The next challenge for coding agents

Coding agents have eliminated the bottleneck of writing code, shifting the focus to the downstream challenges of software maintenance and production issue resolution. Developers now spend 60-70% of their time on these tasks, and AI agents like Claude Code and tools from Resolve AI are being applied to handle production problems more efficiently than humans. The next frontier for agentic AI is tackling the unbounded, complex nature of production environments.

On the first day of Software Engineering 101, you learned about the SDLC — the software development life cycle. You learned that there is a whole lot more to producing quality software than writing some code and deploying it to production. We’ve studied the SDLC every which way, and we know where and how time is spent within it. The coding is the most interesting part of the SDLC. It is one of a number of steps in the process, but it’s the part that takes up the biggest chunk of the SDLC schedule. Agentic coding has changed all of that. I wrote earlier this year about the impacts upstream of coding when writing code is no longer the bottleneck https://www.infoworld.com/article/4116373/when-writing-code-is-no-longer-the-bottleneck.html . Here I want to think about the impacts for developers downstream — what we can expect to happen after the code gets written — when agents write the code. Even before agentic coding, we developers spent far more time reading and maintaining code than we spent writing it. Maintaining the code and supporting the product has always been the major lifetime cost of the project as a whole. Now that the bottleneck has moved away from the coding process, we will look for ways to apply AI agents downstream of writing code. So, just as agentic AI has made writing code trivial, we can expect it to improve the remaining 60% to 70% of our job as well. Maintenance of code can be a real challenge. We are all familiar with how it often goes. We get a vague customer report of a problem along with a copious log, and we have to dive in and make sense of a very complex situation. The application has hundreds of settings and options, making the potential code paths practically infinite. Maybe we have a copy of the customer’s database with their precise settings, or maybe we just know they have one specific setting inside the problem feature set. Maybe we can reproduce the problem. Maybe we can’t. It’s almost always vague and confusing. Here’s where agents can really shine. Normally, a huge log is a problem. What mere mortal can sift through the thousands of entries that our powerful logging tools can produce? No human, but Claude Code will happily gobble up all of that data and quickly zero in on the problem. And by quickly, I mean frequently in a matter of minutes or seconds. Or as Spiros Xanthos https://www.linkedin.com/in/spiros/ , the CEO of Resolve AI — producer of a tool specifically designed for applying agents to production problems — says, “The real opportunity is purpose-built AI that can carry more of the cognitive load of production… If we get that right, developers spend less time manually piecing together what happened and more time building and improving the systems they own.” I’ll venture to say that dealing with production problems is more challenging than writing code, both for humans and agents. But agents have huge contexts and infinite patience, and we humans do not. Code is a bounded system, and agents can train on an enormous body of data illustrating a vast array of coding techniques. Production? As Xanthos put it to me, it’s effectively unbounded, enormously varying, and a living, breathing system different for every product and every company. We should always focus on the tightest bottleneck, and then move on to the next one. We’ve solved the coding choke point, and now we need to turn our attention to the next one — fixing production issues. That’s a non-trivial part of a developer’s job, and we shouldn’t be surprised that AI agents can do it better than we can.