Undo lands $37M to give AI agents the runtime context to fix bugs

The distinction between code and runtime behavior has always mattered, but it has become critical with the rise of AI-generated code. Traditional debugging tools focus on analyzing the source code itself, assuming developers understand what they wrote. However, AI coding assistants produce code faster than humans can comprehend, creating a trust gap. Developers and AI agents alike struggle to understand why such code fails when it does. Undo's approach flips the paradigm by making runtime behavior visible and reproducible, giving both humans and AI systems the context needed to solve problems efficiently.

Rather than relying on sparse logs or stack traces, Undo captures a deterministic record of program execution. This record acts as a time machine, allowing developers and AI agents to replay the program's behavior, inspect variables at any point in time, and trace exactly how a bug occurred. For complex issues buried in multithreaded applications or distributed systems, this capability is invaluable.

The performance lift from 38% to 92% accuracy demonstrates why runtime context is non-negotiable for effective AI-assisted debugging. Large language models can analyze code patterns and identify logical flaws, but they cannot infer what actually happened during execution without seeing the recorded state. By feeding AI agents Undo's deterministic recordings, teams transform the debugging process from educated guessing into precise root-cause identification. This shift has profound implications for code quality, incident response times, and operational stability.

Token usage reduction is another compelling benefit. Each request to an AI model consumes tokens, which directly translates to cost. When AI agents have access to accurate runtime data, they need fewer attempts and iterations to solve problems, reducing the total tokens consumed per debugging session. Combined with faster analysis times, this creates a compelling return on investment for enterprises managing large codebases with AI-generated components.

Enterprise validation from Palo Alto Networks carries significant weight. The company operates at massive scale with complex, mission-critical software. When Palo Alto Networks' engineering leadership publicly endorses Undo's solution, it signals that the technology addresses real, expensive problems in production environments. The statement that Undo catches bugs autonomously in minutes underscores how runtime recordings accelerate the entire debugging lifecycle, from detection to resolution.

Elsewhere Partners' track record demonstrates a focused strategy in growth-stage software. The firm's portfolio spans multiple geographies and sectors, suggesting Undo fits a proven thesis around infrastructure software that solves scaling problems. The choice to fund hiring in product, support, and sales indicates a balanced growth strategy: building better features, ensuring customers succeed, and reaching new markets simultaneously.

The timing of this funding is deliberate. As AI coding assistants proliferate across enterprises, the need for runtime visibility becomes more acute, not less. Undo's CEO positioned the investment as accelerating market capture at the inflection point where AI-generated code becomes the norm rather than the exception. This strategic timing may prove crucial in establishing Undo as the standard for AI-assisted debugging infrastructure.

Building deterministic recording technology is non-trivial. It requires deep understanding of operating systems, processor architectures, and language runtimes. Undo's foundation in 2005 gave the company nearly two decades to refine this approach before AI-assisted debugging became critical. This head start creates a technical moat that competitors cannot easily overcome. While application performance monitoring (APM) tools have proliferated, none offer the granular, reproducible execution context that Undo provides.

The rise of AI coding assistants has fundamentally changed software development dynamics. Tools like GitHub Copilot and Claude for engineering accelerate code production, but they also accelerate the introduction of subtle bugs that human developers might catch through traditional review. Undo's technology bridges this gap by making AI-generated code as inspectable and debuggable as hand-written code. Runtime recordings provide the transparency needed to trust AI-assisted development at scale, addressing one of the key concerns enterprises have about widespread AI code generation.

The execution challenge ahead is significant but well-aligned with Undo's funding plan. Scaling a developer infrastructure company requires strong go-to-market capability, responsive customer support, and continuous product innovation. The hiring focus on product, support, and sales teams suggests Undo understands these requirements. Success will depend on making runtime recording as seamless and accessible as traditional debugging tools while demonstrating clear ROI through reduced debugging time, faster AI model performance, and lower token costs.