Agentic AI with Azure & Copilot

Agentic AI with Azure & Copilot

Build, Deploy & Scale Autonomous AI Agents in 2 days

Agentic AI is rapidly transitioning from academic experiment to enterprise imperative, and Microsoft’s Azure + Copilot ecosystem is becoming a dominant platform for building intelligent agents. Rather than anchoring purely in open-source tooling, this course emphasizes how to harness Azure AI, Copilot Studio, and the broader Microsoft stack to build agents that integrate seamlessly into cloud services, enterprise data, and workflows.

While many courses focus on abstract theory or toy demos, this one is different: the instructor brings over 30 years of industry experience, and the curriculum is grounded in real use cases drawn from enterprise scenarios. You’ll learn not just how agents work, but how they operate securely, at scale, and in production, within a Microsoft/Azure architectural context.

Learning Outcomes

By the end of this course, participants will be able to:

• Explain the architecture and design patterns of agentic AI within the Azure + Copilot ecosystem
• Design and build intelligent agents using Copilot Studio, Azure AI Foundry, and Azure OpenAI
• Connect agents to enterprise data sources (Azure Search, Dataverse, Cognitive Services) via Model Context Protocol (MCP)
• Implement retrieval-augmented generation (RAG) pipelines using Azure AI Search + vector stores
• Author, debug, and manage agents inside Copilot Studio, including Generative Answers flows
• Deploy agents to production using Azure resource management, security, scaling, and monitoring
• Implement runtime safeguards, content filtering, and governance in agentic systems
• Evaluate agent performance and iterate with continuous improvement

Prerequisites

• Strong Python programming skills
• Working understanding of cloud services (especially Azure)
• Familiarity with REST APIs, JSON, data modeling, and vector databases
• Some prior experience with LLMs, embeddings, prompt engineering, or retrieval systems is helpful (though not strictly required)
• Basic knowledge of DevOps/containerization (Docker, Kubernetes)

Detailed Course Outline

Agentic AI in the Microsoft / Azure Era

• What is a copilot vs. an agent? (Microsoft framing)
• The role of Azure OpenAI in the Copilot ecosystem
• Azure AI Foundry, Copilot Studio, and agents as a managed runtime
• Design patterns & “Agent Factory” in Azure (common use-cases, modularity, safety)
• Governance, security, and compliance: content filtering, identity integration, and audit trails

Copilot Studio & Agent Authoring

• Overview of Copilot Studio: interface, capabilities, and architecture
• Generative Answers nodes: connecting to Azure OpenAI + data sources
• Low / no-code vs code-enabled agent design in Copilot Studio
• Debugging agents in Copilot (e.g. agent debugging in Microsoft 365 Copilot)
• Best practices — versioning, iteration, conversation flows, fallback logic

Bridging Agents to Enterprise Data via MCP & Dataverse

• Recap: what is MCP (Model Context Protocol) and why it matters
• Dataverse MCP server: using it to integrate agentic systems with enterprise data models
• Using Generative Answers + Azure AI Search to query and surface structured/unstructured data
• Data connectors, plugins, custom adapters for internal APIs and systems
• Ensuring data access control, latency considerations, and caching strategies

Retrieval-Augmented Generation (RAG) in Azure

• Key concepts: embeddings, vector search, metadata filtering, context windows
• Building a semantic search index with Azure AI Search or vector store
• Agent-driven retrieval vs static context injection
• Strategies for prompt templating, chunking, and relevance feedback
• Agent orchestration between retrieval and generation

Multi-Agent & Orchestration Strategies

• Patterns for multi-agent systems: task decomposition, delegation, agent collaboration
• Copilot / Azure-style inter-agent coordination: sharing state through MCP, events, or message buses
• Error recovery, fallback agents, chaining agents
• Case study: agent network for complex business workflows

Safe Execution & Runtime Safeguards

• When agents generate code or actions — sandboxing, validation, whitelists
• Approaches to limit harmful or unexpected behavior
• Logging, auditing, alerts, and human-in-the-loop oversight
• Failover plans, circuit breakers, rate limiting, and safety wrappers

Deployment, Scaling & Management on Azure

• Architecture decisions: serverless vs container vs VM
• Managing agent runtime via Azure resource governance (VMs, AKS, App Services, Functions)
• Identity, networking, and access control integration (Azure AD, managed identities)
• Monitoring, observability, logging, and tracing agents
• Auto-scaling strategies, cost management

Agent Retraining, Feedback Loops & Continuous Improvement

• Collecting logs, user feedback, corrections, and fine-tuning data
• Strategies for model updates: prompt tuning, lightweight fine-tuning (LoRA / PEFT)
• A/B testing, performance metrics, iteration cycles
• Governance around model drift, quality checks, rollback