Step into the future with Agentic AI Systems, designed to tackle complex challenges with minimal human intervention. These intelligent systems feature conversable agents that can self-organize or be centrally orchestrated, utilizing advanced capabilities like planning, memory, and tool usage.
\nOur comprehensive guide unveils a baseline architecture for deploying these systems using top-tier frameworks like AutoGen and Azure’s powerful suite of services. Learn how Azure Container Apps, Azure Kubernetes Services, and Azure AI Studio seamlessly integrate to create a secure, scalable, and efficient environment for your AI agents.
\nUnlock the full potential of automation and AI with Agentic AI Systems. Transform your enterprise processes and stay ahead of the curve with our expert insights.
","body":"co-author: PierreMalarme
\n\n
Agentic AI Systems are designed to resolved complex problems with limited direct human supervision [1]. These systems are composed of multiple conversable agents that converse with each other and can be orchestrated centrally or self-organize in a decentralized manner [1, 2]. As the usage of multi-agents systems increases in the enterprise to automate complex processes or solve complex tasks, we would like to take a closer look at what the architecture of such systems could look like.
\n\n
These agents possess capabilities such as planning, allowing them to predict future states and select optimal actions to achieve specific goals. They also incorporate memory, enabling them to recall past interactions, experiences, and knowledge, which is crucial for maintaining continuity in tasks and refining strategies. Additionally, agents can utilize various tools, including APIs and external software, to execute code, query databases, and interact with other systems [1, 3]. This tool usage extends their functionality and enables them to perform a wide range of actions.
\nBecause agents can take actions, write, and execute code, there is a potential risk of running code that could be malicious or harmful to the host system or other users [3]. Therefore, understanding the architecture of these systems is crucial to sandboxing code execution, restricting or denying access to production data and services, and mitigating failures, vulnerabilities, and abuses.
\n\n
\n
\n
\n
Azure AI Studio [5] is a managed cloud service used to train, deploy, automate, and manage machine learning models, including large language models (LLM), small language models (SLM), and multi-modal models used by the agents. The platform provides a comprehensive suite of tools and services to facilitate the end-to-end machine learning lifecycle. Key features of the Azure AI Studio include:
\nPrompt Flow [6] is a development tool designed to streamline the entire development lifecycle of Generative AI applications. It supports creating, testing, and deploying prompt flows, which can be used to generate responses or actions based on given prompts. These prompt flows can be deployed to a Machine Learning Workspace or containerized and deployed to Azure Container Apps or Azure Kubernetes Services [7]. AI Studio can also be used to develop and deploy these prompt flows.
\nManaged Online Endpoints are used by agents and backend services to invoke prompt flows for real-time inference. They provide scalable, reliable, and secure endpoints for deploying machine learning models, enabling real-time decision-making and interactions [7].
\nAzure AI Dependencies include essential Azure services and resources that support the functioning of AI Studio and associated projects [8]:
\nContent AI Safety ensures AI-generated content is ethical and safe, preventing the creation or spread of harmful or biased material.
\nThese components and services provided by the Azure AI Studio enable seamless integration, deployment, and management of sophisticated AI solutions, facilitating the development and operation of Agentic AI Systems.
\n\n
Agentic AI Systems represent a significant advancement in artificial intelligence, providing autonomous decision-making and problem-solving capabilities with minimal human intervention. By leveraging conversable agents with planning, memory, and tool usage capabilities, these systems can address complex enterprise challenges. The proposed architecture, utilizing Azure's suite of services—including Azure OpenAI, AI Studio, Azure API Management, Container Apps and many others —provides a robust foundation for deploying these intelligent systems. Ensuring the safety, reliability, and ethical operation of such systems is critical, particularly in managing code execution and data security. As the field evolves, continuous refinement of these architectures and practices will be essential to maximize the benefits and minimize risks associated with Agentic AI.
\n\n
\n
Special thanks to our colleagues for their feedback on the architecture:
\nco-author:
\n
Agentic AI Systems are designed to resolved complex problems with limited direct human supervision [1]. These systems are composed of multiple conversable agents that converse with each other and can be orchestrated centrally or self-organize in a decentralized manner [1, 2]. As the usage of multi-agents systems increases in the enterprise to automate complex processes or solve complex tasks, we would like to take a closer look at what the architecture of such systems could look like.
\n\n
These agents possess capabilities such as planning, allowing them to predict future states and select optimal actions to achieve specific goals. They also incorporate memory, enabling them to recall past interactions, experiences, and knowledge, which is crucial for maintaining continuity in tasks and refining strategies. Additionally, agents can utilize various tools, including APIs and external software, to execute code, query databases, and interact with other systems [1, 3]. This tool usage extends their functionality and enables them to perform a wide range of actions.
\nBecause agents can take actions, write, and execute code, there is a potential risk of running code that could be malicious or harmful to the host system or other users [3]. Therefore, understanding the architecture of these systems is crucial to sandboxing code execution, restricting or denying access to production data and services, and mitigating failures, vulnerabilities, and abuses.
\n\n
\n
\n
\n
Azure AI Studio [5] is a managed cloud service used to train, deploy, automate, and manage machine learning models, including large language models (LLM), small language models (SLM), and multi-modal models used by the agents. The platform provides a comprehensive suite of tools and services to facilitate the end-to-end machine learning lifecycle. Key features of the Azure AI Studio include:
\nPrompt Flow [6] is a development tool designed to streamline the entire development lifecycle of Generative AI applications. It supports creating, testing, and deploying prompt flows, which can be used to generate responses or actions based on given prompts. These prompt flows can be deployed to a Machine Learning Workspace or containerized and deployed to Azure Container Apps or Azure Kubernetes Services [7]. AI Studio can also be used to develop and deploy these prompt flows.
\nManaged Online Endpoints are used by agents and backend services to invoke prompt flows for real-time inference. They provide scalable, reliable, and secure endpoints for deploying machine learning models, enabling real-time decision-making and interactions [7].
\nAzure AI Dependencies include essential Azure services and resources that support the functioning of AI Studio and associated projects [8]:
\nContent AI Safety ensures AI-generated content is ethical and safe, preventing the creation or spread of harmful or biased material.
\nThese components and services provided by the Azure AI Studio enable seamless integration, deployment, and management of sophisticated AI solutions, facilitating the development and operation of Agentic AI Systems.
\n\n
Agentic AI Systems represent a significant advancement in artificial intelligence, providing autonomous decision-making and problem-solving capabilities with minimal human intervention. By leveraging conversable agents with planning, memory, and tool usage capabilities, these systems can address complex enterprise challenges. The proposed architecture, utilizing Azure's suite of services—including Azure OpenAI, AI Studio, Azure API Management, Container Apps and many others —provides a robust foundation for deploying these intelligent systems. Ensuring the safety, reliability, and ethical operation of such systems is critical, particularly in managing code execution and data security. As the field evolves, continuous refinement of these architectures and practices will be essential to maximize the benefits and minimize risks associated with Agentic AI.
\n\n
\n
Special thanks to our colleagues for their feedback on the architecture:
\nStep into the future with Agentic AI Systems, designed to tackle complex challenges with minimal human intervention. These intelligent systems feature conversable agents that can self-organize or be centrally orchestrated, utilizing advanced capabilities like planning, memory, and tool usage.
\nOur comprehensive guide unveils a baseline architecture for deploying these systems using top-tier frameworks like AutoGen and Azure’s powerful suite of services. Learn how Azure Container Apps, Azure Kubernetes Services, and Azure AI Studio seamlessly integrate to create a secure, scalable, and efficient environment for your AI agents.
\nUnlock the full potential of automation and AI with Agentic AI Systems. Transform your enterprise processes and stay ahead of the curve with our expert insights.
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