Undo has introduced a new AI capability for developers designed to support coding agents by providing runtime context. The aim is to help address complex programming issues by giving agents access to execution information that can be difficult to infer from source code alone.
For many years, engineers working with large codebases have faced challenges when tracing complex issues. Traditional AI coding agents, including tools that primarily rely on static code analysis, may not fully capture dynamic runtime behaviour.
Undo AI combines static code analysis with information from program execution. Instead of relying only on source code, it uses execution records so AI systems can better understand what happens during runtime and why certain events occur.
- Root cause analysis support: Assists in identifying causes of persistent production issues, including long-standing bugs.
- Test failure assistance: Can help address failing tests by using execution context to guide fixes.
- Data flow tracing: Helps track how data moves through systems to identify where incorrect values may originate.
- Issue diagnosis: Supports investigation of issues such as intermittent failures and memory-related problems.
- Bug triage support: Enables analysis of recorded execution sessions, even when developers were not present at the time.
The capability is designed to work with existing coding agents, including platforms such as Claude Code, Codex, and GitHub Copilot. Integration is provided through the Undo MCP server, where execution recordings act as structured snapshots of program behaviour that can be inspected by developers and AI tools.
In contexts where AI is increasingly used to generate production code, the system is positioned as a way to provide additional visibility into how that code behaves during execution, allowing developers to review and analyse outputs more directly.
Overall, Undo AI introduces a method of combining execution-level data with coding agents to support debugging and software analysis tasks in complex development environments.
