TLDR: Google is actively advocating for an iterative and experimental approach to developing AI agents, emphasizing that proficiency in working with AI, much like coding, is a skill honed through practice, iteration, and even failure. The tech giant views AI agents as the next evolution in delegating complex tasks to software, leveraging advanced tools like large language models.
Google is strongly advocating for a culture of continuous experimentation and refinement in the burgeoning field of AI agents. The company believes that mastering the interaction with AI is akin to acquiring any other technical skill, such as coding or architecture, where improvement comes through consistent practice, iteration, and a willingness to learn from mistakes. This perspective was highlighted in a recent SiliconANGLE report on August 19, 2025, featuring insights from Google Cloud experts.
Jason Davenport, technical lead for DevRel at Google, and Aja Hammerly, director of DevX AI at Google Cloud, discussed the evolving landscape of AI agents during an exclusive broadcast on theCUBE’s ‘Google Cloud: Passport to Containers’ interview series. They addressed the distinction between the hype surrounding AI and the practical realities of its application, emphasizing that AI agents represent the latest iteration in a decades-long effort to empower developers by making computers execute tasks on their behalf.
Davenport noted the significant progress from the early days of large language models (LLMs) and generative AI, stating, ‘You think now, with agents, we can get code to do things for us on our behalf and execute those goals. We’re starting to be kind of in that territory.’ He underscored that tools like Google’s AI Studio or Gemini CLI are crucial for developers to experiment, make mistakes, and effectively learn how to interact with AI. ‘One of the benefits is just starting to ask it for tasks,’ Davenport added, describing it as a journey to programming oneself and code.
Hammerly further clarified the concept of AI agents, explaining that while the term is relatively new, the underlying principle of delegating work to software has existed for decades, from early chatbots to microservices. Today’s AI agents, she stated, are essentially ‘code with a job,’ empowered by more sophisticated tools such as large language models and application programming interfaces (APIs). She emphasized the importance of understanding how to ‘set the tools up to win,’ noting that asking an AI tool why it produced a certain output can be as valuable as simply asking it to try again. Hammerly remarked, ‘It’s kind of cool that the tools will help you work with the tools. If you care about how it happens, you’d better be very specific.’
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Google’s push for this experimental approach aims to demystify AI agents and provide clear pathways for developers to harness this expansive technological future, ultimately fostering a deeper understanding and more effective utilization of AI capabilities.
