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Automating the Developer Workflow: Lessons from Building with Gemini & AI Agents at Google
Erwin Huizenga
Head of AI Developer Engineering
Large Language Models are reshaping the technology landscape, but moving from experimental projects to organisational-wide adoption presents a significant challenge. This session demystifies the process by detailing Google's internal journey from traditional machine learning to applied Generative AI. It moves beyond theory to present a tangible blueprint for organizational transformation.
The centerpiece of this talk is a deep-dive into a real-world case study: the automation of open-source repository maintenance. Discover how Google-built AI Agents, powered by Gemini, are used to triage bugs, suggest code improvements, and update documentation autonomously. This initiative resulted in a measured 80% reduction in engineering time spent on maintenance tasks.
Large Language Models are reshaping the technology landscape, but moving from experimental projects to organisational-wide adoption presents a significant challenge. This session demystifies the process by detailing Google's internal journey from traditional machine learning to applied Generative AI. It moves beyond theory to present a tangible blueprint for organizational transformation.
The centerpiece of this talk is a deep-dive into a real-world case study: the automation of open-source repository maintenance. Discover how Google-built AI Agents, powered by Gemini, are used to triage bugs, suggest code improvements, and update documentation autonomously. This initiative resulted in a measured 80% reduction in engineering time spent on maintenance tasks.
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