Building AI Copilots: From Concept to Production

Building a production AI copilot requires careful design of conversation management, tool integration, and user experience. Here’s how to build one.

Copilot Architecture

from azure.ai.foundry import AIFoundryClient
from azure.ai.foundry.agents import Agent, Tool

class DataCopilot:
    def __init__(self, ai_client: AIFoundryClient):
        self.agent = Agent(
            model="gpt-4o",
            instructions="""You are a data analytics copilot.
            Help users query data, create visualizations, and understand insights.
            Always explain your reasoning and ask clarifying questions when needed.""",
            tools=[
                Tool.from_function(self.query_database),
                Tool.from_function(self.create_chart),
                Tool.from_function(self.explain_data)
            ]
        )
        self.conversation_history = []

    async def chat(self, user_message: str) -> str:
        self.conversation_history.append({"role": "user", "content": user_message})

        response = await self.agent.run(self.conversation_history)

        self.conversation_history.append({"role": "assistant", "content": response})
        return response

    async def query_database(self, query: str) -> str:
        """Execute SQL query and return results."""
        # Implementation
        pass

    async def create_chart(self, data: dict, chart_type: str) -> str:
        """Create visualization from data."""
        # Implementation
        pass

Build copilots that understand context, use tools effectively, and provide clear explanations.