can unlock for your team, get in touch for a trial.
Blog
A coding agent can turn a ticket into a green pull request in minutes. We split the merge decision into narrow checks, let fixed rules decide, set the strictness by the blast radius of a change, and bring in a person only where the agent gets stuck.
We ran three coding agents through four gateways to see what a gateway controls, what it measures correctly and where it misleads you, from budgets that overshoot to telemetry that leaks prompts and data residency set hop by hop.
We compared a stronger model, a different agent, answers from the author and a read-only investigation before any code. On 19 fresh tickets, the investigation went ahead on 9 of 10 wanted changes and held back 7 of 9 requests an author had already declined.
With the team behind the first AI for code.
Backed by frontier coding agent research with ETH Zurich.
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