From Human Interfaces to Agent Interfaces: Rethinking Software Design in the Age of AI-Native Systems

2026-03-19

AI Summary

This work proposes a new paradigm for software design tailored to AI agents as primary users, addressing the limitations of traditional human-centric approaches. It formally defines the concept of an “agent interface” for the first time, centering on callable capabilities and emphasizing machine interpretability, composability, and invocation reliability. Guided by the interaction requirements of large language model–based agents, the study introduces a capability-oriented software architecture and corresponding interface specifications. By establishing a conceptual foundation and design framework for AI-native systems, this research advances software engineering beyond monolithic applications toward dynamic, composable ecosystems of interoperable capabilities.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Intelligent User Interfaces
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Software systems have traditionally been designed for human interaction, emphasizing graphical user interfaces, usability, and cognitive alignment with end users. However, recent advances in large language model (LLM)-based agents are changing the primary consumers of software systems. Increasingly, software is no longer only used by humans, but also invoked autonomously by AI agents through structured interfaces. In this paper, we argue that software engineering is undergoing a paradigm shift from human-oriented interfaces to agent-oriented invocation systems. We formalize the notion of agent interfaces, introduce invocable capabilities as the fundamental building blocks of AI-oriented software, and outline design principles for such systems, including machine interpretability, composability, and invocation reliability. We then discuss architectural and organizational implications of this shift, highlighting a transition from monolithic applications to capability-based systems that can be dynamically composed by AI agents. The paper aims to provide a conceptual foundation for the emerging paradigm of AI-native software design.

Problem

• AI-native systems
• agent interfaces
• software design
• LLM-based agents
• human-computer interaction

Innovation

• agent interfaces
• invocable capabilities
• AI-native software
• composability
• machine interpretability