New research from Undo, the technology that gives developers the runtime context needed to solve the most challenging problems in the most complex codebases, finds that almost four in five (79%) engineering leaders say their release cycles are no faster than before, despite their teams being able to produce code more easily than at any time in their careers.
As AI agents have increased the volume of code they can create, engineers now spend nearly twice as long debugging it as they do writing it, averaging 16.9 hours a week. That accounts for 42% of the average working week. Engineers are simply unable to keep up with their agents, leading to more than a third (35%) of AI-generated code reaching production before they’ve fully comprehended it.
Adding to the risk, AI agents frequently hallucinate the cause of failures, or fail to identify problems in the codebase entirely. In the past six months, as a result of their use of AI coding tools:
- 81% of organizations have had a production incident or service outage affecting internal users or customers
- 93% have had the root cause of an issue incorrectly diagnosed because of an AI hallucination
- 91% have had test escapes, serious defects or poorly optimized code enter production
Four in five (80%) engineering leaders say coding agents struggle to solve difficult problems in large-scale, complex codebases. The arrival of more powerful models doesn’t offer a realistic solution, with a strong degree of cynicism about the impact the planned IPOs of Anthropic and OpenAI will have on AI affordability. The majority (82%) of engineering leaders think the costs of coding agents will go ‘through the roof’ as the AI labs prioritize making Wall Street happy.
However, engineering leaders widely agree that improving model context is more important than increasing their capability to make AI more powerful. More than four in five (82%) say AI agents would be far more useful for code comprehension and debugging if they were grounded in the context of what happened during runtime.
To learn more, download the full Overcoming the limitations of coding agents in complex software systems report here: https://undo.io/research-report-2026
Methodology
The research was conducted on behalf of Undo by independent research firm Coleman Parkes during July and August 2026. It surveyed 300 senior engineering leaders at organizations with revenues of $250m or above that deliver mission-critical software built on large, complex codebases, 93% of whom work with C/C++. Respondents were based in the United States (200) and the UK (100), across financial services, networking, semiconductor design, computational software and data management.
Producing code has never been easier, but AI-generated bugs and rising debugging workloads are slowing software delivery
Posted in Commentary with tags Undo on September 28, 2026 by itnerdNew research from Undo, the technology that gives developers the runtime context needed to solve the most challenging problems in the most complex codebases, finds that almost four in five (79%) engineering leaders say their release cycles are no faster than before, despite their teams being able to produce code more easily than at any time in their careers.
As AI agents have increased the volume of code they can create, engineers now spend nearly twice as long debugging it as they do writing it, averaging 16.9 hours a week. That accounts for 42% of the average working week. Engineers are simply unable to keep up with their agents, leading to more than a third (35%) of AI-generated code reaching production before they’ve fully comprehended it.
Adding to the risk, AI agents frequently hallucinate the cause of failures, or fail to identify problems in the codebase entirely. In the past six months, as a result of their use of AI coding tools:
Four in five (80%) engineering leaders say coding agents struggle to solve difficult problems in large-scale, complex codebases. The arrival of more powerful models doesn’t offer a realistic solution, with a strong degree of cynicism about the impact the planned IPOs of Anthropic and OpenAI will have on AI affordability. The majority (82%) of engineering leaders think the costs of coding agents will go ‘through the roof’ as the AI labs prioritize making Wall Street happy.
However, engineering leaders widely agree that improving model context is more important than increasing their capability to make AI more powerful. More than four in five (82%) say AI agents would be far more useful for code comprehension and debugging if they were grounded in the context of what happened during runtime.
To learn more, download the full Overcoming the limitations of coding agents in complex software systems report here: https://undo.io/research-report-2026
Methodology
The research was conducted on behalf of Undo by independent research firm Coleman Parkes during July and August 2026. It surveyed 300 senior engineering leaders at organizations with revenues of $250m or above that deliver mission-critical software built on large, complex codebases, 93% of whom work with C/C++. Respondents were based in the United States (200) and the UK (100), across financial services, networking, semiconductor design, computational software and data management.
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