Create a prompt agent with Microsoft Foundry Toolkit for Visual Studio Code

Create, test, and version prompt agents with Agent Builder in Microsoft Foundry Toolkit. Add tools, review conversations, evaluate, and generate client code.

Use Agent Builder in Microsoft Foundry Toolkit for Visual Studio Code to configure a prompt agent’s model, instructions, and tools. Test the agent in the playground, save changes as versions, and generate client code to call it from an application.

This article starts with prompt agents saved in Foundry. Agent Builder also supports locally stored prompts, which have different storage, tool, and evaluation options. For code-based alternatives, see Create an agent.

Prerequisites

• Install Microsoft Foundry Toolkit for Visual Studio Code.
• Select a Foundry project. You need permission to create agents and use the project’s models and tools. See Foundry role-based access control.
• A model deployment in that project. See Set up Foundry resources.

Local prompt development doesn’t require a Foundry project unless you use Foundry resources.

Create a prompt agent

Start with a model and instructions, then save and test the agent before adding tools.

1. In the Foundry Toolkit view, select Developer Tools > Build > Create Agent.
2. Select Build an agent to open Agent Builder.
3. Under Basic Information, enter an Agent name. Start and end the name with a letter or number. You can use hyphens between them.
4. Select a Foundry-hosted model from Model. Use Browse models if you need to add a model.
5. In Instructions, describe the task, required behavior, and expected response. For example, ask the agent to summarize a software issue by identifying the reported problem, reproduction steps, and expected behavior. Tell it to ask for missing information instead of inventing details.
6. Select Save to Foundry.
7. On the Playground tab, enter a request and select Send message. Ask a follow-up question to test the conversation.
8. Review whether the response follows your instructions. Refine the instructions, save, and repeat as needed.

If Developer Tools uses Group by Resource, Create Agent is under Agent Dev Tools instead of Build. Another entry point is My Resources > Agents > Prompt Agent > Add Prompt Agent. Select an existing agent in that list to edit it.

Choose where to save

The available save actions depend on the selected model and tools.

Configuration	Save destination
Foundry-hosted model without tools.	Save to Foundry, with Save to Local available in the save menu.
Foundry-hosted model with Foundry tools.	Save to Foundry.
A model from another provider, or a configuration with local tools.	Save to Local.

The Microsoft Foundry and Local badges show where the agent is stored. A locally stored prompt can still call a cloud model. Local storage doesn’t mean that inference runs on your machine.

Save drafts and versions

Agent Builder separates local recovery drafts from saved agent versions.

Action	Result
Edit the configuration.	Agent Builder stores a local recovery draft. This action doesn’t create a Foundry version.
Run a new, unsaved agent.	Agent Builder validates the agent name and model, and saves the agent before the first run. It prefers Foundry when the configuration supports that destination.
Select Save to Foundry after editing a saved Foundry agent.	Agent Builder saves the configuration as a new version in the project.

If Agent Builder finds a recovery draft, choose Restore Draft or Discard. Save important changes explicitly before switching agents or versions.

You can test unsaved changes to an existing Foundry agent. That run uses the edited configuration rather than a saved agent-version reference. Save before you rely on version-linked conversations, tracing, or generated client code.

Save to Foundry saves an agent version. It doesn’t publish an agent application with a stable application endpoint. For that separate operation, see Publish an agent application.

Select an agent version

Use the version selector next to the agent name to load a saved version. Foundry agent versions are immutable. To keep edits made from an earlier version, select Save to Foundry to create a new version.

The selected version determines the conversation history shown in Agent Builder and the version referenced by generated client code. For service versioning details, see Save changes as versions.

Add tools to a Foundry agent

Tools connect the agent to information and actions outside the model. Available tools depend on the model, your permissions, and the resources in your project. Use Tool Catalog to configure shared connections and authentication, then attach them in Agent Builder.

1. Open a saved Foundry agent on the Playground tab.
2. In Tool, select + > Add tools.
3. In Select a tool, choose a connection from Configured, or use Catalog to find a tool.
4. Complete the required configuration, then select Add Tool.
5. Select Save to Foundry.
6. Send a request that requires the tool, and review its inputs and results. If the agent requests approval, select Approve or Deny for that call.

To review the tool-call approval settings for an MCP tool, open its options and select Configure. You can require approval, approve all tools automatically, or approve specific tools automatically. Review the choice before saving.

Approval settings don’t grant access to the underlying service. For service permissions, see Agent identity concepts.

Use a toolbox (preview)

A toolbox groups reusable tools behind a managed MCP endpoint. Skills and tool search are preview features.

Toolbox integration in prompt agents is in preview and off by default. In Visual Studio Code settings, enable windowsaistudio.enableToolboxInPromptAgent to show toolbox attachment controls.

An agent uses either a toolbox or individual Foundry tools. Attaching a toolbox replaces its individual tools. With a toolbox attached, Add tools adds tools inside that toolbox.

Toolbox edits are staged until you save the agent, when they create a new toolbox version. Toolboxes are shared resources; review both the toolbox and agent changes before saving.

1. Open a saved Foundry agent.
2. In Tool, select + > Browse toolboxes.
3. Select a toolbox, and review its version, tools, and skills.
4. Select Add.
5. Expand the toolbox card to inspect its contents and review approval settings.
6. Select Save to Foundry, then test a request that uses its tools.

You can also select Add to Prompt Agent from the Toolbox resource list. For toolbox creation and shared connections, see Tool Catalog.

Manage an attached toolbox

Use the toolbox card’s More options menu, and then save the agent after you make changes.

Action	Effect
Configure	Change tool-call approval settings for this agent.
Switch version	Select another version of the attached toolbox.
Replace	Choose a different toolbox.
Remove	Detach the toolbox from this agent.
Opt out	Keep its tools as individual agent tools. The agent no longer has access to toolbox skills, tool search, and versioning and reuse as a set.

Connect another agent with A2A (preview)

Agent-to-Agent (A2A) connections let a prompt agent invoke an A2A-compatible agent as a tool. You can attach one directly or through a toolbox. Direct attachment doesn’t require the toolbox opt-in setting.

1. Open a saved Foundry agent.

2. In Tool, select + > Add agent (A2A).

3. In Connect an A2A agent, choose the appropriate tab.

   Tab	What to provide
   Configured	Select an existing A2A connection. This tab appears when configured connections exist.
   Catalog	Select an agent from the Foundry account catalog. Complete the agent-card and authentication steps when prompted.
   Custom	Enter a name, a valid HTTPS endpoint, and the agent-card path. Select Authenticate when retrieving agent card if required.

4. Complete the dialog to connect or add the agent.

5. Select Save to Foundry, then test a request that requires the connection.

For endpoint setup, agent cards, identity options, and permissions, see Enable an A2A endpoint.

Review conversations and switch agents

The Playground contains the current test conversation. Select Clear all messages to start a fresh conversation.

For a saved Foundry agent, select Conversations to review history for the selected version. Select a conversation to inspect its messages and response details. Opening history doesn’t resume that conversation in the playground.

Use the agent selector at the top of Agent Builder to switch between local prompts and Foundry prompt agents. Check the storage badge and version after switching. The Conversations tab is available for saved Foundry agents, not local prompts.

Generate and improve instructions

Use Generate to draft instructions from a task description or Improve to revise existing instructions. When the field is empty, Inspire me can provide a starting idea.

1. Select a model that supports instruction generation.
2. Under Instructions, select Generate if the field is empty, or Improve if it contains instructions.
3. Describe the task or change. For an existing Foundry agent, improvement suggestions are optional.
4. Select Generate or Improve in the dialog.
5. Review the revised instructions and test representative requests.
6. Select Save to Foundry to keep the configuration.

For saved Foundry agents, these actions use Foundry Prompt Optimizer. If the optimization API doesn’t support the model, the Toolkit falls back to standard prompt generation when supported. Selecting a Foundry model in a new draft doesn’t by itself make the draft a saved Foundry agent.

Evaluate a Foundry prompt agent

Save the configuration you want to evaluate, and then select Evaluation.

• Select Scaffold Evaluation Code to generate a local Python evaluation project. Follow the generated instructions to configure and run it.
• Select the Foundry link for guided evaluation setup.

Review the generated evaluation configuration before running it. The scaffold identifies the agent by name; don’t assume it pins the version you selected in Agent Builder.

This tab differs from the local prompt dataset view. For service evaluation guidance, see Evaluate your agents.

Generate client code

After saving a Foundry agent, use the View Code menu to call it from an application.

Action	Output
View Code	A Python project that calls the existing agent. Choose a folder, then follow its README.md for dependencies, configuration, and authentication.
View Snippets	A Python snippet in an editor that calls the existing agent.

Both outputs reference the selected saved version. Save your edits before generating code if the application needs the revised configuration.

Client code doesn’t convert a prompt agent into a hosted agent. For direct SDK use, see the prompt-agent quickstart.

Work with local prompts

Choose local storage to use a model from another provider or test local tools. Select a model, enter instructions, and select Save to Local. With a Foundry model, use the save menu when local storage is available.

Use Save to Local to keep local changes. Local saves don’t create Foundry versions or Foundry conversation-history records.

Connect local tools

For a local prompt, select Tool > + > MCP Server to choose a server and its tools. For configuration and runtime requirements, see Connect a local MCP server.

To test a function schema without implementing an external service:

1. Select Tool > + > Custom Tool.
2. Choose By Example or Upload Existing Schema.
3. Provide the schema, name, and description, then add the tool.
4. Enter a mock response in the tool card.
5. Run the prompt and inspect how the model uses the response.

A mock response doesn’t call an external API. A configured MCP server can execute its tools.

Configure structured output

For a local prompt with a model that supports structured output:

1. Open Settings next to the model selector.
2. Under Structure Output, select json_schema.
3. In Select JSON Schema, choose Use Example or Upload File.
4. Review the schema and select Select.
5. Save the local prompt and run a request to inspect its output.

Available formats depend on the model. These steps apply to local prompt execution, not the response schema of a saved Foundry prompt agent.

Evaluate local prompts with dataset variables

For a saved local prompt, the Evaluation tab provides dataset-based evaluation. Use variables in instructions to run the same prompt with different dataset values.

For example, Summarize the issue for {{audience}}. uses a dataset column named audience. Supply a value for each test case. The local batch runner substitutes that value when it runs the prompt.

The Agent Builder playground doesn’t have a separate Variables panel. For dataset import, evaluators, and result comparison, see Evaluate models, prompts, and agents.

Generate code for a local prompt

Select View Code to generate model integration code. Available SDK, authentication, and language choices depend on the provider and model. These options differ from the Foundry agent client project and snippet actions.

Related content

Use these guides to develop and publish your agent:

• Create an agent with Microsoft Foundry Toolkit
• Agent development lifecycle
• Publish an agent application