We built TestSuiteBench to measure whether coding agents can turn a specification into a test suite that catches bugs. Their tests are valid but weak: the best agent catches 55% of injected bugs, while implementations built from the same specifications pass up to 93% of the reference tests.

Why automating the most visible part of the job did not automate the largest one: where the bottleneck moved, and how to tell whether your agents are actually getting better.

Field crews use the app offline, on site, where a crash means a lost record. How LogicStar found the bugs that mattered, including an authentication vulnerability, and turned production noise into root-cause fixes.

When your product's promise is that local behaves like production, a parity gap is a broken promise. How LogicStar finds the correctness gaps hand-written tests miss and ships them as merged fixes.

Coding agents can write code. But can they decide what actually matters in large software systems? We explore why the next generation of AI software tools must move beyond patch generation toward architectural judgment.

We scale distillation of Agentic Coding Capabilities efficiently, to train a family of best-in-class coding models.

LogicStar Autonomously Finds and Fixes A Real Bug in Our Production Code

AI coding agents accelerate development but also drive up complexity and technical debt, causing early productivity gains to fade. Self-Healing Software closes this gap by automatically detecting and fixing issues as fast as new code is generated. LogicStar provides this capability, keeping codebases healthy and velocity sustainable.

We optimized the OCI layer structure of code execution environments to improve storage and distribution at scale

The L* Agent achieves a new state-of-the-art of 84% on SWT-Bench Verified

LogicStar AI Successfully Completes SOC 2 Audit

LogicStar AI Joins the ETH AI Center as an Affiliate! 🚀

BaxBench: Can LLMs Generate Secure and Correct Backends?

A TechCrunch article about us titled LogicStar is building AI agents for app maintenance

SWT-Bench Benchmarking CodeAgents' Test Generation Capabilities

INSAIT and ETH Zurich Entrepreneurs launch LogicStar AI, a new Agentic AI startup

A Benchmark for Testing and Validating Bugfixes

LogicStar, building the AI agent for fully autonomous application maintenance, raised a $3m round led by Northzone.

We are looking for passionate software engineers to join our team

We are excited to announce the launch of LogicStar AI, our startup to revolutionize application monitoring.