Agentic Coding: When AI Takes Over Development Tasks
Today, coding agents can provide initial solution proposals for development tasks within minutes. The real challenge begins after that: results must be technically reviewed, securely integrated, and used responsibly. Michael Krusche, founder and CEO of Krusche & Company, explains why experienced developers and clear quality standards are becoming even more important in the age of AI.
Ever since generative AI has made its way into software development, the discussion has revolved around one central question: Will developers even be needed in the future? This question doesn’t go far enough. What matters far more is how AI is shifting roles within development teams. Today, the biggest challenge is no longer writing code, but rather evaluating and verifying results from a technical perspective and taking responsibility for their implementation. This is precisely where the real challenge for companies lies.
Why Experience Is a Decisive Factor
The quality of software is not determined solely by how quickly new code is developed. What matters far more is whether it integrates effectively into existing systems and business processes and remains sustainable in the long term. Most enterprise applications consist of systems that have evolved over time, custom business processes, and numerous technical dependencies. Changes affect interfaces, data flows, permissions, and existing architectures. This context cannot be inferred from a single prompt or a code suggestion.
This also changes the role of experienced developers. The ability to contextualize technical decisions, further develop target architectures, identify risks early on, and assess long-term impacts is becoming increasingly important. Implementing an AI tool is often relatively quick. The real challenge, however, lies in permanently integrating these new capabilities into existing workflows. The greatest value is created where speed is combined with clear quality standards and technical accountability.
Agentic Coding Is Transforming Software Development
AI has long since moved beyond simply assisting with writing individual lines of code. Today, modern coding agents already handle entire work steps within existing development workflows. They generate boilerplate code, add test cases, assist with refactoring, and prepare changes based on tickets or issues. This works particularly well in environments where requirements are clearly formulated, tasks are unambiguously defined, and technical standards are established. The more structured the framework, the easier it is to automate individual development steps.
The real time savings come from the code-writing process. A ticket that used to require several hours of development work can now lead to an initial proposed solution within a few minutes. This shifts the focus of software development: what matters most is no longer just how quickly new code is written, but how reliably it can be evaluated, tested, and integrated into existing systems. Code reviews, testing, quality assurance, and security are thus becoming increasingly important. The faster AI delivers results, the more important clear quality standards and transparent approval processes become. More code therefore does not automatically mean greater productivity—productivity only arises when additional code can be reliably tested, understood, and accounted for.
New tools require clear rules
The productive use of agentic coding requires more than just powerful technologies. Companies must establish the organizational and technical prerequisites so that AI can be integrated into software development in a secure, transparent, and sustainable manner. This includes clear rules for the use of AI, binding quality and security standards, and clearly defined responsibilities. It is equally important to regularly review the use of AI and to consistently incorporate practical insights into the further development of software development processes.
Preparation for agentic coding begins long before the first prompt. Before coding agents can be deployed productively, existing applications and established system landscapes must be specifically enhanced. AI-ready engineering therefore does not mean replacing existing systems, but rather evolving them so that modern AI tools can be used effectively, securely, and sustainably over the long term. Ultimately, the success of Agentic Coding does not depend solely on the technology used, but on whether companies work together to further develop their development processes, governance, and collaboration.
Conclusion
Agentic Coding will bring about lasting change in software development—not because developers will be replaced, but because the focus of their work will shift. Less manual code writing means more technical evaluation, more architectural work, more quality assurance, and more responsibility. Companies don’t benefit solely from more powerful AI tools. What matters most is whether they evolve their development processes, roles, and governance just as consistently as they do their technology. Only then will increased speed translate into sustainable quality. The future of software development will therefore not be determined by how much code AI generates, but by how well companies integrate technology, engineering, and governance.
About the Author: Michael Krusche is the founder and CEO of Krusche & Company. For more than 25 years, he has been helping companies build modern software architectures, international development teams, and scalable digital platforms. With Beyond Outsourcing®, he advocates a partnership-based model that combines global engineering talent, nearshore teams, knowledge transfer, and artificial intelligence.
More information: www.krusche.de
