Introducing Azure AI Travel Agents: A Flagship MCP-Powered Sample for AI Travel Solutions
We are excited to introduce AI Travel Agents, a sample application with enterprise functionality that demonstrates how developers can coordinate multiple AI agents (written in multiple languages) to explore travel planning scenarios. It's built with LlamaIndex.TS for agent orchestration, Model Context Protocol (MCP) for structured tool interactions, and Azure Container Apps for scalable deployment. TL;DR: Experience the power of MCP and Azure Container Apps with The AI Travel Agents! Try out live demo locally on your computer for free to see real-time agent collaboration in action. Share your feedback on our community forum. We’re already planning enhancements, like new MCP-integrated agents, enabling secure communication between the AI agents and MCP servers and more. NOTE: This example uses mock data and is intended for demonstration purposes rather than production use. The Challenge: Scaling Personalized Travel Planning Travel agencies grapple with complex tasks: analyzing diverse customer needs, recommending destinations, and crafting itineraries, all while integrating real-time data like trending spots or logistics. Traditional systems falter with latency, scalability, and coordination, leading to delays and frustrated clients. The AI Travel Agents tackles these issues with a technical trifecta: LlamaIndex.TS orchestrates six AI agents for efficient task handling. MCP equips agents with travel-specific data and tools. Azure Container Apps ensures scalable, serverless deployment. This architecture delivers operational efficiency and personalized service at scale, transforming chaos into opportunity. LlamaIndex.TS: Orchestrating AI Agents The heart of The AI Travel Agents is LlamaIndex.TS, a powerful agentic framework that orchestrates multiple AI agents to handle travel planning tasks. Built on a Node.js backend, LlamaIndex.TS manages agent interactions in a seamless and intelligent manner: Task Delegation: The Triage Agent analyzes queries and routes them to specialized agents, like the Itinerary Planning Agent, ensuring efficient workflows. Agent Coordination: LlamaIndex.TS maintains context across interactions, enabling coherent responses for complex queries, such as multi-city trip plans. LLM Integration: Connects to Azure OpenAI, GitHub Models or any local LLM using Foundy Local for advanced AI capabilities. LlamaIndex.TS’s modular design supports extensibility, allowing new agents to be added with ease. LlamaIndex.TS is the conductor, ensuring agents work in sync to deliver accurate, timely results. Its lightweight orchestration minimizes latency, making it ideal for real-time applications. MCP: Fueling Agents with Data and Tools The Model Context Protocol (MCP) empowers AI agents by providing travel-specific data and tools, enhancing their functionality. MCP acts as a data and tool hub: Real-Time Data: Supplies up-to-date travel information, such as trending destinations or seasonal events, via the Web Search Agent using Bing Search. Tool Access: Connects agents to external tools, like the .NET-based customer queries analyzer for sentiment analysis, the Python-based itinerary planning for trip schedules or destination recommendation tools written in Java. For example, when the Destination Recommendation Agent needs current travel trends, MCP delivers via the Web Search Agent. This modularity allows new tools to be integrated seamlessly, future-proofing the platform. MCP’s role is to enrich agent capabilities, leaving orchestration to LlamaIndex.TS. Azure Container Apps: Scalability and Resilience Azure Container Apps powers The AI Travel Agents sample application with a serverless, scalable platform for deploying microservices. It ensures the application handles varying workloads with ease: Dynamic Scaling: Automatically adjusts container instances based on demand, managing booking surges without downtime. Polyglot Microservices: Supports .NET (Customer Query), Python (Itinerary Planning), Java (Destination Recommandation) and Node.js services in isolated containers. Observability: Integrates tracing, metrics, and logging enabling real-time monitoring. Serverless Efficiency: Abstracts infrastructure, reducing costs and accelerating deployment. Azure Container Apps' global infrastructure delivers low-latency performance, critical for travel agencies serving clients worldwide. The AI Agents: A Quick Look While MCP and Azure Container Apps are the stars, they support a team of multiple AI agents that drive the application’s functionality. Built and orchestrated with Llamaindex.TS via MCP, these agents collaborate to handle travel planning tasks: Triage Agent: Directs queries to the right agent, leveraging MCP for task delegation. Customer Query Agent: Analyzes customer needs (emotions, intents), using .NET tools. Destination Recommendation Agent: Suggests tailored destinations, using Java. Itinerary Planning Agent: Crafts efficient itineraries, powered by Python. Web Search Agent: Fetches real-time data via Bing Search. These agents rely on MCP’s real-time communication and Azure Container Apps’ scalability to deliver responsive, accurate results. It's worth noting though this sample application uses mock data for demonstration purpose. In real worl scenario, the application would communicate with an MCP server that is plugged in a real production travel API. Key Features and Benefits The AI Travel Agents offers features that showcase the power of MCP and Azure Container Apps: Real-Time Chat: A responsive Angular UI streams agent responses via MCP’s SSE, ensuring fluid interactions. Modular Tools: MCP enables tools like analyze_customer_query to integrate seamlessly, supporting future additions. Scalable Performance: Azure Container Apps ensures the UI, backend and the MCP servers handle high traffic effortlessly. Transparent Debugging: An accordion UI displays agent reasoning providing backend insights. Benefits: Efficiency: LlamaIndex.TS streamlines operations. Personalization: MCP’s data drives tailored recommendations. Scalability: Azure ensures reliability at scale. Thank You to Our Contributors! The AI Travel Agents wouldn’t exist without the incredible work of our contributors. Their expertise in MCP development, Azure deployment, and AI orchestration brought this project to life. A special shoutout to: Pamela Fox – Leading the developement of the Python MCP server. Aaron Powell and Justin Yoo – Leading the developement of the .NET MCP server. Rory Preddy – Leading the developement of the Java MCP server. Lee Stott and Kinfey Lo – Leading the developement of the Local AI Foundry Anthony Chu and Vyom Nagrani – Leading Azure Container Apps roadmap Matt Soucoup and Julien Dubois – Leading the ACA DevRel strategy Wassim Chegham – Architected MCP and backend orchestration. And many more! See the GitHub repository for all contributors. Thank you for your dedication to pushing the boundaries of AI and cloud technology! Try It Out Experience the power of MCP and Azure Container Apps with The AI Travel Agents! Try out live demo locally on your computer for free to see real-time agent collaboration in action. Conclusion Developers can explore today the open-source project on GitHub, with setup and deployment instructions. Share your feedback on our community forum. We’re already planning enhancements, like new MCP-integrated agents, enabling secure communication between the AI agents and MCP servers and more. This is still a work in progress and we also welcome all kind of contributions. Please fork and star the repo to stay tuned for updates! ◾️We would love your feedback and continue the discussion in the Azure AI Foundry Discord aka.ms/foundry/discord On behalf of Microsoft DevRel Team.Swagger Auto-Generation on MCP Server
Would you like to generate a swagger.json directly on an MCP server on-the-fly? In many use cases, using remote MCP servers is not uncommon. In particular, if you're using Azure API Management (APIM), Azure API Center (APIC) or Copilot Studio in Power Platform, integrating with remote MCP servers is inevitable.How to use any Python AI agent framework with free GitHub Models
I ❤️ when companies offer free tiers for developer services, since it gives everyone a way to learn new technologies without breaking the bank. Free tiers are especially important for students and people between jobs, when the desire to learn is high but the available cash is low. That's why I'm such a fan of GitHub Models: free, high-quality generative AI models available to anyone with a GitHub account. The available models include the latest OpenAI LLMs (like o3-mini), LLMs from the research community (like Phi and Llama), LLMs from other popular providers (like Mistral and Jamba), multimodal models (like gpt-4o and llama-vision-instruct) and even a few embedding models (from OpenAI and Cohere). With access to such a range of models, you can prototype complex multi-model workflows to improve your productivity or heck, just make something fun for yourself. 🤗 To use GitHub Models, you can start off in no-code mode: open the playground for a model, send a few requests, tweak the parameters, and check out the answers. When you're ready to write code, select "Use this model". A screen will pop up where you can select a programming language (Python/JavaScript/C#/Java/REST) and select an SDK (which varies depending on model). Then you'll get instructions and code for that model, language, and SDK. But here's what's really cool about GitHub Models: you can use them with all the popular Python AI frameworks, even if the framework has no specific integration with GitHub Models. How is that possible? The vast majority of Python AI frameworks support the OpenAI Chat Completions API, since that API became a defacto standard supported by many LLM API providers besides OpenAI itself. GitHub Models also provide OpenAI-compatible endpoints for chat completion models. Therefore, any Python AI framework that supports OpenAI-like models can be used with GitHub Models as well. 🎉 To prove it, I've made a new repository with examples from eight different Python AI agent packages, all working with GitHub Models: python-ai-agent-frameworks-demos. There are examples for AutoGen, LangGraph, Llamaindex, OpenAI Agents SDK, OpenAI standard SDK, PydanticAI, Semantic Kernel, and SmolAgents. You can open that repository in GitHub Codespaces, install the packages, and get the examples running immediately. Now let's walk through the API connection code for GitHub Models for each framework. Even if I missed your favorite framework, I hope my tips here will help you connect any framework to GitHub Models. OpenAI I'll start with openai , the package that started it all! import openai client = openai.OpenAI( api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com") The code above demonstrates the two key parameters we'll need to configure for all frameworks: api_key : When using OpenAI.com, you pass your OpenAI API key here. When using GitHub Models, you pass in a Personal Access Token (PAT). If you open the repository (or any repository) in GitHub Codespaces, a PAT is already stored in the GITHUB_TOKEN environment variable. However, if you're working locally with GitHub Models, you'll need to generate a PAT yourself and store it. PATs expire after a while, so you need to generate new PATs every so often. base_url : This parameter tells the OpenAI client to send all requests to "https://models.inference.ai.azure.com" instead of the OpenAI.com API servers. That's the domain that hosts the OpenAI-compatible endpoint for GitHub Models, so you'll always pass that domain as the base URL. If we're working with the new openai-agents SDK, we use very similar code, but we must use the AsyncOpenAI client from openai instead. Lately, Python AI packages are defaulting to async, because it's so much better for performance. import agents import openai client = openai.AsyncOpenAI( base_url="https://models.inference.ai.azure.com", api_key=os.environ["GITHUB_TOKEN"]) model = agents.OpenAIChatCompletionsModel( model="gpt-4o", openai_client=client) spanish_agent = agents.Agent( name="Spanish agent", instructions="You only speak Spanish.", model=model) PydanticAI Now let's look at all of the packages that make it really easy for us, by allowing us to directly bring in an instance of either OpenAI or AsyncOpenAI . For PydanticAI, we configure an AsyncOpenAI client, then construct an OpenAIModel object from PydanticAI, and pass that model to the agent: import openai import pydantic_ai import pydantic_ai.models.openai client = openai.AsyncOpenAI( api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com") model = pydantic_ai.models.openai.OpenAIModel( "gpt-4o", provider=OpenAIProvider(openai_client=client)) spanish_agent = pydantic_ai.Agent( model, system_prompt="You only speak Spanish.") Semantic Kernel For Semantic Kernel, the code is very similar. We configure an AsyncOpenAI client, then construct an OpenAIChatCompletion object from Semantic Kernel, and add that object to the kernel. import openai import semantic_kernel.connectors.ai.open_ai import semantic_kernel.agents chat_client = openai.AsyncOpenAI( api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com") chat = semantic_kernel.connectors.ai.open_ai.OpenAIChatCompletion( ai_model_id="gpt-4o", async_client=chat_client) kernel.add_service(chat) spanish_agent = semantic_kernel.agents.ChatCompletionAgent( kernel=kernel, name="Spanish agent" instructions="You only speak Spanish") AutoGen Next, we'll check out a few frameworks that have their own wrapper of the OpenAI clients, so we won't be using any classes from openai directly. For AutoGen, we configure both the OpenAI parameters and the model name in the same object, then pass that to each agent: import autogen_ext.models.openai import autogen_agentchat.agents client = autogen_ext.models.openai.OpenAIChatCompletionClient( model="gpt-4o", api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com") spanish_agent = autogen_agentchat.agents.AssistantAgent( "spanish_agent", model_client=client, system_message="You only speak Spanish") LangGraph For LangGraph, we configure a very similar object, which even has the same parameter names: import langchain_openai import langgraph.graph model = langchain_openai.ChatOpenAI( model="gpt-4o", api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com", ) def call_model(state): messages = state["messages"] response = model.invoke(messages) return {"messages": [response]} workflow = langgraph.graph.StateGraph(MessagesState) workflow.add_node("agent", call_model) SmolAgents Once again, for SmolAgents, we configure a similar object, though with slightly different parameter names: import smolagents model = smolagents.OpenAIServerModel( model_id="gpt-4o", api_key=os.environ["GITHUB_TOKEN"], api_base="https://models.inference.ai.azure.com") agent = smolagents.CodeAgent(model=model) Llamaindex I saved Llamaindex for last, as it is the most different. The llama-index package has a different constructor for OpenAI.com versus OpenAI-like servers, so I opted to use that OpenAILike constructor instead. However, I also needed an embeddings model for my example, and the package doesn't have an OpenAIEmbeddingsLike constructor, so I used the standard OpenAIEmbedding constructor. import llama_index.embeddings.openai import llama_index.llms.openai_like import llama_index.core.agent.workflow Settings.llm = llama_index.llms.openai_like.OpenAILike( model="gpt-4o", api_key=os.environ["GITHUB_TOKEN"], api_base="https://models.inference.ai.azure.com", is_chat_model=True) Settings.embed_model = llama_index.embeddings.openai.OpenAIEmbedding( model="text-embedding-3-small", api_key=os.environ["GITHUB_TOKEN"], api_base="https://models.inference.ai.azure.com") agent = llama_index.core.agent.workflow.ReActAgent( tools=query_engine_tools, llm=Settings.llm) Choose your models wisely! In all of the examples above, I specified the gpt-4o model. The gpt-4o model is a great choice for agents because it supports function calling, and many agent frameworks only work (or work best) with models that natively support function calling. Fortunately, GitHub Models includes multiple models that support function calling, at least in my basic experiments: gpt-4o gpt-4o-mini o3-mini AI21-Jamba-1.5-Large AI21-Jamba-1.5-Mini Codestral-2501 Cohere-command-r Ministral-3B Mistral-Large-2411 Mistral-Nemo Mistral-small You might find that some models work better than others, especially if you're using agents with multiple tools. With GitHub Models, it's very easy to experiment and see for yourself, by simply changing the model name and re-running the code. Join the AI Agents Hackathon We are currently running a free virtual hackathon from April 8th - 30th, to challenge developers to create agentic applications using Microsoft technologies. You could build an agent entirely using GitHub Models and submit it to the hackathon for a chance to win amazing prizes! You can also join our 30+ streams about building AI agents, including a stream all about prototyping with GitHub Models. Learn more and register at https://aka.ms/agentshackModel Context Protocol (MCP) Server for Azure Database for MySQL
We are excited to introduce a new MCP Server for integrating your AI models with data hosted in Azure Database for MySQL. By utilizing this server, you can effortlessly connect any AI application that supports MCP to your MySQL flexible server (using either MySQL password-based authentication or Microsoft Entra authentication methods), enabling you to provide your business data as meaningful context in a standardized and secure manner.
Tools in Teams AI Library
Hello Team, In LLM we use tools like DynamicTool, DynamicStructuredTool to determine when to call which function and with what parameters. How to do the same using Teams AI Library? My Use Case: I want to find the intent from the user query and based on that intent further call the respective function/tool to perform respective action. How can I achieve this using Teams AI Library? I went through the samples mentioned here: https://github.com/microsoft/teams-ai/blob/main/js/samples/04.ai-apps/ but couldn't find anything similar.Week 2 . Microsoft Agents Hack Online Events and Readiness Resources
https://aka.ms/agentshack 2025 is the year of AI agents! But what exactly is an agent, and how can you build one? Whether you're a seasoned developer or just starting out, this FREE three-week virtual hackathon is your chance to dive deep into AI agent development. Register Now: https://aka.ms/agentshack 🔥 Learn from expert-led sessions streamed live on YouTube, covering top frameworks like Semantic Kernel, Autogen, the new Azure AI Agents SDK and the Microsoft 365 Agents SDK. Week 2 Events: April 14th-18th Day/Time Topic Track 4/14 08:00 AM PT Building custom engine agents with Azure AI Foundry and Visual Studio Code Copilots 4/15 07:00 AM PT Your first AI Agent in JS with Azure AI Agent Service JS 4/15 09:00 AM PT Building Agentic Applications with AutoGen v0.4 Python 4/15 12:00 PM PT AI Agents + .NET Aspire C# 4/15 03:00 PM PT Prototyping AI Agents with GitHub Models Python 4/16 04:00 AM PT Multi-agent AI apps with Semantic Kernel and Azure Cosmos DB C# 4/16 06:00 AM PT Building declarative agents with Microsoft Copilot Studio & Teams Toolkit Copilots 4/16 07:00 AM PT Prompting is the New Scripting: Meet GenAIScript JS 4/16 09:00 AM PT Building agents with an army of models from the Azure AI model catalog Python 4/16 12:00 PM PT Multi-Agent API with LangGraph and Azure Cosmos DB Python 4/16 03:00 PM PT Mastering Agentic RAG Python 4/17 06:00 AM PT Build your own agent with OpenAI, .NET, and Copilot Studio C# 4/17 09:00 AM PT Building smarter Python AI agents with code interpreters Python 4/17 12:00 PM PT Building Java AI Agents using LangChain4j and Dynamic Sessions Java 4/17 03:00 PM PT Agentic Voice Mode Unplugged PythonQuest 6 - I want to build an AI Agent
Quest 6 of the JS AI Build-a-thon marks a major milestone — building your first intelligent AI agent using the Azure AI Foundry VS Code extension. In this quest, you’ll design, test, and integrate an agent that can use tools like Bing Search, respond to goals, and adapt in real-time. With updated instructions, real-world workflows, and powerful tooling, this is where your AI app gets truly smart.European AI and Cloud Summit 2025
Dusseldorf , Germany added 3,000+ tech enthusiast and hosted the Microsoft for Startups Cloud AI Pitch Competition between May 26-28, 2025. We were pleased to attend the European AI Cloud and Collaboration Summits and Biz Apps Summit– to participate and to gain so much back from everyone. It was a packed week filled with insights, feedback, and fun. Below is a recap of various aspects of the event - across keynotes, general sessions, breakout sessions, and the Expo Hall. The event in a nutshell: 3,000+ attendees 237 speakers overall – 98 Microsoft Valued Professionals, 16 Microsoft Valued Professional Regional Directorss, 51 from Microsoft Product Groups and Engineering 306 sessions | 13 tutorials (workshops) 70 sponsors | One giant Expo Hall PreDay Workshop AI Beginner Development Powerclass This workshop is designed to give you a hands-on introduction to the core concepts and best practices for interacting with OpenAI models in Azure AI Foundry portal. Innovate with Azure OpenAI's GPT-4o multimodal model in this hands-on experience in Azure AI Foundry. Learn the core concepts and best practices to effectively generate with text, sound, and images using GPT-4o-mini, DALL-E and GPT-4o-realtime. Create AI assistants that enhance user experiences and drive innovation. Workshop for you azure-ai-foundry/ai-tutorials: This repo includes a collection of tutorials to help you get started with building Generative AI applications using Azure AI Foundry. Each tutorial is designed to be self-contained and provides step-by-step instructions to guide you in the development process. Keynotes & general sessions Day 1 Keynote The Future of AI Is Already Here, Marco Casalaina, VP Products of Azure AI and AI Futurist at Microsoft This session discussed the new and revolutionary changes that you're about to see in AI - and how many of them are available for you to try now. Marco shared how AI is becoming ubiquitous, multimodal, multilingual, and autonomous, and how it will change our lives and our businesses. This session covered: • Incredible advances in multilingual AI • How Copilot (and every AI) are grounded to data, and how we do it in Azure OpenAI • Responsible AI, including evaluation for correctness, and real time content safety • The rise of AI Agents • And how AI is going to move from question-answering to taking action Day 2 Keynote Leveraging Microsoft AI: Navigating the EU AI Act and Unlocking Future Opportunities, Azar Koulibaly, General Manager and Associate General Counsel This session was for developers and business decision makers, Azar set the stage for Microsoft’s advancements in AI and how they align with the latest regulatory framework. Exploring the EU AI Act, its key components, and its implications for AI development and deployment within the European Union. The audience gained a comprehensive understanding of the EU AI Act's objectives, including the promotion of trustworthy AI, the mitigation of risks, and the enhancement of transparency and accountability. Learning aboutMicrosoft's Cloud and AI Services provide robust support for compliance with these new regulations, ensuring that your AI projects are both innovative and legally sound and Microsoft trust center resources. He delved into the opportunities that come with using Microsoft’s state-of-the-art tools, services, and technologies. Discover how partnering with Microsoft can accelerate your AI initiatives, drive business growth, and create competitive advantages in an evolving regulatory landscape. Join us to unlock the full potential of AI while navigating the complexities of the EU AI Act with confidence. General Sessions It is crucial to ensure your organization is technically ready for the full potential of AI. The sessions focused on technical readiness and ensuring you have the latest guidance. Our experts will shared the best practices and provide guidance on how to leverage AI and Azure AI Foundry to maximize the benefits of Agents, LLM and Generative within your organization. Expo Hall + Cloud AI Statup Stage + tutorials The Expo Hall was Buzzing with demos, discussions, interviews, podcasts, lightning talks, popcorn, catering trucks, cotton candy, SWAG, prizes, and community. There was a busy Cloud AI StartupStage, a Business Stage, and shorter talks delivered in front of a shiny airstream trailer. Cloud AI Startup Stage This was a highly informative and engaging event focused on Artificial Intelligence (AI) and its potential for startups. The Microsoft for Startups is a platform to provide startups with the resources, tools, and support they need to succeed. This portion of the event offered value to budding entrepreneurs and established startups looking to scale. For example, on day 2, we focused on accelerating innovation with Microsoft Founders Hub and Azure AI. Startups could kickstart their journey with Azure credits and gain access to 30+ tools and services, benefiting from additional credits and offerings as they evolve. It’s a great way for Startups to navigate technical and business challenges. Cloud AI Startup Pitch The Microsoft for Startups AI Pitch Competition and Startup Stage at European Cloud Summit 2025 This was a highly informative and engaging event focused on Artificial Intelligence (AI) and its potential for startups. The Microsoft Startup Programme was introduced as a platform that provides startups with the resources, tools, and support they need to succeed. The AI Empowerment session provided an in-depth overview of the various AI services available through Microsoft Azure AI Foundry and how these cutting-edge technologies can be integrated into business operations. This was perfect for startups looking to get started with AI or those interested in joining the Microsoft Startup Programme. The Spotlight on Innovation session showcased innovative startups from the European Cloud Summit, giving attendees a unique insight into the cutting-edge ideas that are shaping our future. The Empowering Innovation session featured a panel of experts and successful startup founders sharing insights on leveraging Microsoft technologies, navigating the startup ecosystem, and securing funding. This was valuable for budding entrepreneurs or established startups looking to scale. Startup Showcases Holistic AI - End to End AI Governance Platform, Raj Bharat Patel, Securing Advantage in the Era of Agentic AI With this autonomy comes high variability: the difference between a minor efficiency and a major one could mean millions in savings. Conversely, a seemingly small misstep could cascade into catastrophic reputational or compliance risk. The stakes are high—but so is the potential. The next frontier introduces a new paradigm: AI managing AI. As organizations deploy swarms of autonomous agents across business functions, the challenge expands beyond governing human-AI interactions. Now, it's about ensuring that AI agents can monitor, evaluate, and optimize each other—in real time, at scale. This demands a shift in both architecture and mindset: toward native-AI platforms and a new human role, moving from human-in-the-loop to human-on-the-loop—strategically overseeing autonomous systems, not micromanaging them. In this session, Raj Bharat Patel will explore how forward-thinking enterprises can prepare for this emerging reality—building governance and orchestration infrastructures that enable scale, speed, and safety in the age of agentic AI. D-ID | The #1 Choice for AI Generated Video Creation Platform, Yaniv Levi In a world increasingly powered by AI, how do we make digital experiences feel more human? At D-ID we enable businesses to create lifelike, interactive avatars that are transforming the way users communicate with AI, making it more intuitive, personal, and memorable. In this session, we’ll share how our collaboration with Microsoft for Startups helped us scale and innovate, enabling seamless integration with Microsoft’s tools to enhance customer-facing AI agents, and LLM-powered solutions while adding a powerful and personalized new layer of humanlike expression. Startup Pitch Competition Day 2 of the event focused on accelerating innovation with Microsoft Starups and Azure AI Foundry. Startups can kickstart their journey with Azure credits and gain access to 30+ tools and services, benefiting from additional credits and offerings as they evolve. The Azure AI Foundry provides access to an extensive range of AI models, including OpenAI, Meta, Nvidia, Hugging Face. Moreover, startups can navigate through technical and business challenges with the help of free 1:1 sessions with Microsoft experts. The Cloud Startup Stage Competition showcased the most innovative startups in the Microsoft Azure and Azure OpenAI ecosystem, highlighting their groundbreaking solutions and business models. This was a celebration of innovation and success and provided insights into the experiences, challenges, and future plans of these startups The Judges The Pitches . The Winners 1 st Place Graia 2 nd Place iThink365 3 rd Place ShArc Overall, this event was highly informative, engaging, and valuable for anyone interested in AI and its potential for startups. The Microsoft Startup Programme and Azure AI Foundry are powerful tools that can help startups achieve success and transform their ideas into successful businesses. In the end... We are grateful for this year's active and engaging #CollabSummit & #CloudSummit #BizAppSummit— so much goodness, caring, learning, and fun! Great questions, stories, understanding of your concerns, and the sharing in fun. Thank you and see you next year! We look forward to seeing in Cololgne - May 5-7, 2025 – Collaboration Summit (@CollabSummit), Cloud Summit (@EUCloudSummit), and BizApps Summit (@BizAppsSummit) and continue the discussion around AI in our Azure AI Discord CommunityBuild AI Agents for Postgres with Azure AI Agent Service
Agents can be implemented using various GenAI (Generative AI) frameworks, including LangChain, LangGraph, LlamaIndex, and Semantic Kernel. All these frameworks support using Neon. Implementing AI-driven functions or tools calling requires hundreds of lines of code—now, it takes just a few with the new Azure AI Agent Service. AI Agent Service is a part of Azure AI Foundry, and one can create agents without writing code or using Azure AI SDKs. An AI Agent works like a smart assistant that can answer questions, perform tasks, and automate workflows. It uses AI models from Azure OpenAI, external tools, and databases that help it access and interact with real-world data. AI agents can now set up things like databases on their own. Since they’re already creating new databases every few seconds faster than humans, it looks like they’ll help run a lot of the internet’s backend in the future. For example, an AI agent running a customer support chatbot can instantly set up a new Postgres database to log chat history and user feedback. Or when a new region is added on Azure, the agent can spin up a local database in seconds to keep everything fast and organized. In this article, we will discover how to use Neon Serverless Postgres with the Azure AI Agent service and demonstrate it with a sample project that interacts with your Neon projects, databases, and branching just by typing simple human-like commands. Try out the Neon API Management AI Agent console app source code to see how it works! Why Use Azure AI Agent Service with Neon? Azure AI Agent Service is a fully managed solution that helps developers create, deploy, and scale AI agents without worrying about infrastructure. Azure AI Agent Service handles the entire tool-calling process by following three steps: Executing the model based on the given instructions Automatically calling the required tools, and Delivering the results back to you. Key Features: Smart microservices: AI Agents can answer questions, execute functions, and automate workflows. Built-in tools: Access real-world data from Azure Search, Bing, and more. Seamless API integration: Use Azure AI Foundry SDK or OpenAI SDKs to create and run AI agents. Fully managed: No need to handle computing or storage manually. Neon can store vector embeddings of documents and perform fast similarity searches. Using the pgvector extension, you can efficiently search and retrieve relevant data for AI models to enable more accurate and context-aware responses. One of Neon’s most shining features is database branching, which allows AI Agents to: Create separate environments for testing without affecting production. Experiment with AI models and data without risk. Quickly restore previous versions of the database. Finally, Neon API provides full programmatic control over database creation, updates, and deletions. This is perfect for AI-driven workflows where databases need to be dynamically created for different AI agents or users. Neon API Management AI Agent Imagine you want to create a new database. Instead of going to the Neon Console or writing API calls, you can simply type something like, “Create a database called ‘my-new-database’.” Or, if you want to see what databases you have, just say, “Show me my databases.” That’s exactly what the Neon Azure AI Agent lets you do. We will break down how to create, configure, and integrate an AI agent using the Neon Management API and Azure AI Agent Service. Get Started with Azure AI Agent Service To begin using Azure AI Agent Service, you first need to set up an AI Foundry hub and create an Agent project in your Azure subscription. If you’re new to the service, check out the quickstart guide for a step-by-step introduction. Once your AI hub and project are created, you can deploy a compatible AI model, such as GPT-4o. After deploying the model, you’ll be able to interact with the service and make API calls using the available SDKs. In our example project, we used the Azure AI Projects client library for Python to create an agent, assigning Neon Management API functions and managing message threads. Step 1: Create an AI Project Client First, initialize an AIProjectClient using Azure credentials. This client is responsible for managing AI agents, threads, and interactions. # ai-gent.py project_client = AIProjectClient.from_connection_string( credential=DefaultAzureCredential(), conn_str=os.environ["PROJECT_CONNECTION_STRING"], ) Step 2: Define Neon API Functions Next, define the Neon API functions that the AI Agent will use. These functions allow the AI to perform database-related actions, such as creating a project, listing databases, and managing branches. # neon_functions.py from neon_api import NeonAPI import os from dotenv import load_dotenv load_dotenv() neon = NeonAPI(api_key=os.environ["NEON_API_KEY"]) def create_project(project_name: str): try: response = neon.project_create( project={ "name": project_name, "pg_version": 17, # Fixed typo: removed extra colon "region_id": "azure-eastus2", } ) return format_action_response(response) except Exception as e: return f"Error creating project: {str(e)}" user_functions: Set[Callable[..., Any]] = { create_project } ... Step 3: Register Function Tools Once the functions are defined, we register them as tools that the AI agent can call when processing user requests. # ai-gent.py functions = FunctionTool(user_functions) toolset = ToolSet() toolset.add(functions) Step 4: Create the AI Agent Now, we create an AI agent that understands database management tasks and can execute Neon API functions. # ai-gent.py agent = project_client.agents.create_agent( model=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"], name=f"neon-db-agent-{datetime.now().strftime('%Y%m%d%H%M')}", description="AI Agent for managing Neon databases and running SQL queries.", instructions=f""" You are an AI assistant that helps users create and manage Neon projects, databases, and branches. Use the provided functions to perform actions. The current date is {datetime.now().strftime("%Y-%m-%d")}. """, toolset=toolset, ) print(f"✅ Created agent, ID: {agent.id}") Step 5: Create a Thread for User Interactions The AI Agent needs a thread to interact with users. This thread stores all messages and interactions between the user and the AI. # ai-gent.pythread = project_client.agents.create_thread()print(f"✅ Created thread, ID: {thread.id}") Step 6: Process User Commands When a user sends a command (e.g., “Create a Neon Project”), the AI agent processes it by creating a message, running the request, and returning a response. # ai-gent.py thread = project_client.agents.create_thread() print(f"✅ Created thread, ID: {thread.id}") Step 7: Accept User Commands Finally, the program continuously listens for user input, allowing users to issue commands like “Create a database” or “List projects”. # ai-gent.py def process_command(command: str): message = project_client.agents.create_message( thread_id=thread.id, role="user", content=command, ) print(f"✅ Created message, ID: {message.id}") run = project_client.agents.create_and_process_run( thread_id=thread.id, agent_id=agent.id ) print(f"✅ Run finished with status: {run.status}") if run.status == "failed": print(f"❌ Run failed: {run.last_error}") else: messages = project_client.agents.list_messages(thread_id=thread.id) print(f"📜 Messages: {messages['data'][0]['content'][0]['text']['value']}") Step 8: Run the AI Agent Start the AI agent and interact with Neon API: python ai-gent.py Example Commands After running the script, you can enter commands in the console: Create a project: Create a project called My Neon Project List Projects : List existing projects Conclusion Throughout the article, we learned how to create a dedicated CLI interface that makes it easy for AI agents to deploy and manage database projects. AI agents are now creating more databases on Neon than humans, provisioning thousands of new databases every day. By connecting AI agents to Neon databases, agents can deliver more accurate, context-aware responses based on your data. Ready to see it in action? Try out the Neon API & Azure AI Agent demo today!
