Agentic Kernel offers a decoupled architecture for constructing AI agents by composing reusable components. Developers can define planning pipelines to break down goals, configure short-term and long-term memory stores using embeddings or file-based backends, and register external tools or APIs for action execution. The framework supports dynamic tool selection, agent reflection cycles, and built-in scheduling to manage agent workflows. Its pluggable design accommodates any LLM provider and custom components, enabling use cases such as conversational assistants, automated research agents, and data-processing bots. With transparent logging, state management, and easy integration, Agentic Kernel accelerates development while ensuring maintainability and scalability in AI-driven applications.

Governance.ai is designed to assist organizations in developing a robust AI governance framework. It provides resources for tracking AI models and data usage, ensuring ethical compliance, and enhancing transparency. By integrating best practices from various fields, Governance.ai helps users navigate the complexities of AI governance, balancing innovation with regulatory demands.

Responsible AI focuses on promoting ethical practices in artificial intelligence development and deployment. As a global non-profit organization, it provides organizations and AI professionals with assessments, certifications, and community engagement to create safe and responsible AI systems. The initiative emphasizes transparency, accountability, and alignment with ethical standards, enabling users to navigate the complexities of AI governance and manage associated risks effectively while fostering innovation.

Stable Attribution is an innovative tool designed to identify and attribute the human creators behind AI-generated images. It leverages AI algorithms to trace the source images used in generating AI artworks, providing transparency and recognition to original artists. The service allows users to upload an AI-generated image and returns information about the potential human-made source images that contributed to its creation. This helps in promoting accountability and preventing misuse in the rapidly evolving field of AI art.