I am a cloud solutions architect working closely with customers to support them during their cloud journey. I design scalable, robust and secure systems ready to support global markets and help customers achieve their goals. Beforehand, I was tweaking, writing and improving the code of large performant back end systems, either as a software engineer or a team lead (java is still my passion). Apart from work I enjoy running in the country side or playing with my dog.
Despite the promise of agentic AI, most development teams struggle with adoption due to concerns about code quality and lack of clear implementation paths. Developers worry about hallucinations, incorrect suggestions, and whether AI-generated code meets production standards—concerns that often lead to resistance and superficial proof-of-concepts that fail to deliver value.
Drawing from 15 years as software/data engineers and hands-on experience implementing agentic AI tools across multiple customer PoCs, we have identified a pragmatic adoption path that addresses real concerns about code quality and hallucinations. Through working with teams from individual contributor to leadership roles, we have seen the common issues: lack of time, pressure to ship to production, fear of the unknown, and reluctance to move from comfort zones.
You'll discover how to achieve faster issue resolution and return thousands of hours to your developers by following a proven adoption path. Learn which use cases to start with, how to measure success, and address team resistance. Real customer examples demonstrate concrete outcomes: one team returned 16,700 developer hours through optimized code reviews, while another achieved 65-80% faster issue resolution.
Software engineers and team leads will leave with a clear roadmap for introducing agentic AI to their team, specific strategies to avoid common pitfalls, and the confidence to drive meaningful adoption that delivers measurable results.
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