Why coding agents arent delivering the promised gains for engineering yet

Date:
Wed, 04 Nov 2026 15:16:07 +0000

Description:
AI writes code in minutes, but engineers are struggling to understand and fix it. The knock-on effects are starting to show..

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter Vibe coding agents enable engineers to create code in a fraction of the time. This has shifted the bottleneck to later in the development process: when engineers have to understand what that code is doing, track down bugs, and investigate incidents.

As agents generate ever greater volumes of code, its becoming increasingly difficult for engineers to understand their output. Dr. Greg Law Social Links Navigation

Greg is the co-founder and CTO of Undo. It takes time to work out what their code does and how it interacts with the systems around it. Latest Videos From TechRadar Watch full video here:

To realize the productivity gains, AI must be applied to the later stages of the cycle, not simply code generation. When it comes to large-scale complex code bases, agents struggle. A critical issue in critical industries Nowhere is the pain of code comprehension and debugging more acute than in organizations powering the digital economy from financial services and semiconductor design to the networks and databases that keep everything connected. You may like The visibility gap that's smuggling risk into AI code Why AI coding is widening the software security gap AI agents are great at writing new code but then the human bottleneck slows everything down

In these sectors, one escaped defect can have serious consequences a trading system returning a bad value, a database returning the wrong row, or a
network failure leading to a service blackout. The UKs national air traffic controller found itself an unfortunate case in point when it revealed that
its recent service outage, which caused 2,000 flight cancellations and days
of disruption, was triggered by a legacy software defect.

As AI continues to increase the volume of code engineers can produce, the challenge is no longer about increasing the speed at which they work. Rather, engineering leaders must ensure their teams can still understand what theyve built when something goes wrong or behaves unexpectedly and act before a problem becomes a much bigger one. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me with news
and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors By submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over. The code comprehension gap AI can produce code in minutes. However, that speed comes
at a cost. As AI-generated code accumulates, duplicated logic and layers of change are built on top of one another, creating a kind of spaghettification of the codebase. It then becomes harder for engineers to understand how their code is behaving, let alone know where to start untangling it when something goes wrong.

The problem gets worse as codebases become more complex. Nine in ten engineering leaders say code comprehension and debugging take longer in complex environments such as C/C++ codebases. Engineers were already struggling to keep up when they wrote every line themselves.

Now, with AI being far less capable at debugging complex code than it is at creating it engineers find themselves buried in an avalanche of review work that is beyond human ability. As a result, defects can more easily escape
into production, with no clear path back to their root cause. What to read next 'Faster code is not the same as better software delivered sooner': New report claims AI isn't solving productivity across the entire software development lifecycle - yet 'Whatever you try to fix breaks things even
more': How to avoid the hidden dangers of AI code AI coding is putting software risk on steroids

The productivity gains are failing to add up. Despite being able to write
code more easily, 79% of engineering leaders say their release cycles are no faster. Its a vicious cycle. The faster AI generates code, the more engineers have to review and understand and the harder that becomes as complexity builds. Without solving the comprehension problem, increasing the speed of code generation risks simply moving the bottleneck further downstream. The limits of AI for debugging The obvious answer if humans are struggling to comprehend and debug code is for coding agents to take over. However, without the right inputs, even the most advanced AI tools struggle to deliver
accurate answers.

Coding agents debugging complex codebases often work from static context such as source code, logs and documentation . This tells them what the code is supposed to do, but not necessarily what it actually did at runtime. Agents are left to fill in the gaps and guess at what happened and why.

That lack of context creates a particular problem when the issue is buried in the complexity of a live system. AI agents can produce an answer that looks plausible, but without the evidence to support it, there is no guarantee that the diagnosis is correct. In fact, 93% of engineering organizations have had the root cause of an issue incorrectly diagnosed because of an AI hallucination. As a result, a third (37%) of teams only use AI agents for comprehension and debugging straightforward codebases.

The consequences extend beyond simply failing to solve the problem. When an agent cannot identify the root cause, it can resort to trial and error
adding more logs, rerunning the code and trying another approach. Agents end up stuck in a loop, consuming budgeted AI tokens without getting any closer
to an answer. Better evidence, not bigger models This raises a broader question for engineering leaders: how do you make AI genuinely useful for the difficult parts of software engineering, rather than simply using it to generate more code?

The way to get better results from AI agents isnt necessarily to use a more powerful model, but to give them better evidence. Giving an agent a record of what actually happened when code ran helps it understand how an issue occurred, making it better equipped to trace problems to their root cause and support engineers with debugging.

Ultimately, AI can only be as useful as the information it has to work with. Give agents a clear picture of how software behaved, and they can take on
more of the difficult work that still sits with human engineers today. That means faster answers, fewer dead ends and, most importantly, greater understanding of the software teams are responsible for. Turning AI speed
into engineering value The future of AI-powered development isnt just about generating more code, but understanding what it actually does. Give agents access to the right information, with high-quality context, and they can move from plausible answers to accurate automated root cause analysis.

That gives engineers something far more valuable than speed alone the confidence to let AI tackle the hardest problems without losing control of
the software they build. We've reviewed and ranked the best laptops for programming: Top picks for professional programmers, coders, software engineers, and developers. This article was produced as part of TechRadar Pro Perspectives , our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit



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