AI is shifting from simple chatbots to more complex backend systems capable of making decisions, such as handling refunds and managing supply chains. Traditional frameworks can’t keep up; what’s needed is robust infrastructure. AgentField provides this infrastructure, acting as a control plane for autonomous AI agents. It treats these agents as backend services, ensuring they are production-ready without relying on prompt wrappers or external memory systems like Redis. AgentField supports a range of programming languages, including Python, Go, and TypeScript, allowing developers to create agents that can communicate and execute tasks asynchronously. Key features include routing and discovery through REST APIs, built-in memory with vector search, and observability with automatic workflow visualizations. Each agent also receives a unique cryptographic identity, enhancing security and trust with verifiable credentials and policy enforcement. The architecture of AgentField is designed to handle long-running tasks effectively, unlike traditional frameworks that can struggle with timeouts. Agents can invoke each other seamlessly through the control plane, with every action tracked for transparency. AgentField’s performance metrics indicate it can handle millions of requests per second with minimal memory overhead, making it a strong choice for large-scale applications. The platform emphasizes real-world use cases, such as a parallel research backend that can operate with thousands of agents, showcasing its scalability and efficiency.