aymenfurter/foundry-agent-chat

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):

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):

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(
        conversation=conversation.id,
        input="USER_MESSAGE_HERE",
        extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
    )

    # Print the response
    for item in response.output:
        if item.type == "message":
            for content in item.content:
                if content.type == "output_text":
                    print(content.text)

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

3. Multi-turn Conversation

If the user wants to continue the conversation, reuse the same conversation ID without creating a new one:

python3 << 'PYEOF'
import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient

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

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

with project_client:
    openai_client = project_client.get_openai_client()

    # Reuse existing conversation and agent
    conversation_id = "CONVERSATION_ID_HERE"
    agent_name = "AGENT_NAME_HERE"

    response = openai_client.responses.create(
        conversation=conversation_id,
        input="FOLLOW_UP_MESSAGE_HERE",
        extra_body={"agent": {"name": agent_name, "type": "agent_reference"}},
    )

    print(response.output_text)
PYEOF

4. Present Results

• Show the agent's response to the user
• If the agent used tools (code interpreter, web search), summarize what it did
• For multi-turn conversations, keep track of the conversation ID and agent name

5. Clean Up

When the user is done, delete the agent version:

python3 << 'PYEOF'
import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient

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

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

with project_client:
    project_client.agents.delete_version(agent_name="AGENT_NAME", agent_version="VERSION")
    print("Agent cleaned up.")
PYEOF

Agent Types Summary

| Use Case | Tools | Example | |----------|-------|---------| | Q&A / Chat | None | Customer support bot, writing assistant | | Data Analysis | Code Interpreter | CSV analysis, chart generation, math solver | | Research | Web Search | Market research, fact checking | | Full-featured | Code Interpreter + Web Search | Research analyst with computation |

Notes

• Agents are versioned: each create_version call increments the version number
• Conversations persist state across calls, so the agent remembers context
• Use store=False in the response call to opt out of statefulness when not needed
• The Responses API is synchronous -- no polling required (unlike the old Runs API)
• Agent definitions and conversation state use single-tenant storage for security