foundry-agent-chat

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SKILL.md (first lines)

# Foundry Agent Chat

Create an ad-hoc Foundry prompt agent tailored to a specific purpose using the new v2 Responses API with conversations.

## Prerequisites

The user must have completed the Foundry setup (the `setup-foundry` skill). They need:
- `FOUNDRY_PROJECT_ENDPOINT` -- the project endpoint URL
- `FOUNDRY_MODEL_DEPLOYMENT_NAME` -- the model deployment name

If these are not set, ask the user for the values.

## Steps

### 1. Define the Agent Purpose

Ask the user:
- What should the agent do? (e.g., "Write marketing copy", "Answer questions about our docs", "Translate text")
- Should it have any special instructions or persona?
- Does it need any tools? (code interpreter, file search, web search)

### 2. Create and Run the Agent

Use the following Python script. Customize the `agent_name`, `instructions`, and `tools` based on the user's requirements.

**Basic prompt agent (no tools):**

```bash
python3 << 'PYEOF'
import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import PromptAgentDefinition

endpoint = os.environ.get("FOUNDRY_PROJECT_ENDPOINT", "<ENDPOINT>")
model = os.environ.get("FOUNDRY_MODEL_DEPLOYMENT_NAME", "<MODEL>")

project_client = AIProjectClient(
    endpoint=endpoint,
    credential=DefaultAzureCredential(),
)

with project_client:
    openai_client = project_client.get_openai_client()

    # Create a purpose-built agent
    agent = project_client.agents.create_version(
        agent_name="AGENT_NAME_HERE",
        definition=PromptAgentDefinition(
            model=model,
            instructions="INSTRUCTIONS_HERE",
        ),
        description="DESCRIPTION_HERE",
    )
    print(f"Agent created: {agent.name} v{agent.version}")

    # Create a conversation
    conversation = openai_client.conversations.create()

    # Send the user's message
    response = openai_client.responses.create(
        conversation=conversation.id,
        input="USER_MESSAGE_HERE",
        extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
    )

    # Print the response
    print(response.output_text)

    # Clean up
    project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
    print("Agent cleaned up.")
PYEOF
```

**Agent with tools (code interpreter + web search):**

```bash
python3 << 'PYEOF'
import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import (
    PromptAgentDefinition,
    CodeInterpreterTool,
    CodeInterpreterToolAuto,
)

endpoint = os.environ.get("FOUNDRY_PROJECT_ENDPOINT", "<ENDPOINT>")
model = os.environ.get("FOUNDRY_MODEL_DEPLOYMENT_NAME", "<MODEL>")

project_client = AIProjectClient(
    endpoint=endpoint,
    credential=DefaultAzureCredential(),
)

with project_client:
    openai_client = project_client.get_openai_client()

    # Create agent with tools
    agent = project_client.agents.create_version(
        agent_name="AGENT_NAME_HERE",
        definition=PromptAgentDefinition(
            model=model,
            instructions="INSTRUCTIONS_HERE",
            tools=[
                CodeInterpreterTool(container=CodeInterpreterToolAuto()),
                {"type": "web_search_preview"},
            ],
        ),
        description="DESCRIPTION_HERE",
    )
    print(f"Agent created: {agent.name} v{agent.version}")

    # Create a conversation
    conversation = openai_client.conversations.create()

    # Send the user's message
    response = openai_client.responses.create(

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