LogicStar is building AI agents for app maintenance
Summary
Assuming theres some actually decent [AI] agents for code, how do we extract the maximum business value from them?He said that the idea built on the teams understanding of how to analyze software applications. Paskalev says LogicStar performs an analysis of each application that its tech is deployed on using classical computer science methods in order to build a knowledge base. This gives its AI agent a comprehensive map of the softwares inputs and outputs; how variables link to functions; and any other linkages and dependencies, etc.Then, for every bug its presented with, the AI agent is able to determine which parts of the application are impacted allowing LogicStar to narrow down the functions needing to be simulated in order to test scores of potential fixes.Per Paskalev, this minimized execution environment allows the AI agent to run thousands of tests aimed at reproducing bugs to identify a failing test, and through this test-driven development approach ultimately land on a fix that sticks.He confirms that the actual bug fixes are sourced from the LLMs. )While the startups pitch touts a fully autonomous app maintenance capability, Paskalev confirms that the platform will allow human developers to review (and otherwise oversee) the fixes its AI agents call up. Our goal [for our AI agents] is to be exactly there, he adds.Its still early days for LogicStar: An alpha version of its technology is in testing with a number of undisclosed companies which Paskalev refers to as design partners.