Undo Raises $37M to Give AI Agents Something Most Debuggers Never Had: Context

Software debugging has always had an uncomfortable relationship with certainty. Developers write code. Systems grow. Logs accumulate. Something breaks. Then begins the ritual of trying to reconstruct what happened from a trail of clues that often resembles a witness statement written after three sleepless nights and two emergency production calls.

Undo, a Cambridge, UK-based AI-powered root cause analysis company, raised $37M in funding led by Elsewhere Partners to expand its runtime debugging platform and accelerate adoption across enterprise engineering teams. The company sits at the intersection of AI infrastructure, developer tools, observability, and enterprise software, four markets currently colliding as organizations race to operationalize AI. Undo believes the problem isn't a lack of intelligence. It's a lack of context.

The funding arrives at a moment when software development is accelerating faster than software understanding. AI can now generate code at remarkable speed, but the industry is discovering that creating software faster and understanding software faster are two very different challenges.

What Happened

Undo announced a $37M funding round led by Elsewhere Partners, marking one of the company's largest financing events since its founding in 2005. Founded by Co-Founder & CEO Dr. Greg Law and Co-Founder Julian Smith, Undo built its reputation around deterministic debugging technology often described as time-travel debugging. Instead of relying solely on logs, traces, and educated guesses, the platform records software execution and allows engineers to replay program behavior to identify the precise root cause of failures.

The company's latest positioning extends that concept into the age of Agentic AI. Undo now provides runtime context that AI systems can use to analyze software failures based on actual execution history rather than inference alone. According to company-reported benchmarks, AI root-cause accuracy improved from 38% to 92% when models were supplied with Undo runtime recordings, and the company reports that some investigations can be completed up to 100x faster when execution history is available.

The new capital will support hiring and expansion across the United States and Europe while helping embed Undo's technology more deeply into AI-assisted engineering workflows.

Why This Matters

The software industry is entering a strange phase. For years, engineering teams worried primarily about writing software. Now they're increasingly worried about understanding software. AI coding assistants have dramatically lowered the friction of code creation, resulting in more software, more dependencies, more complexity, and more opportunities for failures that are difficult to reproduce.

That creates a paradox: as software generation becomes easier, software diagnosis becomes harder. Undo is targeting that gap. Most debugging tools attempt to reconstruct events after something breaks, while Undo attempts to preserve the entire sequence of events before they disappear. Traditional observability tools tell you that something went wrong. Deterministic replay helps explain exactly why it went wrong. The distinction sounds subtle until a production issue costs millions of dollars, interrupts customers, or creates a security vulnerability nobody can reproduce.

For enterprise engineering organizations, certainty has become a valuable commodity.

Market Context

The rise of Enterprise AI and AI infrastructure is creating entirely new infrastructure categories. Most attention has focused on model providers, copilots, and AI application layers, while less attention has been paid to the systems required to help AI understand the environments where software actually runs.

This is where Undo's positioning becomes interesting. Large language models are remarkably effective at pattern recognition but far less effective when critical runtime information is missing. Logs rarely tell the entire story. Static code often lacks operational context. AI systems can make educated guesses, but educated guesses remain guesses. Undo's runtime recording technology gives AI systems something closer to direct observation.

That distinction aligns with a broader movement across enterprise infrastructure. Organizations increasingly want AI systems grounded in operational reality rather than probabilistic assumptions, and the companies building those foundational layers may become some of the most strategically important vendors in the AI stack.

Competitive Landscape

Undo operates within a growing ecosystem of developer tools, observability platforms, debugging technologies, and AI-assisted engineering solutions. Its differentiation comes from deterministic replay and runtime recording rather than traditional monitoring alone.

The company serves customers across networking, databases, semiconductors, financial services, cybersecurity, and enterprise software. Public references include Palo Alto Networks, while historical coverage has also cited SAP HANA, Cadence, Micro Focus, and Mentor Graphics, now part of Siemens Digital Industries Software.

These are environments where software failures are not minor inconveniences. They can affect revenue, infrastructure availability, customer trust, and operational continuity. In those environments, reducing uncertainty often matters more than increasing speed, creating a meaningful market opportunity for platforms focused on root cause analysis rather than simply detection.

What This Signals

The Undo funding round signals a broader shift in how investors are evaluating AI infrastructure. For the past several years, capital largely flowed toward systems that help create software faster. A growing portion of investment is now moving toward systems that help organizations manage the consequences of that acceleration.

The round also reflects growing investor interest in infrastructure layers that help enterprises govern and understand AI-generated software. Elsewhere Partners' investment reflects a view that the future software stack requires more than code generation. It requires systems that can explain, verify, diagnose, and audit increasingly complex environments.

The market is beginning to recognize that AI-generated software still needs accountability. Someone, or something, has to know why the system behaved the way it did.

The Bigger Industry Shift

The most important AI companies of the next decade may not be the ones generating the most output. They may be the ones providing the most reliable context.

As enterprises adopt agentic AI, software systems will increasingly make decisions, trigger actions, and modify code with varying degrees of autonomy. Organizations will demand visibility into those actions. Security teams will demand it. Engineering leaders will certainly demand it after the first major incident review.

That creates a growing market for technologies that transform software behavior from mystery into evidence. Undo's $37M funding round is ultimately a wager on a simple idea: understanding what happened may become just as valuable as creating what happens next.