I am a cloud solutions architect working closely with customers to support them during their cloud journey. I work backwards with the customers and help them design and build data platforms in the cloud and deliver valuable data products. Prior to that, I have been working in the IT industry for 13 years working with data analytics projects. I have designed and built DWHs, big data platforms and ETL solutions across industries from financial institutions to telcos and EU institutions. Apart from work I enjoy playing with my dog and I love travelling.
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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