{"id":2862,"date":"2026-09-09T16:00:39","date_gmt":"2026-09-09T23:00:39","guid":{"rendered":"https:\/\/devblogs.microsoft.com\/foundry\/?p=2862"},"modified":"2026-09-10T00:45:41","modified_gmt":"2026-09-10T07:45:41","slug":"whats-new-in-microsoft-foundry-july-august-2026","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/foundry\/whats-new-in-microsoft-foundry-july-august-2026\/","title":{"rendered":"What’s new in Microsoft Foundry: July and August 2026"},"content":{"rendered":"

Microsoft Foundry’s July and August 2026 updates make Hosted Agents, Voice Live integration, and Toolboxes generally available; expand Claude tools hosted on Azure; update Model Router’s regions and model pool; add Foundry Local capabilities; and advance the Python, JavaScript, Java, and .NET SDKs.<\/p>\n

Author’s note<\/strong>: After a long summer break and a small US holiday, we have a lot to catch up on! I’ve brought July and August’s Foundry updates together in one roundup, with code examples and migration notes to help you get started.<\/p><\/blockquote>\n

TL;DR<\/h2>\n

Hosted Agents, Voice Live integration, and Toolboxes are now generally available, giving developers a managed runtime for agent code, real-time voice, and reusable tools managed outside the agent. Claude deployments hosted on Azure add structured outputs, Web search, Web fetch, MCP connector, and Tool search, with the MCP connector using the beta API. Model Router adds regions, refreshes its routing pool with GPT-5.6 variants and Claude Opus 4.8, and expands agentic routing to eligible Anthropic and open-source models. Foundry Local on Azure Local extension 2607<\/code> adds preview model evaluation, vLLM model parallelism, and improved automatic GPU inference tuning, while Foundry DevPack 0.1.3<\/code> provides preview installers for Windows, macOS, and Linux on x64 and Arm64. By the end of August, the Python and JavaScript\/TypeScript SDKs had reached stable version 2.5.0<\/code> and Java 2.4.0<\/code>; the .NET 3.0.0<\/code> line remained in preview, with hosted-agent management and runtime support differing by language.<\/p>\n

Join the community<\/h2>\n

Which July or August update should we cover with a deeper implementation example? Share what you are building\u2014or where you are blocked\u2014in Discord<\/a> or GitHub Discussions<\/a>, and subscribe via RSS<\/a> for the next roundup.<\/p>\n

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Agents & Foundry Agent Service<\/h2>\n

Hosted Agents in Foundry are generally available (GA)<\/h3>\n

Build agents with your preferred framework and run them in Foundry’s managed runtime. We announced general availability for Hosted Agents on July 9<\/a> in a post by Tina Schuchman, who leads our Foundry platform engineering organization.<\/p>\n

Create an agent with the CLI<\/h4>\n

For this example, I’m using the OpenAI Agents SDK using Model Router with the Responses API protocol. The Foundry agents extension for Azure Developer CLI handles scaffolding, local testing, and deployment.<\/p>\n

If you’d rather work in VS Code than follow the CLI steps, I recommend installing Foundry Toolkit from the VS Code Marketplace<\/a>. For a guided visual workflow in the GitHub Copilot App, open Customize > Canvas > Microsoft Foundry to get started with Foundry Canvas (preview)<\/a>.<\/p>\n

Install Azure Developer CLI<\/a> 1.32.0 or later and the Foundry AI agents extension<\/a> 1.0.0-beta.13 (preview) or later. These are the end-of-August releases we’re using as the setup baseline:<\/p>\n

azd extension install azure.ai.agents --version 1.0.0-beta.13<\/code><\/pre>\n
Sign in with both azd auth login<\/code> for the CLI and az login<\/code> for the sample’s local Azure credential flow. You’ll need an existing Foundry project. Agent Service works with many models available in the Foundry model catalog<\/a>\u2014including model-router. If you haven’t deployed Model Router yet, follow the Model Router deployment guide<\/a> before continuing.<\/p>\n
Start from the OpenAI Agents SDK template<\/a>. Run initialization from a writable directory outside another Git repository. Replace the example project resource ID with yours, and model-router<\/code> with your deployment name if it differs:<\/p>\n
azd ai agent init openai-agents-hosted \\\r\n  --manifest https:\/\/github.com\/microsoft-foundry\/foundry-samples\/blob\/main\/samples\/python\/hosted-agents\/bring-your-own\/responses\/openai-agents-sdk\/azure.yaml \\\r\n  --project-id \"\/subscriptions\/<subscription-id>\/resourceGroups\/my-foundry-rg\/providers\/Microsoft.CognitiveServices\/accounts\/my-foundry-resource\/projects\/my-foundry-project\" \\\r\n  --model-deployment model-router \\\r\n  --agent-name openai-agents-router-demo \\\r\n  --deploy-mode code<\/code><\/pre>\n
I named my hosted agent openai-agents-router-demo<\/code> for this demo. You can omit --agent-name<\/code> to keep the template’s default name.<\/p>\n
Next, start the agent server on localhost to test it before deployment. The agent runs locally but still calls your deployed Model Router in Foundry:<\/p>\n
cd openai-agents-hosted\r\nazd ai agent run --no-client<\/code><\/pre>\n
Leave the server running, then invoke it from another terminal in the same directory:<\/p>\n
<\/i>Incurring costs<\/strong><\/p>invoke<\/code> incurs charges for the routed model. After deploy<\/code>, remote sessions also incur hosting charges<\/a>.<\/div><\/p>\n
azd ai agent invoke --local --new-session \\\r\n  \"Give a developer a two-sentence checklist for validating an AI agent before deployment.\"<\/code><\/pre>\n
When the local response looks right, check the source package excludes environment files and virtual environments, then publish the agent.<\/p>\n
azd deploy openai-agents-sdk-invocations\r\n\r\nazd ai agent invoke openai-agents-sdk-invocations --new-session \\\r\n  \"Give a developer a two-sentence checklist for validating an AI agent before deployment.\"<\/code><\/pre>\n
The commands select the template’s service key, openai-agents-sdk-invocations<\/code>; despite that label, this sample uses Responses. Omitting --local<\/code> sends the request to the deployed agent.<\/p>\n
Example cloud response:<\/p>\n
Before deployment, validate functionality and reliability: run unit\/integration\/end-to-end tests and benchmarks against acceptance metrics, stress and latency tests, adversarial and edge-case inputs, domain-shift simulations, and verify reproducibility and data lineage.\r\nAlso confirm safety, ethics, and operations readiness: perform bias\/harm audits and red-team exercises, ensure PII handling, access control and injection protections, clear explainability and user disclaimers, monitoring\/alerts\/SLAs, a human-in-the-loop and rollback\/kill-switch, and legal\/compliance sign-off with a staged rollout plan.<\/code><\/pre>\n
Voila! I chose my model, used a third-party, open-source framework, and deployed my agent my way. Now it’s your turn: start in code, use the CLI, try Foundry Toolkit in VS Code, explore Foundry Canvas in the GitHub Copilot app, or use Azure Skills<\/a> in your preferred agent development environment.<\/p>\n
Once you’ve installed Azure Skills, try this prompt:<\/p>\n
Use the Foundry OpenAI Agents SDK hosted-agent template with Model Router through the Responses API. Reuse my existing Foundry project and model deployment; ask me for their details. Help me test locally, then review the deployment plan and costs with me before I approve deployment.<\/code><\/pre>\n
You can stop the local server with Ctrl<\/kbd>+C<\/kbd>. Keep the deployed agent for the next step: talking to it.<\/p>\n
Give your agent a voice<\/h4>\n
Hosted Agents with Voice Live are also generally available (GA). We’ve tested our agent through text. Now let’s talk to it.<\/p>\n
Using the same hosted agent from our previous example, we’ll run a minimal, voice-only session locally and reuse the AudioProcessor<\/code> from the Voice Live quickstart. Replace the resource endpoint and project name with yours.<\/p>\n
First, install the Voice Live package and its audio dependency. On Linux, install PortAudio first:<\/p>\n
# Linux only\r\nsudo apt-get install -y portaudio19-dev libasound2-dev<\/code><\/pre>\n
Then install the Python packages:<\/p>\n
pip install --pre \"azure-ai-voicelive[aiohttp]\" azure-identity pyaudio<\/code><\/pre>\n
<\/i>Incurring costs<\/strong><\/p>Running this session incurs Voice Live charges<\/a> for text and audio tokens at the pricing tier associated with your agent’s model. The pricing guide also includes token-usage estimates and additional charges for custom speech, voices, or avatars.<\/div><\/p>\n
Download voicelive_client.py<\/code><\/a> to the same directory as your script. Then run:<\/p>\n
import asyncio\r\n\r\nfrom azure.ai.voicelive.aio import connect\r\nfrom azure.ai.voicelive.models import (\r\n    AudioEchoCancellation,\r\n    AudioNoiseReduction,\r\n    AzureStandardVoice,\r\n    InputAudioFormat,\r\n    Modality,\r\n    OutputAudioFormat,\r\n    RequestSession,\r\n    ServerVad,\r\n)\r\nfrom azure.identity.aio import DefaultAzureCredential\r\n\r\nfrom voicelive_client import AudioProcessor\r\n\r\nasync def main():\r\n    async with DefaultAzureCredential() as credential:\r\n        async with connect(\r\n            endpoint=\"https:\/\/<resource-name>.services.ai.azure.com\",\r\n            credential=credential,\r\n            agent_config={\r\n                \"agent_name\": \"openai-agents-router-demo\",\r\n                \"project_name\": \"<project-name>\",\r\n            },\r\n        ) as connection:\r\n            audio = AudioProcessor(connection)\r\n            audio.start_playback()\r\n            try:\r\n                await connection.session.update(\r\n                    session=RequestSession(\r\n                        modalities=[Modality.TEXT, Modality.AUDIO],\r\n                        voice=AzureStandardVoice(name=\"en-US-Ava:DragonHDLatestNeural\"),\r\n                        input_audio_format=InputAudioFormat.PCM16,\r\n                        output_audio_format=OutputAudioFormat.PCM16,\r\n                        turn_detection=ServerVad(),\r\n                        input_audio_echo_cancellation=AudioEchoCancellation(),\r\n                        input_audio_noise_reduction=AudioNoiseReduction(type=\"azure_deep_noise_suppression\"),\r\n                    )\r\n                )\r\n\r\n                async for event in connection:\r\n                    if event.type == \"session.updated\":\r\n                        audio.start_capture()\r\n                    elif event.type == \"response.audio.delta\":\r\n                        audio.queue_audio(event.delta)\r\n                    elif event.type == \"input_audio_buffer.speech_started\":\r\n                        audio.skip_pending_audio()\r\n                    elif event.type == \"error\":\r\n                        raise RuntimeError(event.error.message)\r\n                    elif event.type == \"response.done\" and (\r\n                        event.response.status in (\"failed\", \"incomplete\")\r\n                    ):\r\n                        raise RuntimeError(str(event.response))\r\n            finally:\r\n                audio.shutdown()\r\n\r\ntry:\r\n    asyncio.run(main())\r\nexcept KeyboardInterrupt:\r\n    pass<\/code><\/pre>\n
Voice Live supports more than 600 neural voices; you can explore the standard, HD, and custom options<\/a> and substitute the voice that fits your experience. Beyond voice choice, Voice Live includes real-time audio processing features for natural turn-taking and clearer input. Server-side voice activity detection (VAD) recognizes when I start and stop speaking; the speech_started<\/code> handler then stops pending playback so I can interrupt the agent naturally. Echo cancellation prevents speaker output from feeding back into the microphone, while deep noise suppression reduces background noise. Together, these features enable more natural turn-taking in noisy, speaker-enabled scenarios such as customer support, field service, and in-vehicle assistants.<\/p>\n
Sample output:<\/p>\n
<\/p>\n
https:\/\/devblogs.microsoft.com\/foundry\/wp-content\/uploads\/sites\/89\/2026\/09\/voice-live-hosted-agent-sample.mp3<\/a><\/audio><\/p>\n
Learn more: Voice Live hosted-agent integration<\/a>, Responses-protocol setup<\/a>, shared Python voice client<\/a>, and Voice Live Python quickstart<\/a>.<\/p>\n
You can delete the deployed agent using its agent name:<\/p>\n
azd ai agent delete openai-agents-router-demo<\/code><\/pre>\n
Stuck? See Hosted Agent troubleshooting<\/a>, including help with active sessions that block deletion.<\/p>\n
Run within your network boundary<\/h4>\n
If your agent needs private access to storage, databases, or Key Vault, plan its VNet integration<\/a> alongside the deployment. Subnet capacity, DNS, private endpoints, and outbound access all affect which resources it can reach.<\/p>\n
Get started<\/h4>\n
Create a Hosted Agent<\/a><\/div>\n