Building AI Agents with Microsoft Azure

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Extend AI agents beyond text with tool integrations, enabling reliable real-time actions and data access.

Learn to empower AI agents in Python by building and registering custom tools with Semantic Kernel, allowing access to real-time external data and enhanced functionality.

Discover structured outputs in AI: transform responses into actionable JSON for integration. Utilize schemas, parsers, and function calls to enhance reliability and automation in workflows.

Learn to compel LLMs to output structured, validated JSON by combining Pydantic models, structured prompts, and Semantic Kernel's automatic tool invocation.

Explore agent state management with state machines. Learn how agents track user input, instructions, and tool use for complex workflows, ensuring adaptability and reliability.

Learn to build robust AI agents using a Python finite state machine to define phases, manage agent state, guide LLMs, and ensure predictable, auditable multi-step workflows.

Explore short-term memory in AI agents, enhancing coherence via state, ephemeral, and ephemeral memory strategies for efficient context retention in active sessions.

Learn to add short-term memory to an agent using Python, enabling it to track conversation history, manage memory limits, and handle follow-up questions in dialogues.

Explore using external APIs for real-time data, dynamic actions, and authenticating agents. Discover MCP, a protocol standardizing AI’s tool interoperability and safety.

Learn to enhance LLMs with real-time data using Semantic Kernel, integrating external APIs as plugins, automatic tool selection, and maintaining conversation context for expert agents.

Equip agents to search web for real-time, unstructured info. Ground responses in evidence using APIs, handle noise, and avoid hallucination for credible answers.

Build AI agents with real-time Bing web search using Semantic Kernel, Azure AI Agents, prompt engineering for JSON, and auto tool selection in Python.

Equip agents to access and modify structured data by using SQL for interaction and vector databases for semantic tasks, ensuring seamless integration with private systems.

Learn to build AI agents with Retrieval-Augmented Generation using Azure Cosmos DB, including data ingestion, text-based retrieval, and robust agent integration.

Discover Agentic RAG: Enhance RAG by enabling reflection, query reformulation, and intelligent adaption for nuanced answers. Master retrieval, reasoning, and retry loops.

Build agentic RAG with Cosmos DB: create a self-correcting AI agent that refines queries, uses LLMs to assess document quality, and leverages hybrid search with RRF for better answers.

Explore long-term agent memory: understand semantic, episodic, and procedural memories. Learn storage strategies and best practices for personalized, coherent interactions.

Learn to build AI agents in Python with long-term memory, enabling them to recall past interactions for context-aware, personalized conversations using memory storage and retrieval.

Agent Evaluation guides assessing an agent’s task completion, quality, tool use, and system metrics using response, step, or trajectory strategies to ensure reliable and efficient operations.

Learn to evaluate AI agents by combining rule-based structural checks with LLM-as-judge semantic analysis for comprehensive quality assessment.