The Gliding Horse framework represents a novel approach to AI agent architecture by integrating cognitive functions with security mechanisms. The core innovation lies in the dynamic PDCA (Plan-Do-Check-Act) orchestration that allows agents to continuously improve their decision-making processes. The framework introduces a four-layer memory system that enables agents to retain and recall information more effectively, while the self-evolving skill graphs allow agents to adapt their capabilities over time. The 'immune system' component provides real-time security monitoring and threat detection, ensuring that agents operate within safe boundaries. This holistic approach addresses a critical gap in current agent designs, where cognition and safety are often treated as separate concerns. For developers building complex agent systems, this framework offers a blueprint for creating more reliable and autonomous AI agents that can operate in dynamic environments.
This article introduces the Gliding Horse framework for advanced cognitive optimization in AI agents, focusing on the synergy between the agent's 'brain' (SA scheduling, skill graphs) and 'immune system' (security gates). It covers dynamic PDCA orchestration, four-layer memory, and self-evolving skill graphs. This represents a significant step toward more robust and autonomous agent systems.