Quickstart: Add a Foundry IQ knowledge base to a hosted agent with a toolbox

Provision a Foundry IQ knowledge base, expose it through a Foundry toolbox, and deploy a Python hosted agent that grounds its answers in the knowledge base.

Items marked (preview) in this article are currently in public preview. This preview is provided without a service-level agreement, and we don’t recommend it for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews.

In this quickstart, you ground a hosted agent in a Foundry IQ in Foundry Tools knowledge base, and you reach that knowledge base through a toolbox. A knowledge base in Foundry IQ uses Azure AI Search agentic retrieval to plan a query, search your data, rerank results, and synthesize a cited answer. The toolbox exposes the knowledge base’s Model Context Protocol (MCP) endpoint as a single tool, and the agent calls that tool with its own managed identity for keyless authentication.

You complete three parts:

• Provision a knowledge base one time with a Python script that creates a search index, seeds it with sample documents, and builds the knowledge source and knowledge base.
• Create a toolbox connection that targets the knowledge base’s MCP endpoint and authenticates with the agent’s managed identity.
• Deploy a hosted agent that discovers the knowledge_base_retrieve tool through the toolbox and grounds its answers in the retrieved sources.

The agent code, the knowledge base provisioning script, the toolbox definition, and an automation hook come from the Foundry IQ sample, so you focus on the workflow rather than the implementation.

Prerequisites

This quickstart builds on the hosted-agent toolchain. Complete the Prerequisites in the hosted agent quickstart first, which cover the Azure subscription, project roles, Python, the Azure Developer CLI (azd), and the microsoft.foundry extension.

You also need:

• An Azure AI Search service that supports agentic retrieval. Enable a system-assigned managed identity on the service, and enable role-based access control. In the Azure portal, on the search service, go to Settings > Keys, and set API Access control to Both or Role-based access control.
• A chat model deployment in your Foundry project, such as gpt-5.4-mini. The knowledge base uses the same model to synthesize answers.

Required roles

Assign the following roles before you provision. The knowledge base calls the model with the search service’s managed identity, so that identity needs access to your Foundry account.

Identity	Role	Scope	Why
Your user account	Search Service Contributor	Search service	Create the index, knowledge source, and knowledge base.
Your user account	Search Index Data Contributor	Search service	Upload the sample documents.
Search service managed identity	Cognitive Services User	Foundry account	Call the model for keyless answer synthesis.
Agent managed identity	Search Index Data Reader	Search service	Let the deployed agent retrieve from the knowledge base at query time.

The agent’s managed identity exists only after you deploy the agent, so you assign Search Index Data Reader in Step 6.

Step 1: Initialize the hosted agent

Initialize a hosted agent from the Foundry IQ sample. The sample includes the agent code, the provision_kb.py script, the toolbox.yaml definition, and the automation hooks. Run these commands in an empty directory.

mkdir my-foundry-iq-agent
cd my-foundry-iq-agent
azd ai agent init -m "https://github.com/microsoft-foundry/foundry-samples/blob/main/samples/python/hosted-agents/agent-framework/responses/17-foundry-iq-toolbox/azure.yaml"

Follow the prompts to select your subscription, Foundry project, and a chat model deployment such as gpt-5.4-mini. If you don’t have a project, the flow guides you through creating one. Initialization sets the selected project as the active project and copies the sample files into a new service directory, src/agent-framework-foundry-iq-knowledge-base-responses/.

Step 2: Enable one-command provisioning

The sample includes a postprovision hook that runs the knowledge base script, creates the toolbox connection, creates the toolbox, and sets the TOOLBOX_ENDPOINT environment variable every time you provision. Wire the hook into the azure.yaml file that azd ai agent init generated.

Open azure.yaml and add this top-level block. The postprovision hook must be at the top level because service-scoped hooks support only the package and deploy lifecycle.

hooks:
  postprovision:
    posix:
      shell: sh
      run: ./src/agent-framework-foundry-iq-knowledge-base-responses/hooks/postprovision.sh
    windows:
      shell: pwsh
      run: ./src/agent-framework-foundry-iq-knowledge-base-responses/hooks/postprovision.ps1

The hook locates its own directory, so it works no matter which directory azd runs it from.

Step 3: Provision Azure resources and the knowledge base

1. Point the hook at your existing search service:

   azd env set AZURE_SEARCH_ENDPOINT "https://<your-search>.search.windows.net"
   

   The hook derives the model endpoint for answer synthesis from your project endpoint. To use a different Azure OpenAI resource, set it first with azd env set AZURE_OPENAI_ENDPOINT "https://<account>.openai.azure.com".

2. Provision the resources and the knowledge base:

   azd provision
   

   azd provision creates or reuses your Foundry project and model deployment. The postprovision hook then:

   1. Runs provision_kb.py to create the foundry-iq-index search index, seed the Earth at night documents, and build the foundry-iq-ks knowledge source and foundry-iq-kb knowledge base. The script stores the knowledge base’s MCP endpoint as KB_MCP_ENDPOINT.
   2. Creates the knowledge-base-mcp connection. This connection targets the knowledge base’s MCP endpoint and uses Agentic Identity, so the agent’s managed identity authenticates with no stored secret.
   3. Creates the knowledge-base toolbox from toolbox.yaml, which exposes the knowledge_base_retrieve tool.
   4. Sets TOOLBOX_ENDPOINT so the agent connects to the toolbox.

The connection uses the agentic-identity authentication type, which forwards the agent’s managed identity to the search service. A user token is rejected on purpose, so retrieval succeeds only after you deploy the agent and grant its identity access in Step 6.

Step 4: Run the agent locally

1. Start the agent:

   azd ai agent run
   

   This command creates a virtual environment, installs dependencies, and serves the agent on http://localhost:8088. Preview packages can produce pip warnings during setup. These warnings are nonblocking.

2. In a separate terminal, ask the agent a question that the knowledge base can answer:

   azd ai agent invoke --local "What can you tell me about the Earth at night?"
   

   The local agent connects to the toolbox, but retrieval from the knowledge base requires the deployed agent’s identity. Use the local run to confirm the agent starts and reaches the toolbox. You verify grounded answers after you deploy.

Step 5: Deploy to Foundry Agent Service

Build and deploy the agent container:

azd deploy

When the command finishes, the output shows links to the agent playground and the agent endpoint.

Step 6: Grant the agent access to the knowledge base

The deployed agent retrieves from the knowledge base with its own managed identity. Grant that identity the Search Index Data Reader role on the search service.

1. Find the agent’s identity object ID in the Foundry portal. Go to Agents, select your agent, and then select Identity.

2. Assign the role on the search service:

   searchId=$(az search service show -n <search-name> -g <resource-group> --query id -o tsv)
   az role assignment create --assignee-object-id <agent-identity-object-id> \
     --assignee-principal-type ServicePrincipal \
     --role "Search Index Data Reader" --scope $searchId
   

   In PowerShell, use a line continuation backtick (`) instead of \.

Step 7: Invoke the deployed agent

Send a question to the deployed agent and confirm it answers from the knowledge base:

azd ai agent invoke "What can you tell me about the Earth at night?"

The agent returns a grounded answer with citations to the sample documents. Try a few more questions to see retrieval across the sample corpus:

azd ai agent invoke "Why do some lights appear over the open ocean?"
azd ai agent invoke "How is nighttime imagery used to study light pollution?"

Clean up resources

Delete the resources when you’re finished so you stop incurring charges.

1. Delete the knowledge base, knowledge source, and index from your search service. In the Azure portal, on the search service, delete foundry-iq-kb, foundry-iq-ks, and the foundry-iq-index index. You can keep the search service for other projects.

2. Delete the agent and its Azure resources:

azd down permanently deletes every resource in the resource group, including the Foundry project, model deployments, Container Registry, and the hosted agent. If you provisioned into a resource group that contains other resources, those resources are deleted too.

azd down

Troubleshooting

Issue	Solution
azd provision fails with AZURE_SEARCH_ENDPOINT is not set	Run azd env set AZURE_SEARCH_ENDPOINT "https://<your-search>.search.windows.net" before you provision.
provision_kb.py fails with a permissions error	Confirm your account has Search Service Contributor and Search Index Data Contributor on the search service.
The knowledge base fails to synthesize answers	Confirm the search service managed identity has Cognitive Services User on the Foundry account, and that the search service has a system-assigned managed identity.
The deployed agent returns I don't know for in-scope questions	Confirm you granted the agent’s managed identity Search Index Data Reader on the search service after the first deploy.
Data-plane calls to the search service return 403	In the Azure portal, on the search service, set API Access control to Both or Role-based access control.

What you learned

In this quickstart, you:

• Created a Foundry IQ knowledge base with a search index, knowledge source, and sample documents.
• Exposed the knowledge base through a toolbox connection that uses the agent’s managed identity.
• Deployed a hosted agent that grounds its answers in the knowledge base with citations.

Next step

Give a hosted agent persistent memory

Related content

• What is Foundry IQ?
• Connect a Foundry IQ knowledge base to Foundry Agent Service
• Curate an intent-based toolbox in Foundry
• Quickstart: Deploy your first hosted agent