Create and deploy custom AI agents using Microsoft Foundry Agent Service and Agent Framework.
Covers understanding agent roles and use cases, configuring resources for agent development, and creating agents with Microsoft Foundry Agent Service. Includes implementing complex agents with Microsoft Agent Framework, building multi-agent solutions with orchestration, handling multiple users, and enabling autonomous capabilities. Also covers testing, optimizing, and deploying agents effectively.
5 minutes
5 Questions
Implementing an agentic solution in Azure involves creating AI systems that can autonomously perform tasks, make decisions, and interact with various tools and data sources to achieve specific goals. An agentic solution goes beyond simple question-answering by enabling the AI to plan, execute multi-step workflows, and adapt based on outcomes.
Key components of an agentic solution include:
**1. Azure OpenAI Service**: This serves as the cognitive backbone, providing large language models (LLMs) that can reason, plan, and generate responses. Models like GPT-4 are capable of understanding complex instructions and breaking down tasks into actionable steps.
**2. Function Calling**: This feature allows the AI agent to invoke external functions or APIs when needed. The model determines which function to call based on user intent, enabling integration with databases, external services, or custom business logic.
**3. Azure AI Agent Service**: This managed service helps build, deploy, and scale AI agents. It provides built-in capabilities for conversation management, memory persistence, and tool orchestration.
**4. Tool Integration**: Agents connect to various tools such as Azure Cognitive Search for knowledge retrieval, Azure Functions for executing code, or Microsoft Graph for accessing organizational data.
**5. Memory and Context Management**: Effective agents maintain conversation history and state across interactions, allowing for coherent multi-turn dialogues and task continuation.
**6. Orchestration Frameworks**: Tools like Semantic Kernel or LangChain help coordinate the agent's reasoning process, manage prompts, and handle the flow between different capabilities.
**Implementation Steps**:
- Define the agent's purpose and available tools
- Configure the LLM with appropriate system prompts
- Implement function definitions for external capabilities
- Set up memory storage for context persistence
- Create feedback loops for the agent to evaluate its actions
- Deploy using Azure Container Apps or Azure Kubernetes Service
Agentic solutions enable sophisticated automation scenarios like customer service, research assistants, and workflow automation.Implementing an agentic solution in Azure involves creating AI systems that can autonomously perform tasks, make decisions, and interact with various tools and data sources to achieve specific goals. An agentic solution goes beyond simple question-answering by enabling the AI to plan, execute multi…