Undo has enhanced its platform to allow artificial intelligence agents direct access to complete application execution recordings through a Model Context Protocol (MCP) server. This development enables AI tools to diagnose the root causes of software issues much faster than traditional methods, which could take weeks or months to resolve.
The platform captures every detail of program execution, including instructions, variables, thread events, and system calls. This comprehensive data captures causality far beyond what log analytics or tracing can reveal, providing AI agents with a more accurate understanding of how applications behave in real time. By querying these recordings, AI can pinpoint the origins of intermittent failures and state-dependent bugs, especially in complex environments involving multiple threads and processes.
This capability is particularly crucial as AI-generated code surges, overwhelming developers who spend more time reviewing unfamiliar code than writing it. AI agents equipped with full execution context can effectively review and troubleshoot code they did not generate, bridging a critical knowledge gap.
Mitch Ashley from The Futurum Group emphasized that the real barrier to autonomous debugging is accessing true runtime states—not just static code or logs. Undo’s complete execution records provide definitive runtime evidence that AI agents need to diagnose problems accurately, rather than relying on guesswork.
Development teams face a choice between embedding AI agents within their code-generation tools or employing independent AI reviewers. Regardless, scaling code review to match the pace of AI-generated code will depend heavily on leveraging AI agents empowered by deterministic runtime data like that provided by Undo.