This repository provides a structured set of design patterns and reference architectures for building Agentic AI systems on Azure, targeting developers and architects. It aims to guide users in creating autonomous, reasoning, and adaptable AI systems while addressing challenges like safety, transparency, and fairness.

How It Works

The project is organized into five sections: Foundational Concepts (principles, frameworks, production readiness), Agentic Design Patterns (specific scenarios, best practices, reference architectures), Reference Architectures (Azure implementation details), Agentic Accelerators (sample implementations), and Auxiliary Design Patterns (supporting skills, data pipelines). This layered approach allows users to grasp core concepts before diving into specific implementations and production-ready solutions.

Quick Start & Requirements

• Installation: No explicit installation instructions are provided, suggesting the content is primarily documentation and conceptual guidance.
• Prerequisites: Familiarity with LLMs, Agentic AI concepts, and Azure services is implied.
• Resources: Access to Azure is likely required for implementing the reference architectures and accelerators.
• Links: The repository structure itself serves as a guide.

Highlighted Details

• Focuses on Agentic AI systems, emphasizing autonomy, reasoning, and adaptable planning.
• Addresses key challenges in agentic systems: safety, transparency, explainability, security, privacy, fairness, and human interaction.
• Provides practical guidance through reference architectures and sample implementations (accelerators).
• Includes auxiliary patterns for supporting agent skills and data enrichment.

Maintenance & Community

• Welcomes community contributions via pull requests, requiring agreement to a Contributor License Agreement (CLA).
• Adheres to the Microsoft Open Source Code of Conduct.
• No specific community channels (Discord/Slack) or roadmap links are provided in the README.

Licensing & Compatibility

• The repository itself does not explicitly state a license in the README. However, it is hosted by Microsoft, and contributions are subject to a CLA, implying a Microsoft-managed open-source license.
• Compatibility for commercial use or closed-source linking would depend on the specific license applied to the repository's content.

Limitations & Caveats

The provided accelerators may not cover all aspects of production-ready solutions, and users are directed to foundational concepts for comprehensive understanding. The patterns are not exhaustive and are expected to evolve.