Agentic AI can be built in many programming languages, but the best choices are those with strong support for APIs, concurrency, and data handling. Languages commonly used for backend services tend to work well because Agentic AI systems are fundamentally backend systems with decision-making logic layered on top. The language itself matters less than the ecosystem and tooling around it.
A good language for Agentic AI should make it easy to define structured data, call external services, manage state, and implement control loops. It should also integrate well with databases, including vector databases such as Milvus or Zilliz Cloud. Libraries for HTTP clients, async processing, and logging are particularly important. Many teams choose the same language they already use for backend services to simplify deployment and monitoring.
From a practical standpoint, consistency is more important than novelty. Use a language your team knows well, that fits your existing infrastructure, and that allows you to write clear, testable code. Agentic AI development is less about language features and more about system design: how you structure agent state, enforce constraints, and observe behavior. A familiar, stable language will usually outperform a more experimental choice when it comes to building reliable Agentic AI systems.