Azure Event Grid Domain Creation: Overcoming AZ CLI's TLS Parameter Limitations with Workaround
Introduction: The Intersection of Security Policies and DevOps Automation In the modern cloud landscape, organizations increasingly enforce strict security requirements through platform policies. One common requirement is mandating latest TLS versions for example TLS 1.2 across all deployed resources to protect data in transit. While this is an excellent security practice, it can sometimes conflict with the available configuration options in deployment tools, particularly in the Azure CLI. This blog explores a specific scenario that many Azure DevOps teams encounter: how to deploy an Azure Event Grid domain when your organization has a custom policy requiring latest version considering TLS 1.2, but the Azure CLI command doesn't provide a parameter to configure this setting. The Problem: Understanding the Gap Between Policy and Tooling What Is Azure Event Grid? Azure Event Grid is a serverless event routing service that enables event-driven architectures. It manages the routing of events from various sources (like Azure services, custom applications, or SaaS products) to different handlers such as Azure Functions, Logic Apps, or custom webhooks. An Event Grid domain provides a custom topic endpoint that can receive events from multiple sources, offering a way to organize and manage events at scale. The Policy Requirement: Many organizations implement Azure Policy to enforce security standards across their cloud infrastructure. A common policy might look like this: { "policyRule": { "if": { "allOf": [ { "field": "type", "equals": "Microsoft.EventGrid/domains" }, { "anyOf": [ { "field": "Microsoft.EventGrid/domains/minimumTlsVersion", "exists": false }, { "field": "Microsoft.EventGrid/domains/minimumTlsVersion", "notEquals": "1.2" } ] } ] }, "then": { "effect": "deny" } } } This policy blocks the creation of any Event Grid domain that doesn't explicitly set TLS 1.2 as the minimum TLS version. The CLI Limitation: Now, let's examine the Azure CLI command to create an Event Grid domain: az eventgrid domain | Microsoft Learn TLS property is unrecognized with the latest version of AZ CLI version. Current Status of This Limitation: It's worth noting that this limitation has been recognized by the Azure team. There is an official GitHub feature request tracking this issue, which you can find at => Please add TLS support while creation of Azure Event Grid domain through CLI · Issue #31278 · Azure/azure-cli Before implementing this workaround described in this article, I recommend checking the current status of this feature request. The Azure CLI is continuously evolving, and by the time you're reading this, the limitation might have been addressed. However, as of April 2025, this remains a known limitation in the Azure CLI, necessitating the alternative approach outlined below. Why This Matters: This limitation becomes particularly problematic in CI/CD pipelines or Infrastructure as Code (IaC) scenarios where you want to automate the deployment of Event Grid domain resources. Workaround: You can utilize below ARM template and deploy it through AZ CLI in your deployment pipeline as below: Working ARM template: { "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#", "contentVersion": "1.0.0.0", "parameters": { "domainName": { "type": "string", "metadata": { "description": "Name of the Event Grid Domain" } }, "location": { "type": "string", "defaultValue": "[resourceGroup().location]", "metadata": { "description": "Azure region for the domain" } } }, "resources": [ { "type": "Microsoft.EventGrid/domains", "apiVersion": "2025-02-15", "name": "[parameters('domainName')]", "location": "[parameters('location')]", "properties": { "minimumTlsVersionAllowed": "1.2" } } ] } Please note I've used latest API version from below official Microsoft documentation : Microsoft.EventGrid/domains - Bicep, ARM template & Terraform AzAPI reference | Microsoft Learn Working AZ CLI command: az deployment group create --resource-group <rg> --template-file <armtemplate.json> --parameters domainName=<event grid domain name> You can store this ARM template in your configuration directory with replacement for Azure CLI command. It explicitly sets TLS 1.2 for Event Grid domains, ensuring security compliance where the CLI lacks this parameter. For example: az deployment group create --resource-group <rg> --template-file ./config/<armtemplate.json> --parameters domainName=<event grid domain name> Disclaimer: The sample scripts provided in this article are provided AS IS without warranty of any kind. The author is not responsible for any issues, damages, or problems that may arise from using these scripts. Users should thoroughly test any implementation in their environment before deploying to production. Azure services and APIs may change over time, which could affect the functionality of the provided scripts. Always refer to the latest Azure documentation for the most up-to-date information. Thanks for reading this blog! I hope you've found this workaround valuable for addressing the Event Grid domain TLS parameter limitation in Azure CLI. 😊Azure Event Grid CLI Identity Gaps & Workarounds with Python REST and ARM Templates
Azure Event Grid has become a cornerstone service for building event-driven architectures in the cloud. It provides a scalable event routing service that enables reactive programming patterns, connecting event sources to event handlers seamlessly. However, when working with Event Grid through the Azure CLI, developers often encounter a significant limitation: the inability to configure system-assigned managed identities using CLI commands. In this blog post, I'll explore this limitation and provide practical workarounds using Python REST API calls and ARM templates with CLI to ensure your Event Grid deployments can leverage the security benefits of managed identities without being blocked by tooling constraints. Problem Statement: Unlike many other Azure resources that support the --identity or ---assign-identity parameter for enabling system-assigned managed identities, Event Grid's CLI commands lack this capability while creating event subscription for system topic at the moment. This means that while the Azure Portal and other methods support managed identities for Event Grid, you can't configure them directly through the CLI in case of system topic event subscriptions For example you can add managed identity for delivery through portal but not through AZ CLI: If you try to use the following CLI command: az eventgrid system-topic event-subscription create \ --name my-sub \ --system-topic-name my-topic \ --resource-group my-rg \ --endpoint <EH resource id> --endpoint-type eventhub \ --identity systemassigned You'll run into a limitation: The --identity flag is not supported or unrecognized for system topic subscriptions in Azure CLI. Also, --delivery-identity is in preview and under development Current Status of This Limitation: It's worth noting that this limitation has been recognized by the Azure team. There is an official GitHub feature request tracking this issue, which you can find at Use managed identity to command creates an event subscription for an event grid system topic · Issue #26910 · Azure/azure-cli. Before implementing any of the workarounds described in this article, I recommend checking the current status of this feature request. The Azure CLI is continuously evolving, and by the time you're reading this, the limitation might have been addressed. However, as of April 2025, this remains a known limitation in the Azure CLI, necessitating the alternative approaches outlined below. Why This Matters: This limitation becomes particularly problematic in CI/CD pipelines or Infrastructure as Code (IaC) scenarios where you want to automate the deployment of Event Grid resources with managed identities. Solution 1: Using Azure REST API with Python request library: The first approach to overcome this limitation is to use the Azure REST API with Python. This provides the most granular control over your Event Grid resources and allows you to enable system-assigned managed identities programmatically. System Topic Event Subscriptions - Create Or Update - REST API (Azure Event Grid) | Microsoft Learn You can retrieve Azure Entra token using below CLI command: az account get-access-token Sample working code & payload: import requests import json subscription_id = <> resource_group = <> system_topic_name = <> event_subscription_name = <> event_hub_resource_id = <> access_token = <> url = f"https://management.azure.com/subscriptions/{subscription_id}/resourceGroups/{resource_group}/providers/Microsoft.EventGrid/systemTopics/{system_topic_name}/eventSubscriptions/{event_subscription_name}?api-version=2024-12-15-preview" payload = { "identity": { "type": "SystemAssigned" }, "properties": { "topic": "/subscriptions/<>/resourceGroups/<>/providers/Microsoft.EventGrid/systemTopics/<>", "filter": { "includedEventTypes": [ "Microsoft.Storage.BlobCreated", "Microsoft.Storage.BlobDeleted" ], "advancedFilters": [], "enableAdvancedFilteringOnArrays": True }, "labels": [], "eventDeliverySchema": "EventGridSchema", "deliveryWithResourceIdentity": { "identity": { "type": "SystemAssigned" }, "destination": { "endpointType": "EventHub", "properties": { "resourceId": "/subscriptions/<>/resourceGroups/rg-sch/providers/Microsoft.EventHub/namespaces/<>/eventhubs/<>", "deliveryAttributeMappings": [ { "name": "test", "type": "Static", "properties": { "value": "test", "isSecret": False, "sourceField": "" } }, { "name": "id", "type": "Dynamic", "properties": { "value": "abc", "isSecret": False, "sourceField": "data.key" } } ] } } } } } headers = { "Authorization": f"Bearer {access_token}", "Content-Type": "application/json" } response = requests.put(url, headers=headers, data=json.dumps(payload)) if response.status_code in [200, 201]: print("Event subscription created successfully!") Remember that these tokens are sensitive security credentials, so handle them with appropriate care. They should never be exposed in logs, shared repositories, or other insecure locations. Solution 2: Using ARM Templates & deploying it through CLI Another solution is to use Azure Resource Manager (ARM) templates, which fully support system-assigned managed identities for Event Grid. This approach works well in existing IaC workflows. Microsoft.EventGrid/systemTopics/eventSubscriptions - Bicep, ARM template & Terraform AzAPI reference | Microsoft Learn Here's a sample ARM template that creates an Event Grid topic with a system-assigned managed identity: { "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#", "contentVersion": "1.0.0.0", "parameters": { "systemTopicName": { "type": "string", "metadata": { "description": "Name of the existing system topic" } }, "eventSubscriptionName": { "type": "string", "metadata": { "description": "Name of the event subscription to create" } }, "eventHubResourceId": { "type": "string", "metadata": { "description": "Resource ID of the Event Hub to send events to" } }, "includedEventType": { "type": "string", "defaultValue": "Microsoft.Storage.BlobCreated", "metadata": { "description": "Event type to filter on" } } }, "resources": [ { "type": "Microsoft.EventGrid/systemTopics/eventSubscriptions", "apiVersion": "2024-06-01-preview", "name": "[format('{0}/{1}', parameters('systemTopicName'), parameters('eventSubscriptionName'))]", "identity": { "type": "SystemAssigned" }, "properties": { "deliveryWithResourceIdentity": { "destination": { "endpointType": "EventHub", "properties": { "resourceId": "[parameters('eventHubResourceId')]" } }, "identity": { "type": "SystemAssigned" } }, "eventDeliverySchema": "EventGridSchema", "filter": { "includedEventTypes": [ "[parameters('includedEventType')]" ] } } } ] } How to deploy via Azure CLI: az deployment group create \ --resource-group <your-resource-group> \ --template-file eventgridarmtemplate.json \ --parameters \ systemTopicName=<system-topic-name> \ eventSubscriptionName=<event-subscription-name> \ eventHubResourceId="/subscriptions/<sub>/resourceGroups/<rg>/providers/Microsoft.EventHub/namespaces/<namespace>/eventhubs/<hub>" Disclaimer The sample scripts provided in this article are provided AS IS without warranty of any kind. The author is not responsible for any issues, damages, or problems that may arise from using these scripts. Users should thoroughly test any implementation in their environment before deploying to production. Azure services and APIs may change over time, which could affect the functionality of the provided scripts. Always refer to the latest Azure documentation for the most up-to-date information. Thanks for reading this blog! I hope you've found these workarounds valuable for addressing the Event Grid identity parameter limitation in Azure CLI. 😊Elevate Your AI Expertise with Microsoft Azure: Learn Live Series for Developers
Unlock the power of Azure AI and master the art of creating advanced AI agents. Starting from April 15th, embark on a comprehensive learning journey designed specifically for professional developers like you. This series will guide you through the official Microsoft Learn Plan, focused on the latest agentic AI technologies and innovations. Generative AI has evolved to become an essential tool for crafting intelligent applications, and AI agents are leading the charge. Here's your opportunity to deepen your expertise in building powerful, scalable agent-based solutions using the Azure AI Foundry, Azure AI Agent Service, and the Semantic Kernel Framework. Why Attend? This Learn Live series will provide you with: In-depth Knowledge: Understand when to use AI agents, how they function, and the best practices for building them on Azure. Hands-On Experience: Gain practical skills to develop, deploy, and extend AI agents with Azure AI Agent Service and Semantic Kernel SDK. Expert Insights: Learn directly from Microsoft’s AI professionals, ensuring you're at the cutting edge of agentic AI technologies. Session Highlights Plan and Prepare AI Solutions | April 15th Explore foundational principles for creating secure and responsible AI solutions. Prepare your development environment for seamless integration with Azure AI services. Fundamentals of AI Agents | April 22nd Discover the transformative role of language models and generative AI in enabling intelligent applications. Understand Microsoft Copilot and effective prompting techniques for agent development. Azure AI Agent Service: Build and Integrate | April 29th Dive into the key features of Azure AI Agent Service. Build agents and learn how to integrate them into your applications for enhanced functionality. Extend with Custom Tools | May 6th Enhance your agents’ capabilities with custom tools, tailored to meet unique application requirements. Develop an AI agent with Semantic Kernel | May 8th Use Semantic Kernel to connect to an Azure AI Foundry project Create Azure AI Agent Service agents using the Semantic Kernel SDK Integrate plugin functions with your AI agent Orchestrate Multi-Agent Solutions with Semantic Kernel | May 13th Utilize the Semantic Kernel SDK to create collaborative multi-agent systems. Develop and integrate custom plugin functions for versatile AI solutions. What You’ll Achieve By the end of this series, you'll: Build AI agents using cutting-edge Azure technologies. Integrate custom tools to extend agent capabilities. Develop multi-agent solutions with advanced orchestration. How to Join Don't miss out on this opportunity to level up your development skills and lead the next wave of AI-driven applications. Register now and set yourself apart as a developer equipped to harness the full potential of Azure AI. 🔗 Register for the Learn Live Series 🗓️ Format: Livestream | Language: English | Topic: Core AI Development Take the leap and transform how you develop intelligent applications with Microsoft Azure AI. Does this revision align with your vision for the blog? Let me know if there's anything else you'd like to refine or add!
Using Azure API Management with Azure Front Door for Global, Multi‑Region Architectures
Modern API‑driven applications demand global reach, high availability, and predictable latency. Azure provides two complementary services that help achieve this: Azure API Management (APIM) as the API gateway and Azure Front Door (AFD) as the global entry point and load balancer. Going over the available documentation available, my team and I found this article on how to front a single-region APIM with an Azure Front Door , but we wanted to extend this to a multi-region APIM as well. That led us to design the solution detailed in this article which explains how to configure multi‑regional, active‑active APIM behind Azure Front Door using Custom origins and regional gateway endpoints. (I have also covered topics like why organizations commonly pair APIM with Front Door, when to use internal vs. external APIM modes, etc. but main topic first! Scroll down to the bottom for more info). Configuring Multi‑Regional APIM with Azure Front Door WHAT TO KNOW: If using APIM Premium with multi‑region gateways, each region exposes its own regional gateway endpoint, formatted as: https://<service-name>-<region>-01.regional.azure-api.net Examples: https://mydemo-apim-westeurope-01.regional.azure-api.net https://mydemo-apim-eastus-01.regional.azure-api.net where 'mydemo' is the name of the APIM instance. You will use these regional endpoints and configure them as a separate origin in Azure Front Door—using the Custom origin type. Solution Architecture Azure Front Door Configuration Steps 1. Create an Origin Group Inside your Front Door profile, define a group (Settings -> Origin Groups - > Add -> Add an origin) that will contain all APIM regional gateways. See images below: 2. Add Each APIM Region as a Custom Origin Use the Custom origin type: Origin type: Custom Host name: Use the APIM regional endpoint Example: mydemo-apim-westeurope-01.regional.azure-api.net Origin host header: Same as the host name. Enable certificate subject name validation (Recommended when private link or TLS integrity is required.) Priority: Lower value = higher priority (for failover). Weight: Controls how traffic is distributed across equally prioritized origins. Status: Enable origin. And repeat the same steps for additional APIM regions giving them priority and weightage as you feel appropriate. How to Know Which Region is being Invoked To test this setup, create 2 Virtual Machines (VMs) in Azure - one for each region. For this guide, we chose to create the VMs in West Europe and East US. Open up a Command Prompt from the VM and do a curl on the sample Echo API that comes with every new APIM deployment: Example: curl -v "afd-blah.b01.azurefd.net/echo/resource?param1=sample" Your results should show the region being hit as shown below: How AFD Routes Traffic Across Multiple APIM Regions AFD evaluates origins in this order: Available instances — the Health Probe removes unhealthy origins Priority — selects highest‑priority available origins Latency — optionally selects lowest‑latency pool Weight — round‑robin distribution across selected origins Example When origins are configured as below: West Europe (priority 1, weight 1000) East US (priority 1, weight 500) Central US (priority 2, weight 1000) AFD will: Use West Europe + East US in a 1000:500 ratio. Only use Central US if both West Europe & East US become unavailable. For more information on this nice algorithm, see here: Traffic routing methods to origin - Azure Front Door | Microsoft Learn More Info (as promised) Why Use Azure API Management? Azure API Management is a fully managed service providing: 1. Centralized API Gateway Enforces policies such as authentication, rate limiting, transformations, and caching. Acts as a single façade for backend services, enabling modernization without breaking existing clients. 2. Security & Governance Integrates with Azure AD, OAuth2, and mTLS (mutual TLS). Provides threat protection and schema validation. 3. Developer Ecosystem Developer portal, API documentation, testing console, versioning, and releases. 4. Multi‑Region Gateways (Premium Tier) Allows deployment of additional regional gateways for active‑active, low‑latency global experiences. APIM Deployment Modes: Internal vs. External External Mode The APIM gateway is reachable publicly over the internet. Common when: Exposing APIs to partners, mobile apps, or public clients. You can easily front this with an Azure Front Door for reasons listed in the next section. Internal Mode APIM gateway is deployed inside a VNet, accessible only privately. Used when: APIs must stay private to an enterprise network. Only internal consumers/VPN/VNet peered systems need access. To make your APIM publicly accessible, you need to front it with both an Application Gateway and an Azure Front Door because: Azure Front Door (AFD) cannot directly reach an internal‑mode APIM because AFD requires a publicly routable origin. Application Gateway is a Layer‑7 reverse proxy that can expose a public frontend while still reaching internal private backends (like APIM gateway). [Ref] But Why Put Azure Front Door in Front of API Management? Azure Front Door provides capabilities that APIM alone does not offer: 1. Global Load Balancing As discussed above. 2. Edge Security Web Application Firewall, TLS termination at the edge, DDoS absorption. Reduces load on API gateways. 3. Faster Global Performance Anycast network and global POPs reduce round‑trip latency before requests hit APIM. A POP (Point of Presence) is an Azure Front Door edge location—a physical site in Microsoft’s global network where incoming user traffic first lands. Azure Front Door uses numerous global and local POPs strategically placed close to end‑users (both enterprise and consumer) to improve performance. Anycast is a networking protocol Azure Front Door uses to improve global connectivity. Ref: Traffic acceleration - Azure Front Door | Microsoft Learn 4. Unified Global Endpoint A single public endpoint (e.g., https://api.contoso.com) that intelligently distributes traffic across multiple APIM regions. With all of the above features, it is best to pair API Management with a Front Door, especially when dealing with multi-region architectures. Credits: Junee Singh, Senior Solution Engineer at Microsoft Isiah Hudson, Senior Solution Engineer at MicrosoftKickstart projects with azd Templates
Navigating today’s software development challenges requires streamlined tools and frameworks. The Azure Developer CLI (azd) simplifies provisioning and deployment on Azure with its intuitive, developer-focused commands. Beyond mere automation, azd templates provide reusable blueprints for proof-of-concept projects, complete configurations for managed systems, and robust Infrastructure as Code assets. By accelerating application development and eliminating redundant setup, azd enables developers to focus on innovation. Embrace azd to enhance productivity and adapt to the evolving development landscape effortlessly.Building real-world AI automation with Foundry Local and the Microsoft Agent Framework
A hands-on guide to building real-world AI automation with Foundry Local, the Microsoft Agent Framework, and PyBullet. No cloud subscription, no API keys, no internet required. Why Developers Should Care About Offline AI Imagine telling a robot arm to "pick up the cube" and watching it execute the command in a physics simulator, all powered by a language model running on your laptop. No API calls leave your machine. No token costs accumulate. No internet connection is needed. That is what this project delivers, and every piece of it is open source and ready for you to fork, extend, and experiment with. Most AI demos today lean on cloud endpoints. That works for prototypes, but it introduces latency, ongoing costs, and data privacy concerns. For robotics and industrial automation, those trade-offs are unacceptable. You need inference that runs where the hardware is: on the factory floor, in the lab, or on your development machine. Foundry Local gives you an OpenAI-compatible endpoint running entirely on-device. Pair it with a multi-agent orchestration framework and a physics engine, and you have a complete pipeline that translates natural language into validated, safe robot actions. This post walks through how we built it, why the architecture works, and how you can start experimenting with your own offline AI simulators today. Architecture The system uses four specialised agents orchestrated by the Microsoft Agent Framework: Agent What It Does Speed PlannerAgent Sends user command to Foundry Local LLM → JSON action plan 4–45 s SafetyAgent Validates against workspace bounds + schema < 1 ms ExecutorAgent Dispatches actions to PyBullet (IK, gripper) < 2 s NarratorAgent Template summary (LLM opt-in via env var) < 1 ms User (text / voice) │ ▼ ┌──────────────┐ │ Orchestrator │ └──────┬───────┘ │ ┌────┴────┐ ▼ ▼ Planner Narrator │ ▼ Safety │ ▼ Executor │ ▼ PyBullet Setting Up Foundry Local from foundry_local import FoundryLocalManager import openai manager = FoundryLocalManager("qwen2.5-coder-0.5b") client = openai.OpenAI( base_url=manager.endpoint, api_key=manager.api_key, ) resp = client.chat.completions.create( model=manager.get_model_info("qwen2.5-coder-0.5b").id, messages=[{"role": "user", "content": "pick up the cube"}], max_tokens=128, stream=True, ) from foundry_local import FoundryLocalManager import openai manager = FoundryLocalManager("qwen2.5-coder-0.5b") client = openai.OpenAI( base_url=manager.endpoint, api_key=manager.api_key, ) resp = client.chat.completions.create( model=manager.get_model_info("qwen2.5-coder-0.5b").id, messages=[{"role": "user", "content": "pick up the cube"}], max_tokens=128, stream=True, ) The SDK auto-selects the best hardware backend (CUDA GPU → QNN NPU → CPU). No configuration needed. How the LLM Drives the Simulator Understanding the interaction between the language model and the physics simulator is central to the project. The two never communicate directly. Instead, a structured JSON contract forms the bridge between natural language and physical motion. From Words to JSON When a user says “pick up the cube”, the PlannerAgent sends the command to the Foundry Local LLM alongside a compact system prompt. The prompt lists every permitted tool and shows the expected JSON format. The LLM responds with a structured plan: { "type": "plan", "actions": [ {"tool": "describe_scene", "args": {}}, {"tool": "pick", "args": {"object": "cube_1"}} ] } The planner parses this response, validates it against the action schema, and retries once if the JSON is malformed. This constrained output format is what makes small models (0.5B parameters) viable: the response space is narrow enough that even a compact model can produce correct JSON reliably. From JSON to Motion Once the SafetyAgent approves the plan, the ExecutorAgent maps each action to concrete PyBullet calls: move_ee(target_xyz) : The target position in Cartesian coordinates is passed to PyBullet's inverse kinematics solver, which computes the seven joint angles needed to place the end-effector at that position. The robot then interpolates smoothly from its current joint state to the target, stepping the physics simulation at each increment. pick(object) : This triggers a multi-step grasp sequence. The controller looks up the object's position in the scene, moves the end-effector above the object, descends to grasp height, closes the gripper fingers with a configurable force, and lifts. At every step, PyBullet resolves contact forces and friction so that the object behaves realistically. place(target_xyz) : The reverse of a pick. The robot carries the grasped object to the target coordinates and opens the gripper, allowing the physics engine to drop the object naturally. describe_scene() : Rather than moving the robot, this action queries the simulation state and returns the position, orientation, and name of every object on the table, along with the current end-effector pose. The Abstraction Boundary The critical design choice is that the LLM knows nothing about joint angles, inverse kinematics, or physics. It operates purely at the level of high-level tool calls ( pick , move_ee ). The ActionExecutor translates those tool calls into the low-level API that PyBullet provides. This separation means the LLM prompt stays simple, the safety layer can validate plans without understanding kinematics, and the executor can be swapped out without retraining or re-prompting the model. Voice Input Pipeline Voice commands follow three stages: Browser capture: MediaRecorder captures audio, client-side resamples to 16 kHz mono WAV Server transcription: Foundry Local Whisper (ONNX, cached after first load) with automatic 30 s chunking Command execution: transcribed text goes through the same Planner → Safety → Executor pipeline The mic button (🎤) only appears when a Whisper model is cached or loaded. Whisper models are filtered out of the LLM dropdown. Web UI in Action Pick command Describe command Move command Reset command Performance: Model Choice Matters Model Params Inference Pipeline Total qwen2.5-coder-0.5b 0.5 B ~4 s ~5 s phi-4-mini 3.6 B ~35 s ~36 s qwen2.5-coder-7b 7 B ~45 s ~46 s For interactive robot control, qwen2.5-coder-0.5b is the clear winner: valid JSON for a 7-tool schema in under 5 seconds. The Simulator in Action Here is the Panda robot arm performing a pick-and-place sequence in PyBullet. Each frame is rendered by the simulator's built-in camera and streamed to the web UI in real time. Overview Reaching Above the cube Gripper detail Front interaction Side layout Get Running in Five Minutes You do not need a GPU, a cloud account, or any prior robotics experience. The entire stack runs on a standard development machine. # 1. Install Foundry Local winget install Microsoft.FoundryLocal # Windows brew install foundrylocal # macOS # 2. Download models (one-time, cached locally) foundry model run qwen2.5-coder-0.5b # Chat brain (~4 s inference) foundry model run whisper-base # Voice input (194 MB) # 3. Clone and set up the project git clone https://github.com/leestott/robot-simulator-foundrylocal cd robot-simulator-foundrylocal .\setup.ps1 # or ./setup.sh on macOS/Linux # 4. Launch the web UI python -m src.app --web --no-gui # → http://localhost:8080 Once the server starts, open your browser and try these commands in the chat box: "pick up the cube": the robot grasps the blue cube and lifts it "describe the scene": returns every object's name and position "move to 0.3 0.2 0.5": sends the end-effector to specific coordinates "reset": returns the arm to its neutral pose If you have a microphone connected, hold the mic button and speak your command instead of typing. Voice input uses a local Whisper model, so your audio never leaves the machine. Experiment and Build Your Own The project is deliberately simple so that you can modify it quickly. Here are some ideas to get started. Add a new robot action The robot currently understands seven tools. Adding an eighth takes four steps: Define the schema in TOOL_SCHEMAS ( src/brain/action_schema.py ). Write a _do_<tool> handler in src/executor/action_executor.py . Register it in ActionExecutor._dispatch . Add a test in tests/test_executor.py . For example, you could add a rotate_ee tool that spins the end-effector to a given roll/pitch/yaw without changing position. Add a new agent Every agent follows the same pattern: an async run(context) method that reads from and writes to a shared dictionary. Create a new file in src/agents/ , register it in orchestrator.py , and the pipeline will call it in sequence. Ideas for new agents: VisionAgent: analyse a camera frame to detect objects and update the scene state before planning. CostEstimatorAgent: predict how many simulation steps an action plan will take and warn the user if it is expensive. ExplanationAgent: generate a step-by-step natural language walkthrough of the plan before execution, allowing the user to approve or reject it. Swap the LLM python -m src.app --web --model phi-4-mini Or use the model dropdown in the web UI; no restart is needed. Try different models and compare accuracy against inference speed. Smaller models are faster but may produce malformed JSON more often. Larger models are more accurate but slower. The retry logic in the planner compensates for occasional failures, so even a small model works well in practice. Swap the simulator PyBullet is one option, but the architecture does not depend on it. You could replace the simulation layer with: MuJoCo: a high-fidelity physics engine popular in reinforcement learning research. Isaac Sim: NVIDIA's GPU-accelerated robotics simulator with photorealistic rendering. Gazebo: the standard ROS simulator, useful if you plan to move to real hardware through ROS 2. The only requirement is that your replacement implements the same interface as PandaRobot and GraspController . Build something completely different The pattern at the heart of this project (LLM produces structured JSON, safety layer validates, executor dispatches to a domain-specific engine) is not limited to robotics. You could apply the same architecture to: Home automation: "turn off the kitchen lights and set the thermostat to 19 degrees" translated into MQTT or Zigbee commands. Game AI: natural language control of characters in a game engine, with the safety agent preventing invalid moves. CAD automation: voice-driven 3D modelling where the LLM generates geometry commands for OpenSCAD or FreeCAD. Lab instrumentation: controlling scientific equipment (pumps, stages, spectrometers) via natural language, with the safety agent enforcing hardware limits. From Simulator to Real Robot One of the most common questions about projects like this is whether it could control a real robot. The answer is yes, and the architecture is designed to make that transition straightforward. What Stays the Same The entire upper half of the pipeline is hardware-agnostic: The LLM planner generates the same JSON action plans regardless of whether the target is simulated or physical. It has no knowledge of the underlying hardware. The safety agent validates workspace bounds and tool schemas. For a real robot, you would tighten the bounds to match the physical workspace and add checks for obstacle clearance using sensor data. The orchestrator coordinates agents in the same sequence. No changes are needed. The narrator reports what happened. It works with any result data the executor returns. What Changes The only component that must be replaced is the executor layer, specifically the PandaRobot class and the GraspController . In simulation, these call PyBullet's inverse kinematics solver and step the physics engine. On a real robot, they would instead call the hardware driver. For a Franka Emika Panda (the same robot modelled in the simulation), the replacement options include: libfranka: Franka's C++ real-time control library, which accepts joint position or torque commands at 1 kHz. ROS 2 with MoveIt: A robotics middleware stack that provides motion planning, collision avoidance, and hardware abstraction. The move_ee action would become a MoveIt goal, and the framework would handle trajectory planning and execution. Franka ROS 2 driver: Combines libfranka with ROS 2 for a drop-in replacement of the simulation controller. The ActionExecutor._dispatch method maps tool names to handler functions. Replacing _do_move_ee , _do_pick , and _do_place with calls to a real robot driver is the only code change required. Key Considerations for Real Hardware Safety: A simulated robot cannot cause physical harm; a real robot can. The safety agent would need to incorporate real-time collision checking against sensor data (point clouds from depth cameras, for example) rather than relying solely on static workspace bounds. Perception: In simulation, object positions are known exactly. On a real robot, you would need a perception system (cameras with object detection or fiducial markers) to locate objects before grasping. Calibration: The simulated robot's coordinate frame matches the URDF model perfectly. A real robot requires hand-eye calibration to align camera coordinates with the robot's base frame. Latency: Real actuators have physical response times. The executor would need to wait for motion completion signals from the hardware rather than stepping a simulation loop. Gripper feedback: In PyBullet, grasp success is determined by contact forces. A real gripper would provide force or torque feedback to confirm whether an object has been securely grasped. The Simulation as a Development Tool This is precisely why simulation-first development is valuable. You can iterate on the LLM prompts, agent logic, and command pipeline without risk to hardware. Once the pipeline reliably produces correct action plans in simulation, moving to a real robot is a matter of swapping the lowest layer of the stack. Key Takeaways for Developers On-device AI is production-ready. Foundry Local serves models through a standard OpenAI-compatible API. If your code already uses the OpenAI SDK, switching to local inference is a one-line change to base_url . Small models are surprisingly capable. A 0.5B parameter model produces valid JSON action plans in under 5 seconds. For constrained output schemas, you do not need a 70B model. Multi-agent pipelines are more reliable than monolithic prompts. Splitting planning, validation, execution, and narration across four agents makes each one simpler to test, debug, and replace. Simulation is the safest way to iterate. You can refine LLM prompts, agent logic, and tool schemas without risking real hardware. When the pipeline is reliable, swapping the executor for a real robot driver is the only change needed. The pattern generalises beyond robotics. Structured JSON output from an LLM, validated by a safety layer, dispatched to a domain-specific engine: that pattern works for home automation, game AI, CAD, lab equipment, and any other domain where you need safe, structured control. You can start building today. The entire project runs on a standard laptop with no GPU, no cloud account, and no API keys. Clone the repository, run the setup script, and you will have a working voice-controlled robot simulator in under five minutes. Ready to start building? Clone the repository, try the commands, and then start experimenting. Fork it, add your own agents, swap in a different simulator, or apply the pattern to an entirely different domain. The best way to learn how local AI can solve real-world problems is to build something yourself. Source code: github.com/leestott/robot-simulator-foundrylocal Built with Foundry Local, Microsoft Agent Framework, PyBullet, and FastAPI.Strategic Solutions for Seamless Integration of Third-Party SaaS
Modern systems must be modular and interoperable by design. Integration is no longer a feature, it’s a requirement. Developers are expected to build architectures that connect easily with third-party platforms, but too often, core systems are designed in isolation. This disconnect creates friction for downstream teams and slows delivery. At Microsoft, SaaS platforms like SAP SuccessFactors and Eightfold support Talent Acquisition by handling functions such as requisition tracking, application workflows, and interview coordination. These tools help reduce costs and free up engineering focus for high-priority areas like Azure and AI. The real challenge is integrating them with internal systems such as Demand Planning, Offer Management, and Employee Central. This blog post outlines a strategy centered around two foundational components: an Integration and Orchestration Layer, and a Messaging Platform. Together, these enable real-time communication, consistent data models, and scalable integration. While Talent Acquisition is the use case here, the architectural patterns apply broadly across domains. Whether you're embedding AI pipelines, managing edge deployments, or building platform services, thoughtful integration needs to be built into the foundation, not bolted on later.
