December 5, 2024 / 1 min read
Researchers and entrepreneurs from INSAIT and ETH Zurich have launched LogicStar AI, a new deep-tech startup, which is building fully autonomous agentic AI that helps teams maintain their software.
The founding team behind LogicStar AI is star-studded and includes the founders of DeepCode.ai (now Snyk Code) which is currently delivering more than $100M ARR for Snyk. The LogicStar AI founders are:
LogicStar AI Mission
LogicStar AI is building an agentic AI platform for automatically validating, reproducing, and fixing bugs with high precision. Their technology empowers engineering teams to focus on creating new features, driving growth and innovation, by reducing the burden of maintenance and debugging issues. With LogicStar AI, developers can thus spend more of their time delivering real business value, while LogicStar AI reliably addresses software maintenance problems without manual intervention.
INSAIT’s mission
INSAIT (https://insait.ai) is a world-class computer science and AI research institution, founded in 2022 in partnership with Switzerland’s ETH Zurich and EPFL. The focus of INSAIT is on conducting world-class research and attracting outstanding faculty, research scientists, postdocs, and PhD students. In the short time since its inception, INSAIT has published over 50 papers in all major AI venues, as well as in premier theory conferences.
Join the Journey
As LogicStar AI embarks on this exciting new chapter, we invite talented individuals to join our team and shape the future of reliable AI for code and software applications. For more information on career opportunities, partnerships, or to learn about our innovative solutions, please visit our website and follow LogicStar on LinkedIn. LogicStar AI has offices in both Sofia, Bulgaria and Zurich, Switzerland.
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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