This module provides an introduction to LangChain fundamentals, covering the setup of Azure AI Foundry models using Terraform, the creation of ReAct agents, and the deployment of language model agents. Learners will gain hands-on experience with real-world prompts and secure configurations.

This module explores how tools and function calling enhance AI agent capabilities, allowing them to perform task-oriented actions. Learners will gain an understanding of integrating custom tools, using ReAct agents, and implementing function-based workflows. It provides practical insights into connecting tools to language models for real-world applications.

This module explores the Model Context Protocol (MCP) and its role in enabling AI agents to interact with external systems like Microsoft Learn. Learners will gain an understanding of how to connect agents to an MCP server and use structured tool calls to access documentation. The module also covers key concepts such as tool discovery and server setup.

This module covers the setup, integration, and operational principles of self-hosted MCP servers for real-time web search. Learners will explore how to connect AI agents to external knowledge sources, implement error handling, and develop custom tools for seamless web interaction.

This module covers the use of dynamic sessions for executing Python and shell commands in safe, isolated environments. Learners will explore how to configure and use these sessions within Azure Container Apps to enhance security and control over code execution.

This module explores how AI agents can retain and manage conversational context using Azure Cosmos DB. Learners will understand the role of short-term memory, implement memory storage techniques, and analyze strategies for handling long conversations effectively.

This module explores the role of human oversight in AI agent workflows, focusing on how to implement and use human-in-the-loop middleware to ensure safety, control, and reliability in automated systems. Learners will gain practical knowledge on reviewing, editing, and intervening in AI-generated actions through real-world examples and scenarios.

This module explores how to use before and after model hooks to customize and control AI agent behavior during model execution. Learners will gain practical skills in implementing middleware hooks for inspection, modification, and security in agent workflows. The content provides a foundation for managing agent interactions effectively.

This module explores the design and implementation of multi-agent systems, focusing on how specialized agents collaborate and delegate tasks through orchestration patterns. Learners will gain an understanding of context management, performance optimization, and the use of MCP-enabled tools for scalable AI solutions.

This module introduces the principles of logging and observability in AI agents, focusing on how LangSmith tracing enables effective monitoring, debugging, and optimization of agent-driven workflows. Learners will gain insights into identifying errors, analyzing performance, and improving system efficiency.

This module provides a comprehensive summary of the agent-building process and outlines practical steps for applying the course concepts in real-world scenarios. Learners will gain clarity on the key takeaways and how to continue their learning journey.