agent-framework/python/samples/05-end-to-end/purview_agent

Purview Policy Enforcement Sample (Python)

This getting-started sample shows how to attach Microsoft Purview policy evaluation to an Agent Framework Agent using the middleware approach.

What this sample demonstrates:

1. Configure a Foundry chat client
2. Add Purview policy enforcement middleware (PurviewPolicyMiddleware)
3. Add Purview policy enforcement at the chat client level (PurviewChatPolicyMiddleware)
4. Implement a custom cache provider for advanced caching scenarios
5. Run conversations and observe prompt / response blocking behavior

Note: Caching is automatic and enabled by default with sensible defaults (30-minute TTL, 200MB max size).

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1. Setup

Required Environment Variables

Variable	Required	Purpose
FOUNDRY_PROJECT_ENDPOINT	Yes	Azure AI Foundry project endpoint, for example https://<resource>.services.ai.azure.com/api/projects/<project>
FOUNDRY_MODEL	Optional	Model deployment name (defaults to gpt-4o-mini)
PURVIEW_CLIENT_APP_ID	Yes*	Client (application) ID used for Purview authentication
PURVIEW_USE_CERT_AUTH	Optional (true/false)	Switch between certificate and interactive auth
PURVIEW_TENANT_ID	Yes (when cert auth on)	Tenant ID for certificate authentication
PURVIEW_CERT_PATH	Yes (when cert auth on)	Path to your .pfx certificate
PURVIEW_CERT_PASSWORD	Optional	Password for encrypted certs

2. Auth Modes Supported

A. Interactive Browser Authentication (default)

Opens a browser on first run to sign in.

$env:FOUNDRY_PROJECT_ENDPOINT = "https://<resource>.services.ai.azure.com/api/projects/<project>"
$env:FOUNDRY_MODEL = "gpt-4o-mini"
$env:PURVIEW_CLIENT_APP_ID = "00000000-0000-0000-0000-000000000000"

B. Certificate Authentication

For headless / CI scenarios.

$env:PURVIEW_USE_CERT_AUTH = "true"
$env:PURVIEW_TENANT_ID = "<tenant-guid>"
$env:PURVIEW_CERT_PATH = "C:\path\to\cert.pfx"
$env:PURVIEW_CERT_PASSWORD = "optional-password"

Certificate steps (summary): create / register entra app, generate certificate, upload public key, export .pfx with private key, grant required Graph / Purview permissions.

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3. Run the Sample

From repo root:

cd python/samples/05-end-to-end/purview_agent
python sample_purview_agent.py

If interactive auth is used, a browser window will appear the first time.

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4. How It Works

The sample demonstrates four integration scenarios. Each scenario runs the same three-message sequence via run_policy_flow(...):

1. good (cold cache) - a benign prompt that exercises the cold-cache parallel ProtectionScopes warmup + foreground ProcessContent path.
2. expected block - a sensitive prompt containing the Visa test credit card number 4111 1111 1111 1111. If the tenant has a DLP policy for Microsoft 365 Copilot and AI apps targeting the Credit Card sensitive info type with a Block action, this prompt returns the configured blocked_prompt_message (default: Prompt blocked by policy). If no DLP policy applies, the prompt is allowed (the LLM may still decline on its own, but that is a model-level response, not a Purview block).
3. good (warm cache) - a second benign prompt that exercises the warm-cache path. The custom cache provider scenario prints Cache HIT for the same protection-scopes key, confirming the cache and middleware state survive a prior block.

A. Agent Middleware (run_with_agent_middleware)

1. Builds a Foundry chat client (using the environment project endpoint / deployment)
2. Chooses credential mode (certificate vs interactive)
3. Creates PurviewPolicyMiddleware with PurviewSettings
4. Injects middleware into the agent at construction
5. Runs the three-message good -> block -> good orchestration
6. Prints ALLOWED or BLOCKED per message, plus the model response
7. Uses default caching automatically

B. Chat Client Middleware (run_with_chat_middleware)

1. Creates a chat client with PurviewChatPolicyMiddleware attached directly
2. Policy evaluation happens at the chat client level rather than agent level
3. Demonstrates an alternative integration point for Purview policies
4. Runs the same good -> block -> good orchestration
5. Uses default caching automatically

C. Custom Cache Provider (run_with_custom_cache_provider)

1. Implements the CacheProvider protocol with a custom class (SimpleDictCacheProvider)
2. Shows how to add custom logging and metrics to cache operations
3. The custom provider must implement three async methods:
   • async def get(self, key: str) -> Any | None
   • async def set(self, key: str, value: Any, ttl_seconds: int | None = None) -> None
   • async def remove(self, key: str) -> None
4. Runs the good -> block -> good orchestration and prints Cache MISS/Cache HIT traces alongside policy outcomes, showing the cold-cache warmup populating the cache and warm-cache requests skipping ProtectionScopes.

D. Default Cache (run_with_default_cache)

1. Same as the agent middleware path but with explicit cache TTL and size limits in PurviewSettings
2. Uses the default in-memory CacheProvider
3. Runs the good -> block -> good orchestration

Policy Behavior: Prompt blocks substitute the configured blocked_prompt_message (default Prompt blocked by policy) and terminate the agent run early. Response blocks substitute blocked_response_message. The LLM is never called for a blocked prompt.

Seeing a real BLOCKED outcome: The middle prompt only returns BLOCKED if the tenant actually has a Purview DLP policy that matches the request. Specifically, all of the following must be true:

1. The Entra app id used by PURVIEW_CLIENT_APP_ID (the same id Agent Framework sends as policyLocationApplication.value) is registered as an integrated AI app in Purview (Settings -> AI app and agent locations).
2. A DLP policy in the tenant targets the location Microsoft 365 Copilot and AI apps, scoped to that app id (or All apps).
3. The policy has a rule with the condition Content contains -> Sensitive info types -> Credit Card Number and an action of Restrict access to Microsoft 365 Copilot and AI apps -> Block.
4. The policy is On (not Test mode without notifications).
5. The signed-in user is in the policy's user scope.
6. Required Graph delegated permissions are admin-consented: ProtectionScopes.Compute.All, Content.Process.All, ContentActivity.Write.

If any of those are missing, the credit card prompt is allowed at the Purview layer. The model itself may still decline on its own; that response is a model-level refusal, not a Purview block. The cold/warm cache orchestration is still demonstrated either way - the Cache MISS -> Cache HIT trace from the custom cache scenario does not depend on a block firing.

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5. Code Snippets

Agent Middleware Injection

agent = Agent(
	client=client,
	instructions="You are good at telling jokes.",
	name="Joker",
	middleware=[
		PurviewPolicyMiddleware(credential, PurviewSettings(app_name="Sample App"))
	],
)

Custom Cache Provider Implementation

This is only needed if you want to integrate with external caching systems.

class SimpleDictCacheProvider:
    """Custom cache provider that implements the CacheProvider protocol."""

    def __init__(self) -> None:
        self._cache: dict[str, Any] = {}

    async def get(self, key: str) -> Any | None:
        """Get a value from the cache."""
        return self._cache.get(key)

    async def set(self, key: str, value: Any, ttl_seconds: int | None = None) -> None:
        """Set a value in the cache."""
        self._cache[key] = value

    async def remove(self, key: str) -> None:
        """Remove a value from the cache."""
        self._cache.pop(key, None)

# Use the custom cache provider
custom_cache = SimpleDictCacheProvider()
middleware = PurviewPolicyMiddleware(
    credential,
    PurviewSettings(app_name="Sample App"),
    cache_provider=custom_cache,
)

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