Step-by-Step Tutorial: Building an AI Agent Using Azure AI Foundry
This blog post provides a comprehensive tutorial on building an AI agent using Azure AI Agent service and the Azure AI Foundry portal. AI agents represent a powerful new paradigm in application development, offering a more intuitive and dynamic way to interact with software. They can understand natural language, reason about user requests, and take actions to fulfill those requests. This tutorial will guide you through the process of creating and deploying an intelligent agent on Azure. We'll cover setting up an Azure AI Foundry hub, crafting effective instructions to define the agent's behavior, including recognizing user intent, processing requests, and generating helpful responses. We'll also discuss testing the agent's conversational abilities and provide additional resources for expanding your knowledge of AI agents and the Azure AI ecosystem. This hands-on guide is perfect for anyone looking to explore the practical application of Azure's conversational AI capabilities and build intelligent virtual assistants. Join us as we dive into the exciting world of AI agents.AI Agents: The Multi-Agent Design Pattern - Part 8
This blog post, Part 8 in a series on AI agents, explores the Multi-Agent Design Pattern, outlining the benefits and key components of building systems with multiple interacting agents. It details the scenarios where multi-agent systems excel (large workloads, complex tasks, diverse expertise), highlights their advantages over single-agent approaches (specialization, scalability, fault tolerance), and discusses the fundamental building blocks for implementation, including agent communication, coordination mechanisms, and architectural considerations. The post introduces common multi-agent patterns (group chat, hand-off, collaborative filtering) and illustrates these concepts with a refund process example. Finally, it includes a practical assignment and provides links to further resources and previous posts in the series.Unlocking the Power of AI Agents: An Introductory Guide - Part 1
This blog post introduces Microsoft's "AI Agents for Beginners" course and its accompanying GitHub repository, offering a valuable resource for anyone interested in learning about agentic AI. The course covers fundamental concepts, different types of agents, design patterns, and practical frameworks for building intelligent agents. Whether you're a beginner, intermediate learner, or advanced developer, this free resource provides a comprehensive learning experience, empowering you to create AI systems that can reason, plan, and act autonomously. The post also highlights additional resources, including links to Azure AI Agent Service, Semantic Kernel, AutoGen, and the Azure AI Discord community. Embark on your agentic AI journey today and discover the future of intelligent applications.AI Agents: Mastering Agentic RAG - Part 5
This blog post, Part 5 of a series on AI agents, explores Agentic RAG (Retrieval-Augmented Generation), a paradigm shift in how LLMs interact with external data. Unlike traditional RAG, Agentic RAG allows LLMs to autonomously plan their information retrieval process through an iterative loop of actions and evaluations. The post highlights the importance of the LLM "owning" the reasoning process, dynamically selecting tools and refining queries. It covers key implementation details, including iterative loops, tool integration, memory management, and handling failure modes. Practical use cases, governance considerations, and code examples demonstrating Agentic RAG with AutoGen, Semantic Kernel, and Azure AI Agent Service are provided. The post concludes by emphasizing the transformative potential of Agentic RAG and encourages further exploration through linked resources and previous blog posts in the series.AI Agents: Key Principles and Guidelines - Part 3
This blog post, the third in a series on AI agents, focuses on user-centric design principles for building effective and trustworthy agentic systems. Drawing from the "Agentic Design Patterns" section of Microsoft's "AI Agents for Beginners" GitHub repository, the post outlines key principles categorized by Agent (Space), Agent (Time), and Agent (Core). These principles emphasize connection, accessibility, leveraging historical context, adapting to future needs, and establishing trust through transparency and control. Practical implementation guidelines are provided, along with a travel agent example to illustrate how these principles can be applied in real-world scenarios. The post also links to additional resources and previous installments in the series for a comprehensive learning experience.AI Agents: Planning and Orchestration with the Planning Design Pattern - Part 7
This blog post, Part 7 in a series on AI agents, focuses on the Planning Design Pattern for effective task orchestration. It explains how to define clear goals, decompose complex tasks into manageable subtasks, and leverage structured output (e.g., JSON) for seamless communication between agents. The post includes code snippets demonstrating how to create a planning agent, orchestrate multi-agent workflows, and implement iterative planning for dynamic adaptation. It also links to a practical example notebook (07-autogen.ipynb) and further resources like AutoGen Magnetic One, encouraging readers to explore advanced planning concepts. Links to the previous posts in the series are provided for easy access to foundational AI agent concepts.AI Agents: Mastering the Tool Use Design Pattern - Part 4
This blog post, Part 4 of a series on AI agents, delves into the Tool Use Design Pattern, a key concept in enabling agents to interact with external systems and perform a wider range of tasks. The post explains how tools, ranging from simple functions to complex API calls, are invoked by AI agents through model-generated function calls. Several use cases are presented, highlighting the versatility of this pattern, from dynamic information retrieval and code execution to workflow automation and customer support. The post further details the implementation of function/tool calling, including choosing a suitable LLM, defining a function schema, and writing the function code. Examples using Semantic Kernel and Azure AI Agent Service illustrate how agentic frameworks simplify tool integration. Finally, the post addresses security considerations and provides links to valuable resources, including the "AI Agents for Beginners" GitHub repository and related workshops, for further learning.
Building Agentic Solutions with Autogen 0.4
Multi Agent Systems are a consequence of an organized interaction between diverse agents to achieve a goal. Similar to human collaborations, Agentic solutions are expected to collaborate effectively in accordance with the goal to be accomplished. A crucial aspect is adopting the appropriate design pattern depending on the task on hand. Let us look at the design of Agentic Solutions is stages. Stage 1: Determine all the required Agents and define the required tools which can be leveraged by the Agents. The tools may have access requirements which has to be handled with appropriate security constraints. In Autogen, this is supported through multiple patterns which address different requirements. At its core, Autogen provides the ability to leverage LLMs, human inputs, tools or a combination. Autogen 0.4 in particular has provided an high-level API through AgentChat with preset Agents allowing for variations in agent responses. Some of the preset Agents include 1) AssistantAgent is a built-in agent which can use a language model and tools. It can also handle multimodal messages and instructions of the Agents function. 2) UserProxyAgent: An agent that takes user input returns it as responses. 3) CodeExecutorAgent: An agent that can execute code. 4) OpenAIAssistantAgent: An agent that is backed by an OpenAI Assistant, with ability to use custom tools. 5) MultimodalWebSurfer: A multi-modal agent that can search the web and visit web pages for information. 6) FileSurfer: An agent that can search and browse local files for information. 7) VideoSurfer: An agent that can watch videos for information. A Custom Agents can be used when the preset Agents do not address the need. Stage 2: Identify the optimal interaction between the team of agents. This can include a human in the loop proxy agent which serves as an interface for human inputs. Autogen supports multiple interaction patterns 1) GroupChat is a high-level design pattern for interleaved interactions. In Auotgen 0.4, GroupChat got further abstracted with RoundRobinGroupChat or SelectorGroupChat . This means you can choose to go with abstracted options of RoundRobinGroupChat, SelectorGroupChat or customize it to your need with the base GroupChat in the core. RoundRobinGroupChat team configuration where all agents share the same context respond in a round-robin fashion. Broadcasts response to all agents, provides a consistent context. Human In the Loop - UserProxyAgent SelectorGroupChat team where participants take turns broadcasting messages to all other members. A generative model selects the next speaker based on the shared context, enabling dynamic, context-aware collaboration. selector_func argument with a custom selector function to override the default model-based selection. GroupChat in core 2) Sequential Agents Stage 3: Determine the memory and message passing between the Agents Memory can be the context for the Agent which could be the conversation history, RAG which is pulled from a ListMemory or a Custom Memory Store like a Vector DB. Messaging between Agents uses ChatMessage. This message type allows both text and multimodal communication and includes specific types such as TextMessage or MultiModalMessage. Stage 4: Articulate the Termination condition The following Termination options are available in Autogen 0.4 MaxMessageTermination: Stops after a specified number of messages have been produced, including both agent and task messages. TextMentionTermination: Stops when specific text or string is mentioned in a message (e.g., “TERMINATE”). TokenUsageTermination: Stops when a certain number of prompt or completion tokens are used. This requires the agents to report token usage in their messages. TimeoutTermination: Stops after a specified duration in seconds. HandoffTermination: Stops when a handoff to a specific target is requested. Handoff messages can be used to build patterns such as Swarm. This is useful when you want to pause the run and allow application or user to provide input when an agent hands off to them. SourceMatchTermination: Stops after a specific agent responds. ExternalTermination: Enables programmatic control of termination from outside the run. This is useful for UI integration (e.g., “Stop” buttons in chat interfaces). StopMessageTermination: Stops when a StopMessage is produced by an agent. TextMessageTermination: Stops when a TextMessage is produced by an agent. FunctionCallTermination: Stops when a ToolCallExecutionEvent containing a FunctionExecutionResult with a matching name is produced by an agent. Stage 5: Optionally manage the state This is useful in web application where stateless endpoints respond to requests and need to load the state of the application from persistent storage. The state can be saved by using the save_state() call in the AssistantAgent. assistant_agent.save_state() Finally, Logging and Serializing is also available for debugging and sharing. A well-designed Agentic Solution is crucial to be both optimal and effective in accomplishing the assigned goal. References Autogen - https://microsoft.github.io/autogen/stable/index.htmlAI Agents in Production: From Prototype to Reality - Part 10
This blog post, the tenth and final installment in a series on AI agents, focuses on deploying AI agents to production. It covers evaluating agent performance, addressing common issues, and managing costs. The post emphasizes the importance of a robust evaluation system, providing potential solutions for performance issues, and outlining cost management strategies such as response caching, using smaller models, and implementing router models.
