In a landscape flooded with AI agent frameworks, the Gliding Horse project takes a refreshingly principled approach. Instead of chasing the latest trend, it builds on established methodologies: 5W2H (Who, What, When, Where, Why, How, How Much) for task decomposition, JSON-LD for structured context representation, and the PDCA (Plan-Do-Check-Act) cycle for execution. This combination creates a 'Harness' that is not only executable but also auditable and evolvable. The use of JSON-LD is particularly clever, as it allows agents to share and reason over knowledge graphs in a standard format, enabling interoperability. For developers building complex autonomous systems, this design offers a solid foundation that prioritizes clarity and maintainability over hype. It's a reminder that sometimes the best innovations come from applying proven concepts in new contexts.
The Gliding Horse Agent OS introduces a structured approach to AI agent design using 5W2H task ontologies, JSON-LD for context, and PDCA execution models. This contrasts with trend-driven frameworks, offering a more auditable and evolvable architecture for autonomous agents.