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The Agent-to-Agent (A2A) protocol defines a schema for structured metadata exchange among digital agents, enabling interoperability across distributed systems. However, it lacks provisions for representing civic initiatives and public institutions in machine-readable form. This paper extends A2A’s core principles—modular self-description, machine readability, and decentralized discoverability—to human-led programs. We introduce three metadata blocks tailored to civic contexts: ProgramCard, ParticipationTemplate, and operating_character. These structures are grounded in the G-O-S-R model (Goal \(\leftarrow \) Obstacles \(\leftarrow \) Solutions \(\leftarrow \) Resources), which supports structured reasoning about complex social challenges. The proposed extension enables programs to describe their scope, values, and engagement pathways in a format interpretable by both humans and AI agents, making civic initiatives more discoverable and accessible for collaboration and public benefit. A reference implementation is available to support experimentation and adoption.

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Integrating Civic Initiatives into AI Agent Ecosystems: A Human-Aligned Extension of the A2A Protocol

  • 会议论文

  • 发表日期：

    2026年

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  • Kevin Kells

摘要

The Agent-to-Agent (A2A) protocol defines a schema for structured metadata exchange among digital agents, enabling interoperability across distributed systems. However, it lacks provisions for representing civic initiatives and public institutions in machine-readable form. This paper extends A2A’s core principles—modular self-description, machine readability, and decentralized discoverability—to human-led programs. We introduce three metadata blocks tailored to civic contexts: ProgramCard, ParticipationTemplate, and operating_character. These structures are grounded in the G-O-S-R model (Goal \(\leftarrow \) Obstacles \(\leftarrow \) Solutions \(\leftarrow \) Resources), which supports structured reasoning about complex social challenges. The proposed extension enables programs to describe their scope, values, and engagement pathways in a format interpretable by both humans and AI agents, making civic initiatives more discoverable and accessible for collaboration and public benefit. A reference implementation is available to support experimentation and adoption.