Java or Python for building agents?

"Python didn't become the lingua franca of artificial intelligence by accident. I've argued that Python's dominance in AI isn't due to blazing performance or fancy features, but because it offers the shortest distance from idea to working code. It's an accessible, general-purpose language that " everyone knows," perhaps not as a first programming language but often as a second. No wonder Python has surged in popularity alongside AI's rise. Python lowers the bar to experimentation, which is critical in the fast-moving AI field."

"But Python hasn't cornered the market on AI applications, and it shouldn't, argues Rod Johnson, the creator of the popular Spring framework. Yes, if you're already using Python to build agents, "it would be hard to justify jumping to [Java]" to capture some of the advantages (like being type safe). But if you're already building with Java, using something like the Java-based Embabel agent framework "would be a no-brainer." This is yet another reminder that the key to unlocking data value is about enabling the people and tech stack you already own, rather than chasing after "mystical data scientists" and complicated data architectures."

"It's always about people It's easy to get caught up in technology wars-Python versus Java versus NextBigLanguage-but the hardest part of AI isn't the tools, it's the people. Domain knowledge, skills, and adoption matter more than picking the "perfect" programming language. If you want AI to succeed in your organization, you must meet developers and domain experts where they are. This seems obvious, yet too often our instinct is to throw shiny new tech at the problem and hope people adapt."

Use AI tools that leverage existing organizational expertise and integrate with current systems to capture the most value. Python became dominant in AI because it reduces the distance from idea to working code, is widely known, and lowers the bar to experimentation rather than because of superior performance. Java and other stacks remain viable when they align with existing tooling and developer skills; choosing a language that matches the current tech stack simplifies adoption. Domain knowledge, developer skills, and adoption drive success more than chasing a single language or exotic architectures. Empower people with familiar tools and workflows for practical AI deployment.

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