Building AI Copilots: From Concept to Production

I wrote “Building AI Copilots: From Concept to Production” to share practical, production-minded guidance on this topic.

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.\n\n## Takeaways\n\nAdd a concise, personal takeaway and recommended next steps here.\n