While generative AI for code dramatically increases development speed, veteran developers emphasize a cautious approach due to reliability and quality control concerns in production environments.
Imagine you are an architect building a house. In the past, you had to draw blueprints by hand and lay bricks one by one. But one day, a “robot that builds houses at the push of a button” appears. Some might cheer and want to deploy this robot to the construction site immediately, but a seasoned architect who has spent a lifetime building houses might pull their chair back and say, “Wait a minute, how do we prove that the house this robot built can withstand the elements and won’t collapse?”
Similar scenes are unfolding among developers recently with the emergence of “Agentic AI,” which sets its own goals and performs tasks in software development. Why are veteran developers surprisingly cautious about this smart AI that can write code, fix errors, and even help with deployments on its own?
Why does this matter?
Software is like the blood vessels of modern society. Everything from the banking apps we use every day and hospital systems to autonomous vehicles runs on code. What if the code written by AI looks fine but stops working at a critical moment or creates a security hole?
The caution of veteran developers isn’t just because they “don’t like new tools.” They know better than anyone that there is a vast ocean between “code being written” and “code working perfectly in a production environment.” Source 1 For enterprises, adopting agentic AI is not just a question of increasing speed; it is a complex task that requires careful consideration of data security, talent acquisition, and return on investment (ROI). Source 5
Easy to understand: The gap between ‘functionality’ and ‘trust’
Put simply, it’s like giving a chef a “robot that chops vegetables incredibly fast.” The robot can slice up ingredients in an instant, but it remains the chef’s responsibility to ensure the robot keeps the cutting board hygienic and doesn’t accidentally damage kitchen equipment.
The same goes for Agentic AI. AI can write thousands of lines of code in minutes. But verifying whether that code fits well with existing systems or checking for security vulnerabilities that hackers could exploit is a separate process. Current Agentic Development Security is focusing beyond simple code checking on how to safely control the “process itself” by which AI generates and modifies code. Source 12
Security firm Snyk already analyzed approximately 10,000 development environments and warned that these AI agents are creating new forms of security risks. Source 13
Current state: Who is the ‘real deal’?
There are thousands of companies in the market claiming to offer “Agentic AI.” However, according to a Gartner survey, only about 130 of them actually possess substantive technology. Source 6
Skilled engineers in the field acknowledge that AI agents are very useful tools for reducing repetitive tasks. Source 9 However, if you adopt agents without properly modifying the Software Development Life Cycle (SDLC, the entire process from software planning to deployment), there is a high risk of increased quality degradation and confusion during system integration. Source 8
What will happen in the future?
Agentic AI will not replace developers; it will redefine the way they work. In the future, the ability to verify and manage the code written by AI will become a far more important skill than how well AI writes the code itself.
Some forward-thinking organizations have already introduced the “Software Factory” concept, building processes where AI agents write code, but that code must pass rigorous scenarios and quality gates designed by human developers before being applied to actual systems. Source 8 Source 17
MindTickleBytes AI Reporter’s Perspective
Agentic AI is not a magic wand; it is like a sophisticated machine part. The caution of veteran developers is not a rejection of technology, but the final bastion of defense to ensure technology does not undermine social trust. Tools will get faster, but we must not forget that responsibility for the results remains in the human realm.
References
- Why are good developers so wary of agentic development? https://martinfowler.com/rachels-ramblings/good-devs-wary.html
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Why Enterprises Are Hesitant to Adopt Agentic AI LinkedIn https://www.linkedin.com/posts/adam-tarbox_enterprises-wary-of-agentic-ai-due-to-data-activity-7351606862925946880-8jmf - Why CIOs should be wary of agentic AI claims and vendor-led hype https://finance.yahoo.com/news/why-cios-wary-agentic-ai-070000323.html
- Agentic Coding: Why the Software Development Lifecycle Needs to be Reimagined https://insights.mgm-tp.com/en/2026/quality-assurance/agentic-coding-why-the-software-development-lifecycle-needs-to-be-reimagined/
- The Shift Towards Agentic AI: What It Means for Developers https://dev.to/lofcz/the-shift-towards-agentic-ai-what-it-means-for-developers-4a4o
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The New Security Risks of Agentic Development Snyk https://snyk.io/blog/agentic-development-lifecycle/ - What nearly 10,000 developer environments reveal about the security implications of AI coding agents https://snyk.io/blog/agentic-development-security-ai-coding-risk/
- StrongDM Software Factory https://factory.strongdm.ai/
- AI is too slow
- There is a difference between code being written and it working properly in a production environment
- AI is too expensive
- Office interior
- Software Development Life Cycle (SDLC)
- Lunch menu
- Most have no substance
- Only about 130 out of thousands provide practical solutions
- All companies are innovative