API ECOSYSTEMS IN THE AGE OF ARTIFICIAL INTELLIGENCE

Abstract

APIs are undergoing a fundamental shift from static integration mechanisms toward dynamic, AI‑interpretable interaction surfaces. Large language models and autonomous agents increasingly discover, understand, and orchestrate APIs with minimal human intervention, reshaping integration paradigms across domains. This systematic review (2020–2026) analyzes emerging AI‑native API ecosystems along six dimensions: functionality, security, governance, architecture, efficiency, and application areas. The findings highlight an evolution from conventional REST, SOAP, and messaging architectures to adaptive, context‑aware, and policy‑driven interface models. Concurrently, novel security risks—such as prompt injection, model manipulation, and cascading threats in multi‑layer API orchestrations—intensify the need for advanced protective controls, including mTLS, OAuth 2.1, and zero‑trust governance architectures. A key contribution of this work is a taxonomy that classifies AI‑driven API ecosystems according to autonomy level, governance maturity, interoperability, security posture, and energy efficiency. The review positions APIs as foundational components of intelligent systems and offers guidance for research, standardization efforts, and the secure deployment of AI‑native API architectures.