Welcome to enterprise AI in 2026: the era of autonomous agents is here. Organizations today demand more than simple chatbots – they need intelligent systems that reason, retrieve knowledge, enforce policies, orchestrate tools, and execute real business workflows at scale.
This hands-on, production-grade course teaches you how to build enterprise AI agents on Azure using Microsoft Foundry (the unified AI app & agent factory) and the Microsoft Agent Framework (the official 2026 successor to Semantic Kernel and AutoGen). This is the exact stack top corporations use for governed, scalable, and production-ready agentic AI.
The Microsoft Foundry Advantage
Microsoft Foundry is the “Agent Factory” for the modern enterprise. It unifies high-performance models (OpenAI, Anthropic, Llama, DeepSeek and more), centralized knowledge via Foundry IQ, and built-in governance in one secure environment. Paired with the open-source Microsoft Agent Framework, you will go from local Python prototypes to compliant production deployments on Azure.
Course Curriculum at a Glance
Phase 1: Foundations & Setup
- Agentic AI vs. Traditional Automation: When to use which.
- Microsoft Foundry Architecture: Understanding Hubs, Projects, and the Agent Service.
- The Microsoft Agent Framework: Deep dive into the unified SDK for Python.
- Environment Setup: Azure CLI, VS Code AI Toolkit, and Python SDK configuration.
Phase 2: Building the Customer Support Agent
- Foundry Playground: Rapid prototyping and agent configuration.
- Tool Calling: Connecting your agents to real-time APIs and business systems.
- Foundry IQ & Knowledge Retrieval: Implementing enterprise-grade RAG.
- Responsible AI: Setting up Guardrails, Prompt Shields, and Grounding policies.
- Front-End Integration: Building a Streamlit UI to interact with your agents.
Phase 3: Multi-Agent Workflows & Orchestration
- Agents vs. Workflows: The decision framework for enterprise architects.
- Foundry Workflow Designer: Building deterministic, multi-step AI pipelines.
- Agent Communication Patterns: Orchestrating “teams” of agents to solve complex tasks.
- Observability: Using Traces and Application Insights to debug and monitor agent reasoning.
Phase 4: The Capstone Project
- Design & Architecture: Modeling a Support Triage system for a large organization.
- Intelligent Routing: Using LLMs to categorize and route requests with high precision.
- Autonomous Execution: Validating end-to-end production-grade agent workflows.
