Azure · howieleung · Jul 1, 2026
@@ -14,6 +14,11 @@
 
 * Placeholder
 
+### Sample updates
+
+* Added new Hosted Agent sample `sample_agent_user_identity_isolation.py` under `samples/hosted_agents/`, demonstrating per-user response-chain isolation with delegated end-user identities sent in the `x-ms-user-identity` header.
+* Updated Hosted Agent toolbox asset `samples/hosted_agents/assets/toolbox-agent/main.py` to use `FoundryToolbox` and `as_skills_provider()` for toolbox MCP skill discovery and wiring, replacing the earlier manual MCP session, auth, and HTTP client setup.
+* Updated Hosted Agent asset requirements under `samples/hosted_agents/assets/toolbox-agent/requirements.txt` to use new versions of `agent-framework-foundry` and `agent-framework-foundry-hosting`
 
 ## 2.3.0 (2026-07-01)
 

@@ -2,5 +2,5 @@
   "AssetsRepo": "Azure/azure-sdk-assets",
   "AssetsRepoPrefixPath": "python",
   "TagPrefix": "python/ai/azure-ai-projects",
-  "Tag": "python/ai/azure-ai-projects_762155b7e7"
+  "Tag": "python/ai/azure-ai-projects_82895b86e0"
 }
@@ -17,6 +17,8 @@
     "dargilco",
     "dedup",
     "deser",
+    "devtools",
+    "dotenv",
     "evals",
     "FineTuning",
     "fspath",
@@ -35,10 +37,13 @@
     "reraises",
     "simpleqna",
     "skoid",
+    "testutils",
     "Tadmaq",
     "Udbk",
     "UPIA",
+    "unsanitized",
     "Vnext",
+    "westus",
     "xhigh",
     "likert"
   ],

@@ -64,7 +64,7 @@
 
 with (
     DefaultAzureCredential() as credential,
-    AIProjectClient(endpoint=endpoint, credential=credential, allow_preview=True) as project_client,
+    AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
 ):
 
     # ------------------------------------------------------------------

@@ -63,7 +63,7 @@
 async def main() -> None:
     async with (
         DefaultAzureCredential() as credential,
-        AIProjectClient(endpoint=endpoint, credential=credential, allow_preview=True) as project_client,
+        AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
     ):
 
         # ------------------------------------------------------------------

@@ -56,7 +56,7 @@
 
 with (
     DefaultAzureCredential() as credential,
-    AIProjectClient(endpoint=endpoint, credential=credential, allow_preview=True) as project_client,
+    AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
 ):
 
     # ------------------------------------------------------------------

@@ -39,7 +39,7 @@
 
 with (
     DefaultAzureCredential() as credential,
-    AIProjectClient(endpoint=endpoint, credential=credential, allow_preview=True) as project_client,
+    AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
 ):
 
     # ------------------------------------------------------------------

@@ -63,7 +63,7 @@
 
 with (
     DefaultAzureCredential() as credential,
-    AIProjectClient(endpoint=endpoint, credential=credential, allow_preview=True) as project_client,
+    AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
     project_client.get_openai_client() as openai_client,
 ):
 

@@ -0,0 +1,40 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import os
+
+from agent_framework import Agent
+from agent_framework.foundry import FoundryChatClient
+from agent_framework_foundry_hosting import ResponsesHostServer  # type: ignore[import-untyped]
+from azure.identity import DefaultAzureCredential
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+async def main() -> None:
+    project_endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
+    deployment = os.environ["FOUNDRY_MODEL_NAME"]
+    store_responses = os.environ.get("AGENT_STORE_RESPONSES", "false").lower() == "true"
+
+    with DefaultAzureCredential() as credential:
+        agent = Agent(
+            client=FoundryChatClient(
+                project_endpoint=project_endpoint,
+                model=deployment,
+                credential=credential,
+                allow_preview=True,
+            ),
+            name=os.environ.get("AGENT_NAME", "BASIC_AGENT"),
+            instructions=os.environ.get(
+                "AGENT_INSTRUCTIONS",
+            ),
+            default_options={"store": store_responses},
+        )
+
+        server = ResponsesHostServer(agent)
+        await server.run_async()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
@@ -0,0 +1,3 @@
+agent-framework-foundry==1.10.0
+agent-framework-foundry-hosting>=1.0.0a260630
+python-dotenv
@@ -2,78 +2,53 @@
 
 import asyncio
 import os
-from collections.abc import Callable, Generator
 
-import httpx
-from typing import Any, cast
-from agent_framework import Agent, SkillsProvider
+from agent_framework import Agent
 from agent_framework.foundry import FoundryChatClient
-from agent_framework_foundry_hosting import ResponsesHostServer  # type: ignore[import-untyped]
-from azure.identity import DefaultAzureCredential, get_bearer_token_provider
+from agent_framework_foundry_hosting import FoundryToolbox, ResponsesHostServer
+from azure.identity import DefaultAzureCredential
 from dotenv import load_dotenv
-from mcp.client.session import ClientSession
-from mcp.client.streamable_http import streamable_http_client
-
-MCPSkillsSource = cast(Any, __import__("agent_framework", fromlist=["MCPSkillsSource"]).MCPSkillsSource)
 
+# Load environment variables from .env file
 load_dotenv()
 
 
-class ToolboxAuth(httpx.Auth):
-    """Attach a fresh Foundry bearer token to every request."""
-
-    requires_response_body = True
-
-    def __init__(self, token_provider: Callable[[], str]) -> None:
-        self._token_provider = token_provider
-
-    def auth_flow(self, request: httpx.Request) -> Generator[httpx.Request, httpx.Response, None]:
-        request.headers["Authorization"] = f"Bearer {self._token_provider()}"
-        yield request
-
-
 async def main() -> None:
-    project_endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
-    deployment = os.environ["FOUNDRY_MODEL_NAME"]
-    toolbox_mcp_url = os.environ["MCP_SERVER_URL"]
-    store_responses = os.environ.get("AGENT_STORE_RESPONSES", "false").lower() == "true"
-
     credential = DefaultAzureCredential()
-    token_provider = get_bearer_token_provider(credential, "https://ai.azure.com/.default")
-
-    async with (
-        httpx.AsyncClient(
-            auth=ToolboxAuth(token_provider),
-            headers={"Foundry-Features": "Routines=V1Preview"},
-            timeout=httpx.Timeout(30.0, read=300.0),
-            follow_redirects=True,
-        ) as http_client,
-        streamable_http_client(
-            url=toolbox_mcp_url,
-            http_client=http_client,
-        ) as (read, write, _),
-        ClientSession(read, write) as session,
-    ):
-        await session.initialize()
-
-        skills_provider = SkillsProvider(MCPSkillsSource(client=session))
-
-        agent = Agent(
-            client=FoundryChatClient(
-                project_endpoint=project_endpoint,
-                model=deployment,
-                credential=credential,
-            ),
-            name=os.environ.get("AGENT_NAME", "TOOLBOX_AGENT"),
-            instructions=os.environ.get(
-                "AGENT_INSTRUCTIONS",
-            ),
-            context_providers=[skills_provider],
-            default_options={"store": store_responses},
-        )
 
-        server = ResponsesHostServer(agent)
-        await server.run_async()
+    # FoundryToolbox resolves the toolbox endpoint from the environment
+    # (TOOLBOX_ENDPOINT, or FOUNDRY_PROJECT_ENDPOINT + TOOLBOX_NAME), authenticates
+    # every request with the credential, and forwards the platform per-request
+    # call-id. ``load_tools=False`` keeps the toolbox's tools hidden so only its
+    # Agent Skills (SEP-2640) are surfaced; passing it via ``tools=`` connects the
+    # MCP session that ``as_skills_provider()`` reads from.
+    toolbox = FoundryToolbox(url=os.environ["MCP_SERVER_URL"], credential=credential, load_tools=False)
+
+    # as_skills_provider() discovers skills from skill://index.json on the toolbox
+    # MCP session and exposes them as an agent context provider; SKILL.md bodies are
+    # fetched on demand via resources/read.
+    skills_provider = toolbox.as_skills_provider()
+
+    client = FoundryChatClient(
+        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        model=os.environ["FOUNDRY_MODEL_NAME"],
+        credential=credential,
+    )
+
+    agent = Agent(
+        client=client,
+        name=os.environ.get("AGENT_NAME", "hosted-toolbox-mcp-skills"),
+        instructions="You are a helpful assistant.",
+        tools=toolbox,
+        context_providers=[skills_provider],
+        # History will be managed by the hosting infrastructure, thus there
+        # is no need to store history by the service. Learn more at:
+        # https://developers.openai.com/api/reference/resources/responses/methods/create
+        default_options={"store": False},
+    )
+
+    server = ResponsesHostServer(agent)
+    await server.run_async()
 
 
 if __name__ == "__main__":

@@ -1,10 +1,3 @@
-agent-framework-foundry==1.8.2
-agent-framework-foundry-hosting==1.0.0a260618
-azure-ai-agentserver-core
-azure-ai-agentserver-invocations
-azure-ai-agentserver-responses
-azure-ai-projects
-azure-identity
-httpx
-mcp
+agent-framework-foundry==1.10.0
+agent-framework-foundry-hosting>=1.0.0a260630
 python-dotenv