Developers contemplating the impact of generative AI on coding should note Gartner's projection that the software development landscape is evolving, with AI agents set to write most code, reviewed by seasoned developers. Already, organizations and vendors are implementing AI coding agents that can automate or delegate numerous tasks, with more exploratory initiatives expected to roll out by 2025, according to Philip Walsh from Gartner. By 2026, a significant adoption of these agents is anticipated as developers adapt to harness their strengths, leading to a profound change in engineering workflows by 2027. Gartner forecasts that around 80% of software engineers will need to reskill to accommodate roles emerging from this shift toward generative AI. While current AI coding assistants can produce code snippets, they often falter at creating complete software solutions. However, evolving AI agents are projected to efficiently tackle higher-level programming tasks. Walsh notes that while AI agents will increasingly handle code, human developers will still be essential for validating and refining the generated code. Despite mixed reviews of current AI assistants, vendors are shifting focus toward autonomous coding systems, though full independence remains experimental.

Walsh emphasizes that current tools struggle with context comprehension and the intricate interactions of code dependencies. Some companies, like Caylent, are proactively integrating AI for specific coding tasks, relying on established validation processes to mitigate errors. Moving forward, a new generation of coding agents might not only generate code but also create and execute validation tests. Experts emphasize that AI-native coding will be transformative, although challenges remain. There’s a risk of overwhelming complexity as software becomes predominantly AI-generated, complicating oversight and maintainability. As the landscape shifts toward senior roles prioritizing code review and management of AI-generated software, questions emerge about the evolution of the developer talent pipeline. Skepticism persists among some IT leaders, who argue that while AI may simplify code writing, the core challenges revolve around maintaining systemic integrity and understanding nuanced business requirements that AI cannot replicate. The complexity of developing robust, adaptable systems will continue to require human insight and collaboration.