cosmos_vnet_blocked error with BYO standard agent setup
Hi! We've tried deploying the standard agent setup using terraform as described in the https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/virtual-networks?view=foundry-classic and using the terraform sample available https://github.com/azure-ai-foundry/foundry-samples/tree/main/infrastructure/infrastructure-setup-terraform/15a-private-network-standard-agent-setup/code as a basis to give the necessary support in our codebase. However we keep getting the following error: cosmos_vnet_blocked: Access to Cosmos DB is blocked due to VNET configuration. Please check your network settings and make sure CosmosDB is public network enabled, if this is a public standard agent setup. Has anyone experienced this error?Smart Auditing: Leveraging Azure AI Agents to Transform Financial Oversight
In today's data-driven business environment, audit teams often spend weeks poring over logs and databases to verify spending and billing information. This time-consuming process is ripe for automation. But is there a way to implement AI solutions without getting lost in complex technical frameworks? While tools like LangChain, Semantic Kernel, and AutoGen offer powerful AI agent capabilities, sometimes you need a straightforward solution that just works. So, what's the answer for teams seeking simplicity without sacrificing effectiveness? This tutorial will show you how to use Azure AI Agent Service to build an AI agent that can directly access your Postgres database to streamline audit workflows. No complex chains or graphs required, just a practical solution to get your audit process automated quickly. The Auditing Challenge: It's the month end, and your audit team is drowning in spreadsheets. As auditors reviewing financial data across multiple SaaS tenants, you're tasked with verifying billing accuracy by tracking usage metrics like API calls, storage consumption, and user sessions in Postgres databases. Each tenant generates thousands of transactions daily, and traditionally, this verification process consumes weeks of your team's valuable time. Typically, teams spend weeks: Manually extracting data from multiple database tables. Cross-referencing usage with invoices. Investigating anomalies through tedious log analysis. Compiling findings into comprehensive reports. With an AI-powered audit agent, you can automate these tasks and transform the process. Your AI assistant can: Pull relevant usage data directly from your database Identify billing anomalies like unexpected usage spikes Generate natural language explanations of findings Create audit reports that highlight key concerns For example, when reviewing a tenant's invoice, your audit agent can query the database for relevant usage patterns, summarize anomalies, and offer explanations: "Tenant_456 experienced a 145% increase in API usage on April 30th, which explains the billing increase. This spike falls outside normal usage patterns and warrants further investigation." Let’s build an AI agent that connects to your Postgres database and transforms your audit process from manual effort to automated intelligence. Prerequisites: Before we start building our audit agent, you'll need: An Azure subscription (Create one for free). The Azure AI Developer RBAC role assigned to your account. Python 3.11.x installed on your development machine. OR You can also use GitHub Codespaces, which will automatically install all dependencies for you. You’ll need to create a GitHub account first if you don’t already have one. Setting Up Your Database: For this tutorial, we'll use Neon Serverless Postgres as our database. It's a fully managed, cloud-native Postgres solution that's free to start, scales automatically, and works excellently for AI agents that need to query data on demand. Creating a Neon Database on Azure: Open the Neon Resource page on the Azure portal Fill out the form with the required fields and deploy your database After creation, navigate to the Neon Serverless Postgres Organization service Click on the Portal URL to access the Neon Console Click "New Project" Choose an Azure region Name your project (e.g., "Audit Agent Database") Click "Create Project" Once your project is successfully created, copy the Neon connection string from the Connection Details widget on the Neon Dashboard. It will look like this: postgresql://[user]:[password]@[neon_hostname]/[dbname]?sslmode=require Note: Keep this connection string saved; we'll need it shortly. Creating an AI Foundry Project on Azure: Next, we'll set up the AI infrastructure to power our audit agent: Create a new hub and project in the Azure AI Foundry portal by following the guide. Deploy a model like GPT-4o to use with your agent. Make note of your Project connection string and Model Deployment name. You can find your connection string in the overview section of your project in the Azure AI Foundry portal, under Project details > Project connection string. Once you have all three values on hand: Neon connection string, Project connection string, and Model Deployment Name, you are ready to set up the Python project to create an Agent. All the code and sample data are available in this GitHub repository. You can clone or download the project. Project Environment Setup: Create a .env file with your credentials: PROJECT_CONNECTION_STRING="<Your AI Foundry connection string> "AZURE_OPENAI_DEPLOYMENT_NAME="gpt4o" NEON_DB_CONNECTION_STRING="<Your Neon connection string>" Create and activate a virtual environment: python -m venv .venv source .venv/bin/activate # on macOS/Linux .venv\Scripts\activate # on Windows Install required Python libraries: pip install -r requirements.txt Example requirements.txt: Pandas python-dotenv sqlalchemy psycopg2-binary azure-ai-projects ==1.0.0b7 azure-identity Load Sample Billing Usage Data: We will use a mock dataset for tenant usage, including computed percent change in API calls and storage usage in GB: tenant_id date api_calls storage_gb tenant_456 2025-04-01 1000 25.0 tenant_456 2025-03-31 950 24.8 tenant_456 2025-03-30 2200 26.0 Run python load_usage_data.py Python script to create and populate the usage_data table in your Neon Serverless Postgres instance: # load_usage_data.py file import os from dotenv import load_dotenv from sqlalchemy import ( create_engine, MetaData, Table, Column, String, Date, Integer, Numeric, ) # Load environment variables from .env load_dotenv() # Load connection string from environment variable NEON_DB_URL = os.getenv("NEON_DB_CONNECTION_STRING") engine = create_engine(NEON_DB_URL) # Define metadata and table schema metadata = MetaData() usage_data = Table( "usage_data", metadata, Column("tenant_id", String, primary_key=True), Column("date", Date, primary_key=True), Column("api_calls", Integer), Column("storage_gb", Numeric), ) # Create table with engine.begin() as conn: metadata.create_all(conn) # Insert mock data conn.execute( usage_data.insert(), [ { "tenant_id": "tenant_456", "date": "2025-03-27", "api_calls": 870, "storage_gb": 23.9, }, { "tenant_id": "tenant_456", "date": "2025-03-28", "api_calls": 880, "storage_gb": 24.0, }, { "tenant_id": "tenant_456", "date": "2025-03-29", "api_calls": 900, "storage_gb": 24.5, }, { "tenant_id": "tenant_456", "date": "2025-03-30", "api_calls": 2200, "storage_gb": 26.0, }, { "tenant_id": "tenant_456", "date": "2025-03-31", "api_calls": 950, "storage_gb": 24.8, }, { "tenant_id": "tenant_456", "date": "2025-04-01", "api_calls": 1000, "storage_gb": 25.0, }, ], ) print("✅ usage_data table created and mock data inserted.") Create a Postgres Tool for the Agent: Next, we configure an AI agent tool to retrieve data from Postgres. The Python script billing_agent_tools.py contains: The function billing_anomaly_summary() that: Pulls usage data from Neon. Computes % change in api_calls. Flags anomalies with a threshold of > 1.5x change. Exports user_functions list for the Azure AI Agent to use. You do not need to run it separately. # billing_agent_tools.py file import os import json import pandas as pd from sqlalchemy import create_engine from dotenv import load_dotenv # Load environment variables load_dotenv() # Set up the database engine NEON_DB_URL = os.getenv("NEON_DB_CONNECTION_STRING") db_engine = create_engine(NEON_DB_URL) # Define the billing anomaly detection function def billing_anomaly_summary( tenant_id: str, start_date: str = "2025-03-27", end_date: str = "2025-04-01", limit: int = 10, ) -> str: """ Fetches recent usage data for a SaaS tenant and detects potential billing anomalies. :param tenant_id: The tenant ID to analyze. :type tenant_id: str :param start_date: Start date for the usage window. :type start_date: str :param end_date: End date for the usage window. :type end_date: str :param limit: Maximum number of records to return. :type limit: int :return: A JSON string with usage records and anomaly flags. :rtype: str """ query = """ SELECT date, api_calls, storage_gb FROM usage_data WHERE tenant_id = %s AND date BETWEEN %s AND %s ORDER BY date DESC LIMIT %s; """ df = pd.read_sql(query, db_engine, params=(tenant_id, start_date, end_date, limit)) if df.empty: return json.dumps( {"message": "No usage data found for this tenant in the specified range."} ) df.sort_values("date", inplace=True) df["pct_change_api"] = df["api_calls"].pct_change() df["anomaly"] = df["pct_change_api"].abs() > 1.5 return df.to_json(orient="records") # Register this in a list to be used by FunctionTool user_functions = [billing_anomaly_summary] Create and Configure the AI Agent: Now we'll set up the AI agent and integrate it with our Neon Postgres tool using the Azure AI Agent Service SDK. The Python script does the following: Creates the agent Instantiates an AI agent using the selected model (gpt-4o, for example), adds tool access, and sets instructions that tell the agent how to behave (e.g., “You are a helpful SaaS assistant…”). Creates a conversation thread A thread is started to hold a conversation between the user and the agent. Posts a user message Sends a question like “Why did my billing spike for tenant_456 this week?” to the agent. Processes the request The agent reads the message, determines that it should use the custom tool to retrieve usage data, and processes the query. Displays the response Prints the response from the agent with a natural language explanation based on the tool’s output. # billing_anomaly_agent.py import os from datetime import datetime from azure.ai.projects import AIProjectClient from azure.identity import DefaultAzureCredential from azure.ai.projects.models import FunctionTool, ToolSet from dotenv import load_dotenv from pprint import pprint from billing_agent_tools import user_functions # Custom tool function module # Load environment variables from .env file load_dotenv() # Create an Azure AI Project Client project_client = AIProjectClient.from_connection_string( credential=DefaultAzureCredential(), conn_str=os.environ["PROJECT_CONNECTION_STRING"], ) # Initialize toolset with our user-defined functions functions = FunctionTool(user_functions) toolset = ToolSet() toolset.add(functions) # Create the agent agent = project_client.agents.create_agent( model=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"], name=f"billing-anomaly-agent-{datetime.now().strftime('%Y%m%d%H%M')}", description="Billing Anomaly Detection Agent", instructions=f""" You are a helpful SaaS financial assistant that retrieves and explains billing anomalies using usage data. The current date is {datetime.now().strftime("%Y-%m-%d")}. """, toolset=toolset, ) print(f"Created agent, ID: {agent.id}") # Create a communication thread thread = project_client.agents.create_thread() print(f"Created thread, ID: {thread.id}") # Post a message to the agent thread message = project_client.agents.create_message( thread_id=thread.id, role="user", content="Why did my billing spike for tenant_456 this week?", ) print(f"Created message, ID: {message.id}") # Run the agent and process the query 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}") # Fetch and display the messages messages = project_client.agents.list_messages(thread_id=thread.id) print("Messages:") pprint(messages["data"][0]["content"][0]["text"]["value"]) # Optional cleanup: # project_client.agents.delete_agent(agent.id) # print("Deleted agent") Run the agent: To run the agent, run the following command python billing_anomaly_agent.py Snippet of output from agent: Using the Azure AI Foundry Agent Playground: After running your agent using the Azure AI Agent SDK, it is saved within your Azure AI Foundry project. You can now experiment with it using the Agent Playground. To try it out: Go to the Agents section in your Azure AI Foundry workspace. Find your billing anomaly agent in the list and click to open it. Use the playground interface to test different financial or billing-related questions, such as: “Did tenant_456 exceed their API usage quota this month?” “Explain recent storage usage changes for tenant_456.” This is a great way to validate your agent's behavior without writing more code. Summary: You’ve now created a working AI agent that talks to your Postgres database, all using: A simple Python function Azure AI Agent Service A Neon Serverless Postgres backend This approach is beginner-friendly, lightweight, and practical for real-world use. Want to go further? You can: Add more tools to the agent Integrate with vector search (e.g., detect anomaly reasons from logs using embeddings) Resources: Introduction to Azure AI Agent Service Develop an AI agent with Azure AI Agent Service Getting Started with Azure AI Agent Service Neon on Azure Build AI Agents with Azure AI Agent Service and Neon Multi-Agent AI Solution with Neon, Langchain, AutoGen and Azure OpenAI Azure AI Foundry GitHub Discussions That's it, folks! But the best part? You can become part of a thriving community of learners and builders by joining the Microsoft Learn Student Ambassadors Community. Connect with like-minded individuals, explore hands-on projects, and stay updated with the latest in cloud and AI. 💬 Join the community on Discord here and explore more benefits on the Microsoft Learn Student Hub.Staying in the flow: SleekFlow and Azure turn customer conversations into conversions
A customer adds three items to their cart but never checks out. Another asks about shipping, gets stuck waiting eight minutes, only to drop the call. A lead responds to an offer but is never followed up with in time. Each of these moments represents lost revenue, and they happen to businesses every day. SleekFlow was founded in 2019 to help companies turn those almost-lost-customer moments into connection, retention, and growth. Today we serve more than 2,000 mid-market and enterprise organizations across industries including retail and e-commerce, financial services, healthcare, travel and hospitality, telecommunications, real estate, and professional services. In total, those customers rely on SleekFlow to orchestrate more than 600,000 daily customer interactions across WhatsApp, Instagram, web chat, email, and more. Our name reflects what makes us different. Sleek is about unified, polished experiences—consolidating conversations into one intelligent, enterprise-ready platform. Flow is about orchestration—AI and human agents working together to move each conversation forward, from first inquiry to purchase to renewal. The drive for enterprise-ready agentic AI Enterprises today expect always-on, intelligent conversations—but delivering that at scale proved daunting. When we set out to build AgentFlow, our agentic AI platform, we quickly ran into familiar roadblocks: downtime that disrupted peak-hour interactions, vector search delays that hurt accuracy, and costs that ballooned under multi-tenant workloads. Development slowed from limited compatibility with other technologies, while customer onboarding stalled without clear compliance assurances. To move past these barriers, we needed a foundation that could deliver the performance, trust, and global scale enterprises demand. The platform behind the flow: How Azure powers AgentFlow We chose Azure because building AgentFlow required more than raw compute power. Chatbots built on a single-agent model often stall out. They struggle to retrieve the right context, they miss critical handoffs, and they return answers too slowly to keep a customer engaged. To fix that, we needed an ecosystem capable of supporting a team of specialized AI agents working together at enterprise scale. Azure Cosmos DB provides the backbone for memory and context, managing short-term interactions, long-term histories, and vector embeddings in containers that respond in 15–20 milliseconds. Powered by Azure AI Foundry, our agents use Azure OpenAI models within Azure AI Foundry to understand and generate responses natively in multiple languages. Whether in English, Chinese, or Portuguese, the responses feel natural and aligned with the brand. Semantic Kernel acts as the conductor, orchestrating multiple agents, each of which retrieves the necessary knowledge and context, including chat histories, transactional data, and vector embeddings, directly from Azure Cosmos DB. For example, one agent could be retrieving pricing data, another summarizing it, and a third preparing it for a human handoff. The result is not just responsiveness but accuracy. A telecom provider can resolve a billing question while surfacing an upsell opportunity in the same dialogue. A financial advisor can walk into a call with a complete dossier prepared in seconds rather than hours. A retailer can save a purchase by offering an in-stock substitute before the shopper abandons the cart. Each of these conversations is different, yet the foundation is consistent on AgentFlow. Fast, fluent, and focused: Azure keeps conversations moving Speed is the heartbeat of a good conversation. A delayed answer feels like a dropped call, and an irrelevant one breaks trust. For AgentFlow to keep customers engaged, every operation behind the scenes has to happen in milliseconds. A single interaction can involve dozens of steps. One agent pulls product information from embeddings, another checks it against structured policy data, and a third generates a concise, brand-aligned response. If any of these steps lag, the dialogue falters. On Azure, they don’t. Azure Cosmos DB manages conversational memory and agent state across dedicated containers for short-term exchanges, long-term history, and vector search. Sharded DiskANN indexing powers semantic lookups that resolve in the 15–20 millisecond range—fast enough that the customer never feels a pause. Microsoft Phi’s model Phi-4 as well as Azure OpenAI in Foundry Models like o3-mini and o4-mini, provide the reasoning, and Azure Container Apps scale elastically, so performance holds steady during event-driven bursts, such as campaign broadcasts that can push the platform from a few to thousands of conversations per minute, and during daily peak-hour surges. To support that level of responsiveness, we run Azure Container Apps on the Pay-As-You-Go consumption plan, using KEDA-based autoscaling to expand from five idle containers to more than 160 within seconds. Meanwhile, Microsoft Orleans coordinates lightweight in-memory clustering to keep conversations sleek and flowing. The results are tangible. Retrieval-augmented generation recall improved from 50 to 70 percent. Execution speed is about 50 percent faster. For SleekFlow’s customers, that means carts are recovered before they’re abandoned, leads are qualified in real time, and support inquiries move forward instead of stalling out. With Azure handling the complexity under the hood, conversations flow naturally on the surface—and that’s what keeps customers engaged. Secure enough for enterprises, human enough for customers AgentFlow was built with security-by-design as a first principle, giving businesses confidence that every interaction is private, compliant, and reliable. On Azure, every AI agent operates inside guardrails enterprises can depend on. Azure Cosmos DB enforces strict per-tenant isolation through logical partitioning, encryption, and role-based access control, ensuring chat histories, knowledge bases, and embeddings remain auditable and contained. Models deployed through Azure AI Foundry, including Azure OpenAI and Microsoft Phi, process data entirely within SleekFlow’s Azure environment and guarantees it is never used to train public models, with activity logged for transparency. And Azure’s certifications—including ISO 27001, SOC 2, and GDPR—are backed by continuous monitoring and regional data residency options, proving compliance at a global scale. But trust is more than a checklist of certifications. AgentFlow brings human-like fluency and empathy to every interaction, powered by Azure OpenAI running with high token-per-second throughput so responses feel natural in real time. Quality control isn’t left to chance. Human override workflows are orchestrated through Azure Container Apps and Azure App Service, ensuring AI agents can carry conversations confidently until they’re ready for human agents. Enterprises gain the confidence to let AI handle revenue-critical moments, knowing Azure provides the foundation and SleekFlow provides the human-centered design. Shaping the next era of conversational AI on Azure The benefits of Azure show up not only in customer conversations but also in the way our own teams work. Faster processing speeds and high token-per-second throughput reduce latency, so we spend less time debugging and more time building. Stable infrastructure minimizes downtime and troubleshooting, lowering operational costs. That same reliability and scalability have transformed the way we engineer AgentFlow. AgentFlow started as part of our monolithic system. Shipping new features used to take about a month of development and another week of heavy testing to make sure everything held together. After moving AgentFlow to a microservices architecture on Azure Container Apps, we can now deploy updates almost daily with no down time or customer impact. And this is all thanks to native support for rolling updates and blue-green deployments. This agility is what excites us most about what's ahead. With Azure as our foundation, SleekFlow is not simply keeping pace with the evolution of conversational AI—we are shaping what comes next. Every interaction we refine, every second we save, and every workflow we streamline brings us closer to our mission: keeping conversations sleek, flowing, and valuable for enterprises everywhere.When Should You Use RAG vs Fine-Tuning in Microsoft Foundry?
If you've been working with Microsoft Foundry, you've likely come across this question: Should I use RAG or Fine-Tuning? The answer becomes much simpler when you focus on the core goal of your solution. Here's a straightforward way to think about it. What is RAG (Retrieval-Augmented Generation)? RAG allows your model to retrieve relevant information from your data sources before generating a response. Instead of relying only on what the model already knows, it: Searches your documents or knowledge base Retrieves relevant content Uses that context to generate grounded, cited answers Use RAG when: ✅ Your data changes frequently ✅ You need answers based on real documents ✅ You have a large, evolving document library ✅ You are building "chat with your data" experiences What is Fine-Tuning? Fine-tuning customizes how the model behaves by training it on task-specific examples. It helps the model: Produce consistent and structured outputs Follow a specific tone, format, or brand voice Align with business rules, compliance policies, and workflows Use Fine-Tuning when: ✅ You need consistent and predictable responses ✅ You want a specific tone, format, or behavior ✅ Your task is stable and well-defined ✅ You are operating at massive scale Visual Overview Below is a quick visual summary to help compare both approaches: A Simple Way to Decide Ask yourself: Is my problem about accessing the right data, or controlling how the model behaves? If it's about data → use RAG If it's about behavior → use Fine-Tuning Quick Comparison What You Need RAG Fine-Tuning Data changes often ✅ Yes ❌ Not ideal Change model behavior/style ❌ No ✅ Yes Fast to get started ✅ Faster ❌ Needs training High-volume, stable queries ⚠️ Token costs grow ✅ Predictable cost Brand voice / compliance ⚠️ Limited ✅ Built into model Large, evolving document library ✅ Perfect fit ❌ Hard to maintain Can You Use Both? In many real-world scenarios, the best teams do exactly that: RAG brings in the right, up-to-date information Fine-Tuning ensures consistent behavior and output quality Think of RAG as giving your model the right books to read, and Fine-Tuning as teaching it how to think and respond. Together, they cover both sides of the equation. I'd love to hear from others in the community: Are you using RAG, Fine-Tuning, or both in your Foundry projects? What use cases are you solving? What challenges or trade-offs have you experienced along the way? Looking forward to your insights. Let's learn from each other! 🚀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.The brand new Azure AI Agent Service at your fingertips
Intro Azure AI Agent Service is a game-changer for developers. This fully managed service empowers you to build, deploy, and scale high-quality, extensible AI agents securely, without the hassle of managing underlying infrastructure. What used to take hundreds of lines of code can now be achieved in just a few lines! So here it is, a web application that streamlines document uploads, summarizes content using AI, and provides seamless access to stored summaries. This article delves into the architecture and implementation of this solution, drawing inspiration from our previous explorations with Azure AI Foundry and secure AI integrations. Architecture Overview Our Azure AI Agent Service WebApp integrates several Azure services to create a cohesive and scalable system: Azure AI Projects & Azure AI Agent Service: Powers the AI-driven summarization and title generation of uploaded documents. Azure Blob Storage: Stores the original and processed documents securely. Azure Cosmos DB: Maintains metadata and summaries for quick retrieval and display. Azure API Management (APIM): Manages and secures API endpoints, ensuring controlled access to backend services. This architecture ensures a seamless flow from document upload to AI processing and storage, providing users with immediate access to summarized content. Azure AI Agent Service – Frontend Implementation The frontend of the Azure AI Agent Service WebApp is built using Vite and React, offering a responsive and user-friendly interface. Key features include: Real-time AI Chat Interface: Users can interact with an AI agent for various queries. Document Upload Functionality: Supports uploading documents in various formats, which are then processed by the backend AI services. Document Repository: Displays a list of uploaded documents with their summaries and download links. This is the main UI , ChatApp.jsx. We can interact with Chat Agent for regular chat, while the keyword “upload:” activates the hidden upload menu. Azure AI Agent Service – Backend Services The backend is developed using Express.js, orchestrating various services to handle: File Uploads: Accepts documents from the frontend and stores them in Azure Blob Storage. AI Processing: Utilizes Azure AI Projects to extract text, generate summaries, and create concise titles. Metadata Storage: Saves document metadata and summaries in Azure Cosmos DB for efficient retrieval. One of the Challenges was to not recreate the Agents each time our backend reloads. So a careful plan is configured, with several files – modules for the Azure AI Agent Service interaction and Agents creation. The initialization for example is taken care by a single file-module: const { DefaultAzureCredential } = require('@azure/identity'); const { SecretClient } = require('@azure/keyvault-secrets'); const { AIProjectsClient, ToolUtility } = require('@azure/ai-projects'); require('dotenv').config(); // Keep track of global instances let aiProjectsClient = null; let agents = { chatAgent: null, extractAgent: null, summarizeAgent: null, titleAgent: null }; async function initializeAI(app) { try { // Setup Azure Key Vault const keyVaultName = process.env.KEYVAULT_NAME; const keyVaultUrl = `https://${keyVaultName}.vault.azure.net`; const credential = new DefaultAzureCredential(); const secretClient = new SecretClient(keyVaultUrl, credential); // Get AI connection string const secret = await secretClient.getSecret('AIConnectionString'); const AI_CONNECTION_STRING = secret.value; // Initialize AI Projects Client aiProjectsClient = AIProjectsClient.fromConnectionString( AI_CONNECTION_STRING, credential ); // Create code interpreter tool (shared among agents) const codeInterpreterTool = ToolUtility.createCodeInterpreterTool(); const tools = [codeInterpreterTool.definition]; const toolResources = codeInterpreterTool.resources; console.log('🚀 Creating AI Agents...'); // Create chat agent agents.chatAgent = await aiProjectsClient.agents.createAgent("gpt-4o-mini", { name: "chat-agent", instructions: "You are a helpful AI assistant that provides clear and concise responses.", tools, toolResources }); console.log('✅ Chat Agent created'); // Create extraction agent agents.extractAgent = await aiProjectsClient.agents.createAgent("gpt-4o-mini", { name: "extract-agent", instructions: "Process and clean text content while maintaining structure and important information.", tools, toolResources }); console.log('✅ Extract Agent created'); // Create summarization agent agents.summarizeAgent = await aiProjectsClient.agents.createAgent("gpt-4o-mini", { name: "summarize-agent", instructions: "Create concise summaries that capture main points and key details.", tools, toolResources }); console.log('✅ Summarize Agent created'); // Create title agent agents.titleAgent = await aiProjectsClient.agents.createAgent("gpt-4o-mini", { name: "title-agent", instructions: `You are a specialized title generation assistant. Your task is to create titles for documents following these rules: 1. Generate ONLY the title text, no additional explanations 2. Maximum length of 50 characters 3. Focus on the main topic or theme 4. Use proper capitalization (Title Case) 5. Avoid special characters and quotes 6. Make titles clear and descriptive 7. Respond with nothing but the title itself Example good responses: Digital Transformation Strategy 2025 Market Analysis: Premium Chai Tea Cloud Computing Implementation Guide Example bad responses: "Here's a title for your document: Digital Strategy" (no explanations needed) This document appears to be about digital transformation (just the title needed) The title is: Market Analysis (no extra text)`, tools, toolResources }); console.log('✅ Title Agent created'); // Store in app.locals app.locals.aiProjectsClient = aiProjectsClient; app.locals.agents = agents; console.log('✅ All AI Agents initialized successfully'); return { aiProjectsClient, agents }; } catch (error) { console.error('❌ Error initializing AI:', error); throw error; } } // Export both the initialization function and the shared instances module.exports = { initializeAI, getClient: () => aiProjectsClient, getAgents: () => agents }; Our backend utilizes 4 agents, creating the Azure AI Agent Service Agents and we will find them in the portal, when the Backend deploys At the same time, each interaction is stored and managed as thread and that’s how we are interacting with the Azure AI Agent Service. Deployment and Security of Azure AI Agent Service WebApp Ensuring secure and efficient deployment is crucial. We’ve employed: Azure API Management (APIM): Secures API endpoints, providing controlled access and monitoring capabilities. Azure Key Vault: Manages sensitive information such as API keys and connection strings, ensuring data protection. Every call to the backend service is protected with Azure API Management Basic Tier. We have only the required endpoints pointing to the matching Endpoints of our Azure AI Agent Service WebApp backend. Also we are storing the AIConnectionString variable in Key Vault and we can move all Variables in Key Vault as well, which i recommend ! Get started with Azure AI Agent Service To get started with Azure AI Agent Service, you need to create an Azure AI Foundry hub and an Agent project in your Azure subscription. Start with the quickstart guide if it’s your first time using the service. You can create a AI hub and project with the required resources. After you create a project, you can deploy a compatible model such as GPT-4o. When you have a deployed model, you can also start making API calls to the service using the SDKs. There are already 2 Quick-starts available to get your Azure AI Agent Service up and running, the Basic and the Standard. I have chosen the second one the Standard plan, since we have a WebApp, and the whole Architecture comes very handy ! We just added the CosmosDB interaction and the API Management to extend to an enterprise setup ! Our own Azure AI Agent Service deployment, allows us to interact with the Agents, and utilize tools and functions very easy. Conclusion By harnessing the power of Azure’s cloud services, we’ve developed a scalable and efficient web application that simplifies document management through AI-driven processing. This solution not only enhances productivity but also ensures secure and organized access to essential information. References Azure AI Agent Service Documentation What is Azure AI Agent Service Azure AI Agent Service Quick starts Azure API Management Azure AI Foundry Azure AI Foundry Inference Demo
