Azure Architecture Guide

AI and Agentic Architecture

What This Chapter Is About

Help readers design Azure AI and agentic architecture by connecting model access, orchestration, retrieval, tools, identity, authorization, safety, evaluation, monitoring, cost, and operations.

This chapter introduces AI architecture as an application and platform design problem, not only a model selection problem.

Keep one durable idea in view:

AI and agentic architecture is the design of controlled reasoning, trusted data access, tool use, safety, evaluation, and operations around model-powered workflows.

Sections

  1. AI and Agentic Architecture Mental Model

  2. Model and Agent Platform with Azure AI Foundry and Azure OpenAI

  3. Retrieval, Data Grounding, and Tool Integration

  4. Identity, Authorization, Safety, and Evaluation

  5. Monitoring, Cost, Operations, and Change Management

  6. AI and Agentic Architecture Scenario

How the Chapter Develops

The chapter develops in this order:

  1. Start with AI architecture as a controlled workflow around model calls, data, tools, and users.

  2. Explain where Azure AI Foundry and Azure OpenAI fit at a high level.

  3. Explain retrieval, grounding, tool calling, enterprise APIs, and Model Context Protocol concepts.

  4. Explain identity, authorization, safety controls, evaluation, and risk ownership.

  5. Explain monitoring, cost, operations, model change, prompt change, and incident response.

  6. Apply the model to an enterprise assistant or agentic workflow with real constraints.

Together, these sections show that AI architecture is about controlled capability, not merely adding a chatbot to an application.

What You Will Be Able to Do

By the end of this chapter, the reader should be able to:

  • Explain the main responsibilities in an Azure AI architecture.

  • Describe the role of Azure AI Foundry and Azure OpenAI at a high level.

  • Explain retrieval, grounding, tool use, and enterprise API access.

  • Connect identity, authorization, safety, and evaluation to model-powered workflows.

  • Discuss monitoring, cost, operations, and change management.

  • Sketch an AI or agentic architecture with controlled data and tool access.

Key Ideas

  • Model access is only one part of AI architecture.

  • Retrieval and grounding help connect model output to enterprise data, but access control still matters.

  • Tool calling and agent workflows need authorization, auditability, safety limits, and failure handling.

  • Evaluation is part of the delivery lifecycle, not a one-time demo step.

  • Monitoring and cost controls matter because AI behavior and usage can change quickly.

  • AI systems should be designed with ownership for prompts, data, tools, model configuration, and incidents.

Related Provider-Neutral Reading

  • AI and Agentic Architecture develops the reusable retrieval, tool-use, safety, evaluation, operations, and change-management responsibilities behind this Azure implementation.